Quality control system for wood-based products production

Abstract

The present invention relates to a system for a production system configured to manufacture at least one product comprising wood from at least one material comprising wood, the system comprising a data acquisition module configured to acquire at least one data sample from the at least one product and / or the at least one material, a decider module configured to process of the at least one data sample related to the at least one product and / or the at least one material resulting in at least one decider module result, and a controller module configured to provide operational commands to the production system according to the at least one decider module result. Furthermore, the present invention relates to a method for a production system configured to manufacture at least one product comprising wood from at least one material comprising wood, the method comprising an acquiring step, wherein the acquiring step comprises acquiring at least one data sample from the at least one product and / or the at least one material, a deciding step wherein the deciding step comprises processing the at least one data sample related to the at least one product and / or the at least one material resulting in at least one deciding step result, and a controlling step, wherein the controlling step comprises providing operational commands to the production system according to the at least one deciding step result.

Description

[0001] Quality Control System for Wood-Based Products Production

[0002] The present invention relates to quality control of wood materials, and the wooden products these materials are manufactured into, before, after and during the manufacturing process.

[0003] In order to produce novel environmentally friendly wood-based products, in packaging and other applications it is a necessity to perform quality control, otherwise the scrap volume is very large and due to wood property of fibrous splintering, plus powder dusting, jams rapidly fine mechanics of production machinery. In addition, Al based quality control enables the production machinery to achieve very high speed that is critical to achieve mass production and favorable unit economics. In short, it's not feasible for humans to carry out quality control for the large production volume and speed.

[0004] Machine vision has had very good progress in development in recent years thanks to following fields:

[0005] Image acquisition - cameras resolutions, framerates and other properties of sensors have increased very strongly. An 8Mpix industrial camera is a very typical generic sensor, while a decade ago l-2Mpix was a decent value. Framerates 100-200Hz are very widely available, previously above 120Hz was a high-speed camera specialized solution.

[0006] Defect Detection algorithms - various machine learning technologies have emerged and are developing very rapidly, including convolutional neural networks (CNN) and deep learning algorithms.

[0007] Data processing speeds and access to algorithms - generic GPUs and CPUs have nowadays dedicated computer vision toolsets like OpenCV library, TensorFlow, Yolo models etc. to enable the use of their computing power in the industrial application. Also, GPU designs have dedicated highly capable structures in context of neural networks and artificial intelligence.

[0008] Developments such as these have been put to use in the following inventions:

[0009] CN111325713B pertains to a method, system, and storage medium for detecting wood defects based on neural networks. The invention utilizes a neural network to process image data of wood, segmenting the images to identify defects accurately. The method includes steps for collecting wood image data, preprocessing the data, and using a neural network model trained to recognize various types of defects. The system improves the accuracy and efficiency of detecting defects in wood, making it valuable for quality control in the lumber and woodworking industries. The storage medium can store instructions for executing the described method, ensuring the application of the technology in practical scenarios. CN107392896B describes a method and system for detecting defects in wood using deep learning techniques. The invention includes collecting image data of wood surfaces, preprocessing these images, and then analyzing the images using a trained deep learning model. This model is capable of identifying various types of wood defects, such as knots, cracks, and rot, with high accuracy. The system enhances the efficiency and accuracy of wood defect detection, making it useful for quality control in the lumber industry. The invention also outlines the structure of the system, which includes components for image acquisition, data processing, and defect analysis.

[0010] However, these inventions are still limited as the images require a high enough resolution for big enough wood materials. Dividing that image then feeding it into the algorithms currently in use for quality control are also limited by the algorithm's memory capacity as well. Furthermore, these algorithms are not integrated into a production system allowing defect detection during the manufacturing process as well as before or after manufacture.

[0011] The present invention alleviates at least some of these short-comings.

[0012] In a first aspect, the invention relates to a system for a production system configured to manufacture at least one product from at least one material, the system comprising: a data acquisition module configured to acquire at least one data sample from the at least one product and / or the at least one material, a decider module configured to process of the at least one data sample related to the at least one product and / or the at least one material resulting in at least one decider module result, and a controller module configured to provide operational commands to the production system according to the at least one decider module result.

[0013] In a further embodiment, the system comprising the production system may be configured to manufacture at least one product from at least one material. The system may also be configured to perform quality control on the at least one material and / or the at least one product. The system may furthermore be configured to perform quality control on the at least one material while being manufactured into the at least one product.

[0014] Moreover, the production system may be configured to discard and / or retain the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controller module. The production system may additionally or alternatively be configured to detect if the at least one product and / or the at least one material is in the correct position. The production system may also be configured to output at least one data indicative of the positioning of the least one product and / or at least one material.

[0015] Additionally or alternatively, the data acquisition module may be configured to acquire at least one data sample. The at least one data sample may also be acquired in real-time. The data acquisition module may furthermore be configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material. The data may be acquired in real-time as well as in a sequential way. One data sample from the data acquired has thus a connection to another data sample that may or may not be related to a time sequence.

[0016] In one embodiment, the data acquisition module may comprise at least one data acquisition device such as but not limited to cameras, sensors. Furthermore the at least one data acquisition device may be positioned relative to the production system in a manner conducive to acquiring the at least one data sample. Additionally or alternatively, the at least one data acquisition device may be positioned essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material in a manner conducive to acquiring the at least one data sample.

[0017] In a further embodiment, the at least one data acquisition device may be configured to acquire real-time data. The at least one data acquisition device may also be configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material. Furthermore, the at least one data acquisition device may be configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material. That is, the data acquisition device may be moved to allow its area of coverage to encompass the at least one part of the at least one product and / or at least one part of the at least one material in a sequential way. It is possible for the at least one product and or at least one material to also be moved in such a way that the at least one data acquisition device's area of coverage encompasses the at least one product and / or at least one material.

[0018] Moreover, the data acquisition module may be configured to adapt the lighting conditions of the at least one product and / or at least one material accordingly. The data acquisition module may also be configured to shield the at least one product and / or at least one material from external lighting. Furthermore, the data acquisition module may be configured to backlight the at least one product and / or at least one material with respect to the at least one data acquisition device. The backlighting of the at least one product and / or at least one material may be achieved with a colored light wherein the colored light is chosen according to the at least one product and / or at least one material. Additionally or alternatively, the colored light's spectrum range may comprise the infrared (IR) spectrum in addition to the visible light spectrum.

[0019] In one embodiment, the data acquisition module may be configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module. The data acquisition module may also be configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in a sequential way and / or in real-time. The data acquisition module may furthermore be configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module using a FIFO basis.

[0020] In another embodiment, wherein the data acquisition module may be configured to acquire or provide a plurality of data samples, the data acquisition module may also be configured to ensure overlap between the data samples. The at least one data acquisition device may also be configured to acquire the plurality of data samples. The at least one data acquisition device may be configured to move its coverage area to acquire the plurality of data samples. The data acquisition module may furthermore be configured to receive at least one acquiring status signal wherein the data acquisition module acquires / stops acquiring the at least one data sample according to that at least one acquiring status signal.

[0021] In one embodiment, the decider module may comprise a preprocessing module. The preprocessing module may be configured to perform at least one type conversion on the at least one data sample. The preprocessing module may also be configured to perform at least one transformation on the at least one data sample such as but not limited to at least one hue adjustment, brightness adjustment, saturation adjustment, rotation, translation, scale manipulation, horizontal flip, vertical flip. Another example of transformation would be applying at least one mosaic function defined such as aligning and stitching together multiple images taken from slightly different perspectives or positions to create a seamless and higher-resolution composite image. A further example of transformation would be applying at least one mix-up function and / or mix-up augmentation defined as a data augmentation technique that generates a weighted combination of random image pairs.

[0022] In another embodiment, the decider module may comprise at least one artificial intelligence (Al) module. The at least one Al module may also be configured to execute at least one feature extraction function. The at least one Al module may furthermore be configured to execute at least one convolution such as but not restricted to at least one 2D-convolution.

[0023] In a further embodiment the at least one Al module may be configured to execute at least one sequence modelling algorithm. In this case, sequence modelling algorithm would be encompassing algorithms that are specifically designed to understand and model sequences of data, which often involve retaining information over short and / or long periods of time as is the case with RNN, LSTM and transformer architectures. Moreover, the at least one sequence modelling algorithm may be configured to execute at least one Al algorithm that makes use of any kind of working and / or short-term memory. Short-term memory may relate exclusively to information storage while working memory may relate to information storage and manipulation. Furthermore, the at least one Al module may be configured to execute at least one transformer model implementation. Transformer models rely on self-attention mechanisms to process input sequences in parallel, allowing it to capture long-range dependencies efficiently. The architecture consists of stacks of encoders and decoders, with each layer using multi-head self-attention and feed-forward neural networks, complemented by residual connections and layer normalization. This architecture allows for parallelization of data compared to RNN architecture which requires data to be inputted sequentially. Additionally or alternatively, the transformer architecture memory is not restricted by the architecture itself like in RNN and LSTM algorithms, but is only limited by the amount of memory supported by the hardware implemented in the system. This allows for the at least one data acquisition module and / or at least one data acquisition device to make use of a lesser resolution than what is commonly used without impacting the amount of time needed for the at least one Al module to output a result, and thus showcases a preferred advantage of the present invention.

[0024] In another embodiment, the at least one Al module may be configured to execute image classification, wherein image classification consists of just classifying an image in at least one category, image recognition, wherein image recognition consists of classifying an image in at least one category while outputting the location of the at least one main area for the classification decision, and / or at least one anchor-free detection implementation, wherein anchor-free detection is an object detection approach that eliminates the use of predefined anchor boxes, which are commonly used in traditional anchor-based detectors to predict object locations and sizes. Instead, anchor-free detectors directly predict the key points or the center points of objects along with their dimensions and class probabilities. Anchor-free detection simplifies the detection pipeline, reduces computational overhead, and often improves detection accuracy by avoiding the complexities and limitations associated with anchor box design and matching.

[0025] Furthermore, the at least one Al module may be configured to execute at least one pattern recognition algorithm regardless if that at least one algorithm qualifies as artificial intelligence and / or machine learning. These algorithms would allow reduce the load on the processing hardware. Examples of pattern recognition algorithms would be discriminant analysis, logistic regression, decision trees, Kernel estimation, KNNs, perceptrons, MLPs, NNs, clustering algorithms, PCA, MPCA, RNNs. Additionally or alternatively, the at least one Al module may be configured to execute at least one probabilistic pattern recognition algorithm such as algorithms making use of a frequentist or Bayesian approach. These algorithms would reduce the load on the processing hardware and thus showcases a preferred advantage of the present invention.

[0026] In a further embodiment, the system may be configured for training the at least one Al module. The system may also be configured for testing the at least one Al module. In one embodiment, the decider module may be configured to convert the at least one decider module result for accessibility according to the controller module. The controller module may also comprise at least one probability threshold module. The at least one probability threshold module may comprise at least one probability threshold wherein the at least one probability threshold may be adjustable. Each parameter may be assessed with the probability of being true. The at least one probability threshold may serve to tune the process quality baseline. The sensitivity of the system may thus be optimizable to find a balance between a decrease of production downtime, an increase in speed, and / or for testing purposes. Furthermore, the controller module may be configured to output the at least one operation command to the production system based on the at least one Al module result and the at least one probability threshold module.

[0027] In another embodiment the controller module may be configured to transmit at least one start signal to the data acquisition module.

[0028] In a further embodiment, the system may be configured to output at least one system heartbeat state such as "live" or "Not responding". Additionally or alternatively, the controller module may be configured to transmit the at least one system heartbeat state to the production system. Should the system heartbeat state be able to be translated to "Not responding", the production system may output an error state. The system may also be configured to output at least one system status state such as "waiting for next product", "processing", "error", etc.

[0029] The at least one system status state and / or the at least one system heartbeat state may depend on all of the modules comprising the system. The system may be configured to output at least one system decision such as "OK" or "Not OK". The system may be configured furthermore to output at least one defect ID. The at least one system decision and / or the at least one defect ID would relate to the at least one operation command to the production system based on the at least one Al module result and the at least one probability threshold module. Specifically, the at least one system decision would relate to the operation commands provided by the controller module wherein the production system may be configured to discard and / or retain the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controller module.

[0030] Additionally or alternatively, the production system may be configured to transmit at least one system status state to the controller module. The controller module may be configured to transmit at least one system decision to the production system.

[0031] Moreover, the controller module may be configured to transmit at least one system decision and / or at least one defect ID to the production system. The controller module may also be configured to transmit an acquiring status signal to the data acquisition module. The data acquisition module may be configured to receive at least one acquiring status signal wherein the data acquisition module acquires / stops acquiring the at least one data sample according to that at least one acquiring status signal.

[0032] Additionally or alternatively, communication between the modules of the system may be achieved by different communication means and protocols. For example, modules may communicate over TCP / IP (approximately OSI layer 4) protocol for high-bandwidth, low latency communication and easy interoperability. The controller may store the at least one data sample on a local or network accessible drive. The at least one data sample and / or any metadata that is related to the system may be read directly from that location, over local network, by the decider module. Any of the at least one results may be communicated to the controller over MQTT protocol (OSI layer 7), with the payload dependent on the controller module's relay capabilities. For flexibility, the decider may contain an adapter for translating purposes explained previously. The modules may make use of other communication protocols such as but not restricted to Ethercat, Ethernet / IP, CANbus, CanOpen, Profinet, Fieldbus, DeviceNet, Modbus, Profibus, RS-485, RS-232, CC-Link, Digital signal (TTL), HART (Highway Addressable Remote Transducer), AS-Interface (Actuator Sensor Interface), BACnet, Sercos III, DNP3 (Distributed Network Protocol), Interbus, LonTalk. The modules comprising the system may be configured to communicate wirelessly.

[0033] Furthermore, the system may comprise a user interface device wherein the user interface device may be configured to display information relating to the system. Examples of such user interfaces may be screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of the system's parameters to the user), and / or speakers configured to communicate audio data (e.g. playing audio data to the user).

[0034] In one embodiment, the at least one decider module result may be indicative of the quality of at least one material comprising wood / at least one product comprising wood. The at least one decider module result may also indicative of the quality of at least one sheet comprising wood, more preferably at least one veneer sheet, at least one manufactured spring, at least one manufactured mat and / or at least one currently being manufactured mat.

[0035] In a further embodiment, the decider module may be configured to identify at least one along the grain crack, at least one perpendicular to grain direction crack, at least one crack at any angle, at least one branch knot, at least one splinter, at least one ragged edge, at least one undesirable shape such as a non-rectangular sheet, at least one undesirable edge such as a non-straight edge and / or a ragged edge, at least one out of range dimension, at least one loss of material, at least one excessively thin area and / or edge, and / or at least one discoloration, in the at least one sheet comprising wood. Additionally or alternatively, the decider module may be configured to identify if more than one sheet was inputted into the system.

[0036] In another embodiment, the decider module may be configured to identify at least one burnt patch, at least one deformation, at least one branch knot defect, at least one splinter protruding out, at least one crack, along the grain crack, at least one perpendicular to grain direction crack, at least one crack at any angle, at least one strongly ragged edge, at least one discontinuity, at least one dent, and / or at least one discoloration in the at least one manufactured spring.

[0037] Additionally or alternatively, the decider module may be configured to identify at least one additional spring produced onto the at least one manufactured spring.

[0038] Moreover, the decider module may comprise at least one spring geometry tolerance value, wherein the decider module may be configured to identify at least one defect in the at least one manufactured spring related to the at least one wood springs geometry tolerance value. The decider module may furthermore comprise at least one coloration data, wherein the decider module may be configured to identify at least one defect in the at least one manufactured spring related to the at least one coloration data.

[0039] In another embodiment, the decider module may be configured to identify if at least one spring, and / or at least one coil of the at least one spring is snapped to at least one working plate with at least one fixing pin in the at least one mat. The at least one working plate would relate to a support plate comprising at least one pin for mat production in the production system. The decider module may also be configured to identify if the step of at least one spring relative to at least one pin is uniform in the at least one mat. Additionally or alternatively, the decider module may be configured to identify if at least one spring is deformed and / or crushed in the at least one mat. Furthermore, the decider module may be configured to identify if at least one protruding feature of at least one spring can extend out of the at least one spring's general diameter in the at least one mat.

[0040] The at least one material comprising wood and / or the at least one product comprising wood may substantially be made from wood and / or at least a partially woody plant such as bamboo, willow, rattan, reed, cane, dried palm leaves...

[0041] The at least one material comprising wood and / or the at least one product comprising wood may comprise a mass fraction of woody substance amounting to at least 35%, preferably at least 50%, more preferably at least 70% and most preferably at least 90%. The at least one manufactured spring, for example, may comprise a mass fraction of woody substance amounting to at least 50%, preferably at least 70% and most preferably at least 90%.

[0042] Additionally and alternatively, the at least one manufactured spring may comprise an outer diameter of 5mm to 20 mm, preferably 8 mm to 15mm, and more preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position. The at least one manufactured spring also comprises a length of 10-5000 mm, preferably 150 mm to 1500 mm, more preferably 200 mm to 1000 mm, and most preferably 200 mm to 900 mm in a substantially unbiased state. Moreover, the at least one manufactured spring's strip further comprises a width of 1 mm to 20 mm, preferably 2 mm to 7 mm, more preferably 3 mm to 7 mm, and most preferably 4 mm to 6 mm in a substantially unbiased state, as well as may comprise an outer diameter of 5 mm to 60 mm, preferably 5 mm to 20 mm, more preferably 8 mm to 15 mm, and most preferably 10 mm to 12 mm in a substantially unbiased state.

[0043] Furthermore, the at least one sheet comprising wood may comprise dimensions of preferably 2500 mm x 1200 mm.

[0044] In a second aspect the invention relates to a method for a production system configured to manufacture at least one product from at least one material, the method comprising: an acquiring step, wherein the acquiring step comprises acquiring at least one data sample from the at least one product and / or the at least one material, a deciding step wherein the deciding step comprises processing the at least one data sample related to the at least one product and / or the at least one material resulting in at least one deciding step result, and a controlling step, wherein the controlling step comprises providing operational commands to the production system according to the at least one deciding step result.

[0045] In a further embodiment, the method may comprise operating the production system, wherein the production system may be configured to manufacture at least one product from at least one material. The method may furthermore comprise performing quality control on the at least one material and / or the at least one product.

[0046] Moreover, the production system may be configured to discard and / or retain the at least one material and / or the at least one product being manufactured according to the operational commands generated by the controlling step. The production system may additionally or alternatively be configured to detect if the at least one product and / or the at least one material is in the correct position. The production system may also be configured to output at least one data indicative of the positioning of the least one product and / or at least one material.

[0047] Additionally or alternatively, the acquiring step may comprise acquiring at least one data sample. The at least one data sample may also be acquired in real-time. The acquiring step may furthermore comprise acquiring data from at least one part of the at least one product and / or at least one part of the at least one material. The data may be acquired in realtime as well as in a sequential way. One data sample from the data acquired has thus a connection to another data sample that may or may not be related to a time sequence. In one embodiment, the acquiring step may comprise acquiring the at least one data sample provided from at least one data acquisition device such as but not limited to cameras, sensors. Furthermore the at least one data acquisition device may be positioned relative to the production system in a manner conducive to acquiring the at least one data sample. Additionally or alternatively, the at least one data acquisition device may be positioned essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material in a manner conducive to acquiring the at least one data sample.

[0048] In a further embodiment, the at least one data acquisition device may be configured to acquire real-time data. The at least one data acquisition device may also be configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material. Furthermore, the at least one data acquisition device may be configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material. That is, the data acquisition device may be moved to allow its area of coverage to encompass the at least one part of the at least one product and / or at least one part of the at least one material in a sequential way. It is possible for the at least one product and or at least one material to be moved in such a way that the at least one data acquisition device's area of coverage encompasses the at least one product and / or at least one material.

[0049] Moreover, the acquiring step may comprise adapting the lighting conditions of the at least one product and / or at least one material accordingly. The acquiring step may also comprise shielding the at least one product and / or at least one material from external lighting. Furthermore, the acquiring step may comprise backlighting the at least one product and / or at least one material with respect to the at least one data acquisition device. The backlighting of the at least one product and / or at least one material may be achieved with a colored light wherein the colored light is chosen according to the at least one product and / or at least one material. Additionally or alternatively, the colored light's spectrum range may comprise the infrared (IR) spectrum in addition to the visible light spectrum.

[0050] In one embodiment, the acquiring step may comprise transmitting the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to a decider module, wherein the decider module allows the processing the at least one data sample of the deciding step. The acquiring step may also comprise transmitting the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in a sequential way and / or in real-time. The acquiring step may furthermore comprise transmitting the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module using a FIFO basis. In another embodiment, wherein the acquiring step comprising acquiring or providing a plurality of data samples, the acquiring step may also comprise ensuring overlap between the data samples. The acquiring step may furthermore comprise operating the at least one data acquisition device such that it may also be configured to acquire the plurality of data samples. The at least one data acquisition device may be configured to move its coverage area to acquire the plurality of data samples. The acquiring step may furthermore comprise receiving at least one acquiring status signal wherein the acquiring step comprises acquiring / stopping to acquire the at least one data sample according to that at least one acquiring status signal.

[0051] In one embodiment, the deciding step may comprise a preprocessing step. The preprocessing step may comprise performing at least one type conversion on the at least one data sample. The preprocessing step may also comprise performing at least one transformation on the at least one data sample such as but not limited to at least one hue adjustment, brightness adjustment, saturation adjustment, rotation, translation, scale manipulation, horizontal flip, vertical flip. Another example of transformation would be applying at least one mosaic function defined such as aligning and stitching together multiple images taken from slightly different perspectives or positions to create a seamless and higher-resolution composite image. A further example of transformation would be applying at least one mix-up function and / or mix-up augmentation defined as a data augmentation technique that generates a weighted combination of random image pairs.

[0052] In another embodiment, the deciding step is performed on at least one artificial intelligence (Al) module. The deciding step may also comprise executing at least one feature extraction function. The deciding step may furthermore comprise executing at least one convolution such as but not restricted to at least one 2D-convolution.

[0053] In a further embodiment the deciding step may comprise executing at least one sequence modelling algorithm. In this case, sequence modelling algorithm would be encompassing algorithms that are specifically designed to understand and model sequences of data, which often involve retaining information over short and / or long periods of time as is the case with RNN, LSTM and transformer architectures. Moreover, the at least one sequence modelling algorithm may be configured to execute at least one Al algorithm that makes use of any kind of working and / or short-term memory. Short-term memory may relate exclusively to information storage while working memory may relate to information storage and manipulation. Furthermore, the deciding step may comprise executing at least one transformer model implementation. Transformer models rely on self-attention mechanisms to process input sequences in parallel, allowing it to capture long-range dependencies efficiently. The architecture consists of stacks of encoders and decoders, with each layer using multi-head self-attention and feed-forward neural networks, complemented by residual connections and layer normalization. This architecture allows for parallelization of data compared to RNN architecture which requires data to be inputted sequentially. Additionally or alternatively, the transformer architecture memory is not restricted by the architecture itself like in RNN and LSTM algorithms, but is only limited by the amount of memory supported by the hardware implemented in the system. This allows for the acquiring step and / or the at least one data acquisition device to make use of a lesser resolution than what is commonly used without impacting the amount of time needed for the deciding step to output a result, and thus showcases a preferred advantage of the present invention.

[0054] In another embodiment, the deciding step may comprise executing image classification, wherein image classification consists of just classifying an image in at least one category, image recognition, wherein image recognition consists of classifying an image in at least one category while outputting the location of the at least one main area for the classification decision, and / or at least one anchor-free detection implementation, wherein anchor-free detection is an object detection approach that eliminates the use of predefined anchor boxes, which are commonly used in traditional anchor-based detectors to predict object locations and sizes. Instead, anchor-free detectors directly predict the key points or the center points of objects along with their dimensions and class probabilities. Anchor- free detection simplifies the detection pipeline, reduces computational overhead, and often improves detection accuracy by avoiding the complexities and limitations associated with anchor box design and matching.

[0055] Furthermore, the deciding step may comprise executing at least one pattern recognition algorithm regardless if that at least one algorithm qualifies as artificial intelligence and / or machine learning. These algorithms would allow reduce the load on the processing hardware. Examples of pattern recognition algorithms would be discriminant analysis, logistic regression, decision trees, Kernel estimation, KNNs, perceptrons, MLPs, NNs, clustering algorithms, PCA, MPCA, RNNs. Additionally or alternatively, the at least one Al module may be configured to execute at least one probabilistic pattern recognition algorithm such as algorithms making use of a frequentist or Bayesian approach. These algorithms would reduce the load on the processing hardware and thus showcases a preferred advantage of the present invention.

[0056] In a further embodiment, the method may comprise training the at least one Al algorithm. The method may also comprise testing the at least one Al algorithm.

[0057] In one embodiment, the deciding step may comprise converting the at least one deciding step result for accessibility according to the controlling step. The controlling step may also comprise accessing at least one probability threshold module. The controlling step may furthermore comprise accessing at least one probability threshold comprised in the at least one probability threshold module, wherein the at least one probability threshold may be adjustable. Each parameter may be assessed with the probability of being true. The at least one probability threshold may serve to tune the process quality baseline. The sensitivity of the system may thus be optimizable to find a balance between a decrease of production downtime, an increase in speed, and / or for testing purposes. Furthermore, the controlling step may comprise outputting the at least one operation command to the production system based on the at least one Al module result and the at least one probability threshold module.

[0058] In another embodiment the controlling step may be configured to transmit at least one start signal to the acquiring step.

[0059] In a further embodiment, the method may comprise outputting at least one system heartbeat state such as "live" or "Not responding". Additionally or alternatively, the controlling step may comprise transmitting the at least one system heartbeat state to the production system. Should the system heartbeat state be able to be translated to "Not responding", the production system may output an error state. The method may also comprise outputting at least one system status state such as but not limited to "waiting for next product", "processing", "error".

[0060] The at least one system status state and / or the at least one system heartbeat state may depend on all of the modules making use of the method and / or at least part of the method. The method may comprise outputting at least one system decision such as "OK" or "Not OK". The method may comprise furthermore outputting at least one defect ID. The at least one system decision and / or the at least one defect ID would relate to the at least one operation command to the production system based on the at least one deciding step result and the at least one probability threshold module. Specifically, the at least one system decision would relate to the operation commands provided by the controlling step wherein the production system may be configured to discard and / or retain the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controlling step.

[0061] Additionally or alternatively, the production system may be configured to transmit at least one system status state to the controlling step. The controlling step may comprise transmitting at least one system decision to the production system.

[0062] Moreover, the controlling step may comprise transmitting at least one system decision and / or at least one defect ID to the production system. The controlling step may comprise transmitting an acquiring status signal to the acquiring step. The acquiring step may be configured to receive at least one acquiring status signal wherein the acquiring step comprises acquiring / stopping to acquire the at least one data sample according to that at least one acquiring status signal. Additionally or alternatively, communication between the modules of the system performing the method may be achieved by different communication means and protocols. For example, modules may communicate over TCP / IP (approximately OSI layer 4) protocol for high-bandwidth, low latency communication and easy interoperability. The controller may store the at least one data sample on a local or network accessible drive. The at least one data sample and / or any metadata that is related to the system may be read directly from that location, over local network, by the decider module. Any of the at least one results may be communicated to the controller over MQTT protocol (OSI layer 7), with the payload dependent on the controller module's relay capabilities. For flexibility, the decider may contain an adapter for translating purposes explained previously. The modules comprising the system performing the method may be configured to communicate wirelessly.

[0063] Furthermore, the method may comprise operating a user interface device wherein the user interface device may be configured to display information relating to the method. Examples of such user interfaces may be screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of the system's parameters to the user), and / or speakers configured to communicate audio data (e.g. playing audio data to the user).

[0064] In one embodiment, the at least one deciding step result may be indicative of the quality of at least one material comprising wood / at least one product comprising wood. The at least one deciding step result may also indicative of the quality of at least one sheet comprising wood, more preferably at least one veneer sheet, at least one manufactured spring, at least one manufactured mat and / or at least one currently being manufactured mat.

[0065] In a further embodiment, the deciding step may comprise identifying at least one along the grain crack, at least one perpendicular to grain direction crack, at least one crack at any angle, at least one branch knot, at least one splinter, at least one ragged edge, at least one undesirable shape such as a non-rectangular sheet, at least one undesirable edge such as a non-straight edge and / or a ragged edge, at least one out of range dimension, at least one loss of material, at least one excessively thin area and / or edge, and / or at least one discoloration, in the at least one sheet comprising wood.

[0066] Additionally or alternatively, the deciding step may comprise identifying if more than one sheet was inputted into the production system.

[0067] In another embodiment, the deciding step may comprise identifying at least one burnt patch, at least one deformation, at least one branch knot defect, at least one splinter protruding out, at least one crack, along the grain crack, at least one perpendicular to grain direction crack, at least one crack at any angle, at least one strongly ragged edge, at least one discontinuity, at least one dent, and / or at least one discoloration in the at least one manufactured spring.

[0068] Additionally or alternatively, the deciding step may comprise identifying at least one additional spring produced onto the at least one manufactured spring.

[0069] Moreover, the deciding step may comprise at least one spring geometry tolerance value, wherein the deciding step may comprise identifying at least one defect in the at least one manufactured spring related to the at least one wood springs geometry tolerance value. The deciding step may furthermore comprise at least one coloration data, wherein the deciding step may comprise identifying at least one defect in the at least one manufactured spring related to the at least one coloration data.

[0070] In another embodiment, the deciding step may comprise identifying if at least one spring, and / or at least one coil of the at least one spring is snapped to at least one working plate with at least one fixing pin in the at least one mat. The at least one working plate would relate to a support plate comprising at least one pin for mat production in the production system. The deciding step may also comprise identifying if the step of at least one spring relative to at least one pin is uniform in the at least one mat. Additionally or alternatively, the deciding step may comprise identifying if at least one spring is deformed and / or crushed in the at least one mat. Furthermore, the deciding step may comprise identifying if at least one protruding feature of at least one spring can extend out of the at least one spring's general diameter in the at least one mat.

[0071] The at least one material comprising wood and / or the at least one product comprising wood may substantially be made from wood and / or at least a partially woody plant such as but not limited to bamboo, willow, rattan, reed, cane, dried palm leaves.

[0072] The at least one material comprising wood and / or the at least one product comprising wood may comprise a mass fraction of woody substance amounting to at least 35%, preferably at least 50%, more preferably at least 70% and most preferably at least 90%. The at least one manufactured spring, for example, may comprise a mass fraction of woody substance amounting to at least 50%, preferably at least 70% and most preferably at least 90%.

[0073] Additionally and alternatively, the at least one manufactured spring may comprise an outer diameter of 5mm to 30 mm, preferably 8 mm to 20mm, and more preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position. The at least one manufactured spring also comprises a length of 10-5000 mm, preferably 150 mm to 1500 mm, more preferably 200 mm to 1000 mm, and most preferably 200 mm to 900 mm in a substantially unbiased state. Moreover, the at least one manufactured spring's strip further comprises a width of 1 mm to 20 mm, preferably 2 mm to 7 mm, more preferably 3 mm to 7 mm, and most preferably 4 mm to 6 mm in a substantially unbiased state, as well as may comprise an outer diameter of 5 mm to 60 mm, preferably 5 mm to 20 mm, more preferably 8 mm to 15 mm, and most preferably 10 mm to 12 mm in a substantially unbiased state.

[0074] Furthermore, the method described herein is independent from the dimension of the at least one sheet comprising wood. The system described herein is also configured to be independent of the dimensions of the at least one sheet comprising wood. This is a preferred advantage of the invention disclosed herein.

[0075] In one embodiment, the system may comprise a logger module, wherein the logger module may comprise instructions which, when executed, cause the system to carry out the method as described previously. The logger module may comprise logs of all operations performed by the system, logs of all operations induced by the method, logs of all metadata required for assessing the system, logs of all metadata required for evaluating the decider module, logs of all metadata required for training the at least one Al module, and / or logs of all data required for training the at least one Al module.

[0076] In another embodiment, the system may comprise a data processing system comprising at least one data processing device wherein the data processing system may be configured to perform the method.

[0077] The data processing system may also comprise one or more processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions). The processing unit(s) may be singular or plural. For example, the data- processing system may comprise at least one of CPU, GPU, DSP, APU, ASIC, ASIP or FPGA. The data processing system may comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and / or secondary memory (e.g. HDD, SDD). The data processing system may comprise volatile and / or non-volatile memory such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM. The data processing may comprise a combination of components to perform the method previously described, such as and preferably at least on GPU and a combination of memory components allowing for high memory storage to efficiently perform at least one transformer model implementation.

[0078] The data processing system may comprise internal communication interfaces (e.g. busses) configured to facilitate electronic data exchange between components of the data processing system, such as, the communication between the memory components and the processing components. The data processing system may comprise external communication interfaces configured to facilitate electronic data exchange between the data processing system and devices or networks external to the data processing system. For example, the data processing system may comprise network interface card(s) that may be configured to connect the data processing system to a network, such as, to the Internet. The data processing system may be configured to transfer electronic data using a standardized communication protocol. The data processing system may be a centralized or distributed computing system.

[0079] Embodiments

[0080] Below, system embodiments will be discussed. These embodiments are abbreviated with the letter S followed by a number. Whenever reference is herein made to system embodiments, these embodiments are meant.

[0081] 51. A system for a production system configured to manufacture at least one product from at least one material, the system comprising: a data acquisition module configured to acquire at least one data sample from the at least one product and / or the at least one material, a decider module configured to process of the at least one data sample related to the at least one product and / or the at least one material resulting in at least one decider module result, and a controller module configured to provide operational commands to the production system according to the at least one decider module result.

[0082] 52. The system according to the preceding system embodiment, the system further comprising the production system configured to manufacture at least one product from at least one material.

[0083] 53. The system according to any of the preceding system embodiment wherein the system is configured to perform quality control on the at least one material and / or the at least one product.

[0084] 54. The system according to any of the preceding system embodiment wherein the system is configured to perform quality control on the at least one material while being manufactured into the at least one product.

[0085] 55. The system according to the preceding system embodiment wherein the production system is configured to discard the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controller module.

[0086] 56. The system according to any of the preceding systems embodiments wherein the production system is configured to retain the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controller module. S7. The system according to any of the preceding system embodiments wherein the production system is configured to detect if the at least one product and / or the at least one material is in the correct position.

[0087] 58. The system according to the preceding embodiment wherein the production system is configured to output at least one data indicative of the positioning of the least one product and / or at least one material.

[0088] 59. The system according to any of the preceding embodiments wherein the data acquisition module is configured to acquire at least one data sample.

[0089] 510. The system according to any of the preceding system embodiments wherein the data acquisition module is configured to acquire at least one data sample in realtime.

[0090] 511. The system according to any of the preceding system embodiments wherein the data acquisition module is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material.

[0091] 512. The system according to any of the preceding system embodiments wherein the data acquisition module is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in realtime.

[0092] 513. The system according to any of the preceding system embodiments wherein the data acquisition module is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way.

[0093] 514. The system according to any of the preceding system embodiment wherein the data acquisition module comprises at least one data acquisition device.

[0094] 515. The system according to preceding system embodiment wherein the at least one data acquisition device is positioned relative to the production system in a manner conducive to acquiring the at least one data sample.

[0095] 516. The system according to preceding system embodiment wherein the at least one data acquisition device is positioned essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material in a manner conducive to acquiring the at least one data sample.

[0096] 517. The system according to any of two preceding system embodiments wherein the at least one data acquisition device is configured to acquire real-time data.

[0097] 518. The system according to any of the three preceding system embodiments wherein the at least one data acquisition device is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material.

[0098] 519. The system according to any of the four preceding system embodiments wherein the at least one data acquisition device is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material.

[0099] 520. The system according to any of the preceding embodiments wherein the data acquisition module is configured to adapt the lighting conditions of the at least one product and / or at least one material accordingly.

[0100] 521. The system according to any of the preceding embodiments wherein the data acquisition module is configured to shield the at least one product and / or at least one material from external lighting.

[0101] 522. The system according to any of the preceding embodiments wherein the data acquisition module is configured to backlight the at least one product and / or at least one material with respect to the at least one data acquisition device.

[0102] 523. The system according to the preceding embodiment wherein the data acquisition module is configured to backlight the at least one product and / or at least one material with a colored light wherein the colored light is chosen according to the at least one product and / or at least one material.

[0103] 524. The system according to the preceding embodiment wherein the colored light's spectrum range comprises the IR spectrum in addition to the visible light spectrum.

[0104] 525. The system according to any of the preceding embodiments wherein the data acquisition module is configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module.

[0105] 526. The system according to any of the preceding embodiments wherein the data acquisition module is configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in a sequential way.

[0106] 527. The system according to any of the preceding embodiments wherein the data acquisition module is configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in real-time.

[0107] 528. The system according to any of the preceding embodiments wherein the data acquisition module is configured to transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module using a FIFO basis.

[0108] 529. The system according to any of the preceding embodiments wherein a data acquisition module configured to acquire or provide a plurality of data samples and wherein the data acquisition module is configured to ensure overlap between the data samples.

[0109] 530. The system according to any of the preceding embodiments with the features of system embodiments S14 and S29 wherein the at least one data acquisition device is configured to acquire the plurality of data samples.

[0110] 531. The system according to the preceding embodiment wherein the at least one data acquisition device is configured to move its coverage area to acquire the plurality of data samples.

[0111] 532. The system according to any of the preceding embodiments wherein the data acquisition module is configured to receive at least one acquiring status signal.

[0112] 533. The system according to the preceding embodiment wherein the data acquisition module acquires / stops acquiring the at least one data sample according to the at least one acquiring status signal. 534. The system according to any of the preceding system embodiments wherein the decider module comprises a preprocessing module.

[0113] 535. The system according to the preceding system embodiment wherein the preprocessing module is configured to perform at least one transformation on the at least one data sample.

[0114] 536. The system according to any of system embodiments S34-S35wherein the preprocessing module is configured to perform at least one type conversion on the at least one data sample.

[0115] 537. The system according to any of system embodiments S34-S36 wherein the preprocessing module is configured to perform at least one hue adjustment on the at least one data sample.

[0116] 538. The system according to any of system embodiments S34-S37 wherein the preprocessing module is configured to perform at least one brightness adjustment on the at least one data sample.

[0117] 539. The system according to any of system embodiments S34-S38 wherein the preprocessing module is configured to perform at least one saturation adjustment on the at least one data sample.

[0118] 540. The system according to any of system embodiments S34-S39 wherein the preprocessing module is configured to perform at least one rotation manipulation on the at least one data sample.

[0119] 541. The system according to any of system embodiments S34-S40 wherein the preprocessing module is configured to perform at least one translation manipulation on the at least one data sample.

[0120] 542. The system according to any of system embodiments S34-S41 wherein the preprocessing module is configured to perform at least one scale manipulation on the at least one data sample.

[0121] S43. The system according to any of system embodiments S34-S42 wherein the preprocessing module is configured to perform at least one horizontal flip on the at least one data sample. S44. The system according to any of system embodiments S34-S43 wherein the preprocessing module is configured to perform at least one vertical flip on the at least one data sample.

[0122] 545. The system according to any of system embodiments S34-S44 wherein the preprocessing module is configured to perform at least one mosaic function on the at least one data sample.

[0123] 546. The system according to any of system embodiments S34-S45 wherein the preprocessing module is configured to perform at least one mixup function on the at least one data sample.

[0124] 547. The system according to any of the preceding system embodiments wherein the decider module comprises at least one Al module.

[0125] 548. The system according to the preceding system embodiment wherein the at least one Al module is configured to execute at least one feature extraction function.

[0126] 549. The system according to any of the two preceding system embodiments wherein the at least one Al module is configured to execute at least one convolution.

[0127] 550. The system according to any of preceding system embodiments S47-S49 wherein the at least one Al module is configured to execute at least one 2D-convolution.

[0128] 551. The system according to any of preceding system embodiments S47-S50 wherein the at least one Al module is configured to execute at least one sequence modelling algorithm.

[0129] 552. The system according to the preceding embodiment wherein the at least one sequence modelling algorithm is configured to execute at least one Al algorithm that makes use of any kind of working and / or short-term memory.

[0130] 553. The system according to any of the five preceding embodiments wherein the at least one Al module is configured to execute at least one transformer model implementation.

[0131] 554. The system according to any of embodiments S47-S53 wherein the at least one Al module is configured to execute image classification. S55. The system according to any of embodiments S47-S54 wherein the at least one Al module is configured to execute image recognition.

[0132] 556. The system according to any of embodiments S47-S55 wherein the at least one Al module is configured to execute at least one anchor-free detection implementation.

[0133] 557. The system according to any of embodiments S47-S56 wherein the at least one Al module is configured to execute at least one pattern recognition algorithm regardless if that at least one algorithm qualifies as artificial intelligence and / or machine learning.

[0134] 558. The system according to any embodiments S47-S57 wherein the at least one Al module is configured to execute at least one probabilistic pattern recognition algorithm.

[0135] 559. The system according to any preceding system embodiment wherein the decider module is configured to convert the at least one decider module result for accessibility according to the controller module.

[0136] 560. The system according to any of the preceding embodiments wherein the controller module comprises at least one probability threshold module.

[0137] 561. The system according to the preceding embodiment wherein the at least one probability threshold module comprises at least one probability threshold.

[0138] 562. The system according to the preceding embodiment wherein the at least one probability threshold is adjustable.

[0139] 563. The system according to any of preceding embodiments S60-S62 wherein the controller module is configured to output the at least one operation command to the production system based on the at least one Al module result and the at least one probability threshold module.

[0140] 564. The system according to any of the preceding embodiments wherein the controller module is configured to transmit at least one start signal to the data acquisition module.

[0141] 565. The system according to any of the preceding embodiments wherein the system is configured to output at least one system heartbeat state. S66. The system according to any of the preceding embodiments wherein the system is configured to output at least one system status state.

[0142] 567. The system according to any of the preceding embodiments wherein the system is configured to output at least one system decision.

[0143] 568. The system according to any of the preceding embodiments wherein the system is configured to output at least one defect ID.

[0144] 569. The system according to any of the preceding embodiments with the features of system embodiment S2 wherein the controller module is configured to transmit at least one system heartbeat state to the production system.

[0145] 570. The system according to any of the preceding embodiments with the features of system embodiment S2 wherein the production system is configured to transmit at least one system status state to the controller module.

[0146] 571. The system according to any of the preceding embodiments with the features of system embodiment S2 wherein the controller module is configured to transmit at least one system decision to the production system.

[0147] 572. The system according to any of the previous embodiments with the features of system embodiment S2 wherein the controller module is configured to transmit at least one defect ID to the production system.

[0148] 573. The system according to any of the previous embodiments wherein the controller module is configured to transmit an acquiring status signal to the data acquisition module.

[0149] 574. The system according to any of the preceding embodiments wherein the modules comprising the system are configured to communicate wirelessly.

[0150] 575. The system according to any preceding embodiment wherein the system comprises a user interface device.

[0151] 576. The system according to the previous embodiment wherein the user interface device is configured to display information relating to the system. S77. The system according to any of the preceding embodiments wherein the at least one decider module result is indicative of the quality of at least one material comprising wood / at least one product comprising wood.

[0152] 578. The system according to the preceding embodiment wherein the at least one decider module result is indicative of the quality of at least one sheet comprising wood.

[0153] 579. The system according to the preceding embodiment wherein the at least one decider module result is indicative of the quality of at least one veneer sheet.

[0154] 580. The system according to any of the preceding embodiments with the features of embodiment S77, wherein the at least one decider module result is indicative of the quality of at least one manufactured spring.

[0155] 581. The system according to any of the preceding embodiments with the features of embodiment S77, wherein the at least one decider module result is indicative of the quality of at least one manufactured mat.

[0156] 582. The system according to any of the preceding embodiments with the features of embodiment S77, wherein the at least one decider module result is indicative of the quality of at least one currently being manufactured mat.

[0157] 583. The system according any of the preceding system embodiments with the features of system embodiments S78-S82 wherein the decider module is configured to identify at least one crack in the at least one sheet comprising wood.

[0158] 584. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S83 wherein the decider module is configured to identify at least one branch knot in the at least one sheet comprising wood.

[0159] 585. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S84 wherein the decider module is configured to identify at least one splinter in the at least one sheet comprising wood.

[0160] 586. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S85 wherein the decider module is configured to identify at least one ragged edge in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S86 wherein the decider module is configured to identify at least one undesirable shape of the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S87 wherein the decider module is configured to identify at least one undesirable edge in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S88 wherein the decider module is configured to identify at least one out of range dimension in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S89 wherein the decider module is configured to identify at least one loss of material in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S90 wherein the decider module is configured to identify at least one excessively thin area and / or edge in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S91 wherein the decider module is configured to identify if more than one sheet was inputted into the system. The system according to any of the preceding system embodiments with the features of any of system embodiments S78-S92 wherein the decider module is configured to identify at least one discoloration in the at least one sheet comprising wood. The system according to any of the preceding system embodiments with the features of system embodiment S80 wherein the decider module is configured to identify at least one burnt patch in the at least one manufactured spring. 595. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94 wherein the decider module is configured to identify at least one deformation in the at least one manufactured spring.

[0161] 596. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S95 wherein the decider module is configured to identify at least one branch knot defect in the at least one manufactured spring.

[0162] 597. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S96 wherein the decider module is configured to identify at least one splinter in the at least one manufactured spring.

[0163] 598. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S97 wherein the decider module is configured to identify at least one crack in the at least one manufactured spring.

[0164] 599. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S97 wherein the decider module is configured to identify at least one along the grain crack in the at least one manufactured spring.

[0165] 5100. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S99 wherein the decider module is configured to identify at least one perpendicular to grain direction crack in the at least one manufactured spring.

[0166] 5101. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S100 wherein the decider module is configured to identify at least one crack at any angle in the at least one manufactured spring.

[0167] 5102. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S101 wherein the decider module is configured to identify at least one ragged edge in the at least one manufactured spring. 5103. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S102 wherein the decider module is configured to identify at least one discontinuity in the at least one manufactured spring.

[0168] 5104. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S103 wherein the decider module is configured to identify if the at least one manufactured spring is dented.

[0169] 5105. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S104 wherein the decider module is configured to identify at least one additional spring produced onto the at least one manufactured spring.

[0170] 5106. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S105 wherein the decider module is configured to identify at least one discoloration in the at least one manufactured spring.

[0171] 5107. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S106 wherein the decider module comprises at least one spring geometry tolerance value.

[0172] 5108. The system according to any of the preceding system embodiments with the features of system embodiment S107 wherein the decider module is configured to identify at least one defect in the at least one manufactured spring related to the at least one spring geometry tolerance value.

[0173] 5109. The system according to any of the preceding system embodiments with the features of any of system embodiments S80, S94-S108 wherein the decider module comprises at least one coloration data.

[0174] SI 10. The system according to any of the preceding system embodiments with the features of system embodiment S109 wherein the decider module is configured to identify at least one defect in the at least one manufactured spring related to the at least one coloration data.

[0175] Sill. The system according to any of the preceding system embodiments with the features of system embodiments S81 or S82 wherein the decider module is configured to identify if at least one spring is snapped to at least one working plate with at least one fixing pin in the at least one mat. The system according to any of the preceding system embodiments with the features of system embodiments S81 or S82 wherein the decider module is configured to identify if the step of at least one spring relative to at least one pin is uniform in the at least one mat. The system according to any of the preceding system embodiments with the features of system embodiments S81 or S82 wherein the decider module is configured to identify if at least one spring is deformed and / or crushed in the at least one mat. The system according to any of the preceding system embodiments with the features of system embodiments S81 or S82 wherein the decider module is configured to identify if at least one protruding feature of at least one spring can extend out of the at least one spring's general diameter in the at least one mat. The system according to any of the preceding system embodiments with the features of system embodiment S77, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from wood. The system according to any of the preceding system embodiments with the features of system embodiment S77, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least a partially wood plant. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least bamboo. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least willow. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least rattan.

[0176] 5120. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least reed.

[0177] 5121. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least cane.

[0178] 5122. The system according to any of the preceding system embodiments with the features of system embodiment SI 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least dried palm leaves.

[0179] 5123. The system according to any of the preceding system embodiments with the features of system embodiment S77, wherein the at least one material comprising wood and / or the at least one product comprising wood comprise a mass fraction of woody substance amounting to at least 35%, preferably at least 50%, more preferably at least 70% and most preferably at least 90%.

[0180] 5124. The system according to any of the preceding system embodiments with the features of system embodiment S80, wherein the at least one material comprising wood and / or the at least one manufactured spring comprise a mass fraction of woody substance amounting to at least 50%, preferably at least 70% and most preferably at least 90%.

[0181] 5125. The system according to any of the preceding system embodiments with the features of system embodiment S80, wherein the at least one manufactured spring comprises an outer diameter of 5mm to 20 mm, preferably 8 mm to 15mm, and more preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position. 5126. The system according to any of the preceding system embodiments with the features of system embodiment S80, wherein the at least one manufactured spring comprises a length of 10-5000 mm, preferably 150 mm to 1500 mm, more preferably 200 mm to 1000 mm, and most preferably 200 mm to 900 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0182] 5127. The system according to any of the preceding system embodiments with the features of system embodiment S80, wherein the at least one manufactured spring's strip comprises a width of 1 mm to 20 mm, preferably 2 mm to 7 mm, more preferably 3 mm to 7 mm, and most preferably 4 mm to 6 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0183] 5128. The system according to any of the preceding system embodiments with the features of system embodiment S80, wherein the at least one manufactured spring comprises an outer diameter of 5 mm to 60 mm, preferably 5 mm to 20 mm, more preferably 8 mm to 15 mm, and most preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0184] 5129. The system according to any of the preceding system embodiments with the features of system embodiment S78, wherein the system is configured to be independent of the dimensions of the at least one sheet comprising wood.

[0185] Below, method embodiments will be discussed. These embodiments are abbreviated with the letter M followed by a number. Whenever reference is herein made to method embodiments, these embodiments are meant.

[0186] Ml. A method for a production system configured to manufacture at least one product from at least one material, the method comprising: an acquiring step, wherein the acquiring step comprises acquiring at least one data sample from the at least one product and / or the at least one material, a deciding step wherein the deciding step comprises processing the at least one data sample related to the at least one product and / or the at least one material resulting in at least one deciding step result, and a controlling step, wherein the controlling step comprises providing operational commands to the production system according to the at least one deciding step result.

[0187] M2. The method according to the preceding method embodiment, the method further comprising the production system configured to manufacture at least one product from at least one material.

[0188] M3. The method according to any of the preceding method embodiment wherein the method comprises performing quality control on the at least one material and / or the at least one product.

[0189] M4. The method according to any of the preceding method embodiment wherein the method comprises performing quality control on the at least one material while being manufactured into the at least one product.

[0190] M5. The method according to the preceding method embodiment wherein the production system is configured to discard the at least one material and / or the at least one product being manufactured according to the operational commands generated by the controlling step.

[0191] M6. The method according to any of the preceding methods embodiments wherein the production system is configured to retain the at least one material and / or the at least one product being manufactured according to the operational commands generated by the controlling step.

[0192] M7. The method according to any of the preceding method embodiments wherein the production system is configured to detect if the at least one product and / or the at least one material is in the correct position.

[0193] M8. The method according to the preceding embodiment wherein the production system is configured to output at least one data indicative of the positioning of the least one product and / or at least one material.

[0194] M9. The method according to any of the preceding embodiments wherein the acquiring step comprises acquiring at least one data sample. MIO. The method according to any of the preceding method embodiments wherein the acquiring step comprises acquiring at least one data sample in real-time.

[0195] Mil. The method according to any of the preceding method embodiments wherein the acquiring step comprises acquiring data from at least one part of the at least one product and / or at least one part of the at least one material.

[0196] M12. The method according to any of the preceding method embodiments wherein the acquiring step comprises acquiring data from at least one part of the at least one product and / or at least one part of the at least one material in real-time.

[0197] M13. The method according to any of the preceding method embodiments wherein the acquiring step comprises acquiring data from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way.

[0198] M14. The method according to any of the preceding method embodiment wherein the acquiring step comprises acquiring the at least one data sample provided from at least one data acquisition device.

[0199] M15. The method according to preceding method embodiment wherein the acquiring step comprises positioning the at least one data acquisition device relative to the production system in a manner conducive to acquiring the at least one data sample.

[0200] M16. The method according to preceding method embodiment wherein the acquiring step comprises positioning the at least one data acquisition device essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material in a manner conducive to acquiring the at least one data sample.

[0201] M17. The method according to any of two preceding method embodiments wherein the at least one data acquisition device is configured to acquire real-time data.

[0202] M18. The method according to any of the three preceding method embodiments wherein the acquiring step comprises acquiring data provided from the at least one data acquisition device, wherein the data relates to at least one part of the at least one product and / or at least one part of the at least one material.

[0203] M19. The method according to any of the four preceding method embodiments wherein the acquiring step comprises acquiring data provided from the at least one data acquisition device, wherein the data relates to at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material.

[0204] M20. The method according to any of the preceding embodiments wherein the acquiring step comprises adapting the lighting conditions of the at least one product and / or at least one material accordingly.

[0205] M21. The method according to any of the preceding embodiments wherein the acquiring step comprises shielding the at least one product and / or at least one material from external lighting.

[0206] M22. The method according to any of the preceding embodiments, with the features of method embodiment S14, wherein the acquiring step comprises backlighting the at least one product and / or at least one material with respect to the at least one data acquisition device.

[0207] M23. The method according to the preceding embodiment wherein the acquiring step comprises backlight the at least one product and / or at least one material with a colored light wherein the colored light is chosen according to the at least one product and / or at least one material.

[0208] M24. The method according to the preceding embodiment wherein the colored light's spectrum range comprises the IR spectrum in addition to the visible light spectrum.

[0209] M25. The method according to any of the preceding embodiments wherein the acquiring step comprises transmitting the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to a decider module, wherein the decider module allows the processing the at least one data sample of the deciding step.

[0210] M26. The method according to the preceding embodiment wherein the acquiring step comprises transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in a sequential way.

[0211] M27. The method according to any of the two preceding embodiments wherein the acquiring step comprises transmit the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module in real-time.

[0212] M28. The method according to any of the three preceding embodiments wherein the acquiring step comprises transmitting the data acquired from at least one part of the at least one product and / or at least one part of the at least one material to the decider module using a FIFO basis.

[0213] M29. The method according to any of the preceding embodiments wherein the acquiring step comprises acquiring or providing a plurality of data samples and wherein the acquiring step comprises ensuring overlap between the data samples.

[0214] M30. The method according to any of the preceding embodiments with the features of method embodiments S14 and S29 wherein the at least one data acquisition device is configured to acquire the plurality of data samples.

[0215] M31. The method according to the preceding embodiment wherein the data acquiring step comprises moving the coverage area of the at least one data acquisition device to acquire the plurality of data samples.

[0216] M32. The method according to any of the preceding embodiments wherein the acquiring step comprises receiving at least one acquiring status signal.

[0217] M33. The method according to the preceding embodiment wherein the acquiring step comprises acquiring / stopping to acquire the at least one data sample according to at least one acquiring status signal.

[0218] M34. The method according to any of the preceding method embodiments wherein the deciding step comprises a preprocessing step.

[0219] M35. The method according to the preceding method embodiment wherein the preprocessing step comprises performing at least one transformation on the at least one data sample.

[0220] M36. The method according to any of method embodiments S34-S35, wherein the preprocessing step comprises performing at least one type conversion on the at least one data sample. M37. The method according to any of method embodiments S34-S36 wherein the preprocessing step comprises performing at least one hue adjustment on the at least one data sample.

[0221] M38. The method according to any of method embodiments S34-S37 wherein the preprocessing step comprises performing at least one brightness adjustment on the at least one data sample.

[0222] M39. The method according to any of method embodiments S34-S38 wherein the preprocessing step comprises performing at least one saturation adjustment on the at least one data sample.

[0223] M40. The method according to any of method embodiments S34-S39 wherein the preprocessing step comprises performing at least one rotation manipulation on the at least one data sample.

[0224] M41. The method according to any of method embodiments S34-S40 wherein the preprocessing step comprises performing at least one translation manipulation on the at least one data sample.

[0225] M42. The method according to any of method embodiments S34-S41 wherein the preprocessing step comprises performing at least one scale manipulation on the at least one data sample.

[0226] M43. The method according to any of method embodiments S34-S42 wherein the preprocessing step comprises performing at least one horizontal flip on the at least one data sample.

[0227] M44. The method according to any of method embodiments S34-S43 wherein the preprocessing step comprises performing at least one vertical flip on the at least one data sample.

[0228] M45. The method according to any of method embodiments S34-S44 wherein the preprocessing step comprises performing at least one mosaic function on the at least one data sample.

[0229] M46. The method according to any of method embodiments S34-S45 wherein the preprocessing step comprises performing at least one mixup function on the at least one data sample. M47. The method according to any of the preceding method embodiments wherein at least part of the deciding step is performed on at least one Al module.

[0230] M48. The method according to the preceding method embodiment wherein the deciding step comprises executing at least one feature extraction function.

[0231] M49. The method according to any of the two preceding method embodiments wherein the deciding step comprises executing at least one convolution.

[0232] M50. The method according to any of preceding method embodiments S47-S49 wherein the deciding step comprises executing at least one 2D-convolution.

[0233] M51. The method according to any of preceding method embodiments S47-S50 wherein the deciding step comprises executing at least one sequence modelling algorithm.

[0234] M52. The method according to the preceding embodiment wherein the at least one sequence modelling algorithm comprises executing at least one Al algorithm that makes use of any kind of working and / or short-term memory.

[0235] M53. The method according to any of the five preceding embodiments wherein the deciding step comprises executing at least one transformer model implementation.

[0236] M54. The method according to any of embodiments S47-S53 wherein the deciding step comprises executing image classification.

[0237] M55. The method according to any of embodiments S47-S54 wherein the deciding step comprises executing image recognition.

[0238] M56. The method according to any of embodiments S47-S55 wherein the deciding step comprises executing at least one anchor-free detection implementation.

[0239] M57. The method according to any of embodiments S47-S56 wherein the deciding step comprises executing at least one pattern recognition algorithm regardless if that at least one algorithm qualifies as artificial intelligence.

[0240] M58. The method according to any embodiments S47-S57 wherein the deciding step comprises executing at least one probabilistic pattern recognition algorithm. M59. The method according to any preceding method embodiment wherein the deciding step comprises converting the at least one deciding step result for accessibility according to the controlling step.

[0241] M60. The method according to any of the preceding embodiments wherein the controlling step comprises accessing at least one probability threshold module.

[0242] M61. The method according to the preceding embodiment wherein the controlling step comprises accessing at least one probability threshold comprised in the at least one probability threshold module.

[0243] M62. The method according to the preceding embodiment wherein the at least one wherein the controlling step comprises adjusting the at least one probability threshold.

[0244] M63. The method according to any of preceding embodiments S60-M62 wherein the controlling step comprises outputting the at least one operation command to the production system based on the at least one decider result and the at least one probability threshold.

[0245] M64. The method according to any of the preceding embodiments wherein the controlling step comprises transmitting at least one start signal to the acquiring step.

[0246] M65. The method according to any of the preceding embodiments wherein the method comprises outputting at least one system heartbeat state.

[0247] M66. The method according to any of the preceding embodiments wherein the method comprises outputting at least one system status state.

[0248] M67. The method according to any of the preceding embodiments wherein the method comprises outputting at least one system decision.

[0249] M68. The method according to any of the preceding embodiments wherein the method comprises outputting at least one defect ID.

[0250] M69. The method according to any of the preceding embodiments with the features of method embodiment S2 wherein the controlling step comprises transmitting at least one system heartbeat state to the production system. M70. The method according to any of the preceding embodiments with the features of method embodiment S2 wherein the production system comprises transmitting at least one method status state to the controlling step.

[0251] M71. The method according to any of the preceding embodiments with the features of method embodiment S2 wherein the controlling step comprises transmitting at least one system decision to the production system.

[0252] M72. The method according to any of the previous embodiments with the features of method embodiment S2 wherein the controlling step comprises transmitting at least one defect ID to the production system.

[0253] M73. The method according to any of the previous embodiments wherein the controlling step comprises transmitting an acquiring status signal to the acquiring step.

[0254] M74. The method according to any of the preceding embodiments wherein the method comprises wireless communication between the modules comprising the system according to any of the preceding system embodiment.

[0255] M75. The method according to any preceding embodiment wherein the method comprises operating a user interface device.

[0256] M76. The method according to the preceding embodiment wherein the method comprises displaying information relating to the method, using the user interface device.

[0257] M77. The method according to any of the preceding embodiments wherein the at least one deciding step result is indicative of the quality of at least one wood-based material / at least one wood-based product.

[0258] M78. The method according to any of the preceding embodiments wherein the at least one deciding step result is indicative of the quality of at least one sheet comprising wood.

[0259] M79. The method according to the preceding embodiment wherein the at least one deciding step result is indicative of the quality of at least one veneer sheet.

[0260] M80. The method according to any of the preceding embodiments wherein the at least one deciding step result is indicative of the quality of at least one manufactured spring. M81. The method according to any of the preceding embodiments wherein the at least one deciding step result is indicative of the quality of at least one manufactured mat.

[0261] M82. The method according to any of the preceding embodiments wherein the at least one deciding step result is indicative of the quality of at least one currently being manufactured mat.

[0262] M83. The method according any of the preceding method embodiments with the features of any of method embodiments S78-S82 wherein the deciding step comprises identifying at least one crack in the at least one sheet comprising wood.

[0263] M84. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S83 wherein the deciding step comprises identifying at least one branch knot in the at least one sheet comprising wood.

[0264] M85. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S84 wherein the deciding step comprises identifying at least one splinter in the at least one sheet comprising wood.

[0265] M86. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S85 wherein the deciding step comprises identifying at least one ragged edge in the at least one sheet comprising wood.

[0266] M87. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S86 wherein the deciding step comprises identifying at least one undesirable shape of the at least one sheet comprising wood.

[0267] M88. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S87 wherein the deciding step comprises identifying at least one undesirable edge in the at least one sheet comprising wood.

[0268] M89. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S88 wherein the deciding step comprises identifying at least one out of range dimension in the at least one sheet comprising wood.

[0269] M90. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S89 wherein the deciding step comprises identifying at least one loss of material in the at least one sheet comprising wood.

[0270] M91. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S90 wherein the deciding step comprises identifying at least one excessively thin in the at least one sheet comprising wood.

[0271] M92. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S91 wherein the deciding step comprises identifying if more than one sheet was inputted into the method.

[0272] M93. The method according to any of the preceding method embodiments with the features of any of method embodiments S78-S92 wherein the deciding step comprises identifying at least one discoloration in the at least one sheet comprising wood.

[0273] M94. The method according to any of the preceding method embodiments with the features of method embodiment S80 wherein the deciding step comprises identifying at least one burnt patch in the at least one manufactured spring.

[0274] M95. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94 wherein the deciding step comprises identify at least one deformation in the at least one manufactured spring.

[0275] M96. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S95 wherein the deciding step comprises identifying at least one branch knot defect in the at least one manufactured spring.

[0276] M97. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S96 wherein the deciding step comprises identifying at least one splinter in the at least one manufactured spring. M98. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S97 wherein the deciding step comprises identifying at least one crack in the at least one manufactured spring.

[0277] M99. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S97 wherein the deciding step comprises identifying at least one along the grain crack in the at least one manufactured spring.

[0278] M100. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S99 wherein the deciding step comprises identifying at least one perpendicular to grain direction crack in the at least one manufactured spring.

[0279] M101. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S100 wherein the deciding step comprises identifying at least one crack at any angle in the at least one manufactured spring.

[0280] M102. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S101 wherein the deciding step comprises identifying at least one ragged edge in the at least one manufactured spring.

[0281] M103. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S102 wherein the deciding step comprises identifying at least one discontinuity in the at least one manufactured spring.

[0282] M104. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S103 wherein the deciding step comprises identifying if the at least one manufactured spring is dented.

[0283] M105. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S104 wherein the deciding step comprises identifying at least one additional spring produced onto the at least one manufactured spring. M106. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S105 wherein the deciding step comprises identifying at least one discoloration in the at least one manufactured spring.

[0284] M107. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S106 wherein the deciding step comprises at least one spring geometry tolerance value.

[0285] M108. The method according to the preceding method embodiment wherein the deciding step comprises identifying at least one defect in the at least one manufactured spring related to the at least one spring geometry tolerance value.

[0286] M109. The method according to any of the preceding method embodiments with the features of any of method embodiments S80, S94-S108 wherein the deciding step comprises at least one coloration data.

[0287] MHO. The method according to the preceding method embodiment, wherein the deciding step comprises identifying at least one defect in the at least one manufactured spring related to the at least one coloration data.

[0288] Mill. The method according to any of the preceding method embodiments with the features of method embodiments S81 or S82 wherein the deciding step comprises identifying if at least one spring is snapped to at least one working plate with at least one fixing pin in the at least one mat.

[0289] Ml 12. The method according to any of the preceding method embodiments with the features of method embodiments S81 or S82 wherein the deciding step comprises identifying if the step of at least one spring relative to at least one pin is uniform in the at least one mat.

[0290] Ml 13. The method according to any of the preceding method embodiments with the features of method embodiments S81 or S82 wherein the deciding step comprises identifying if at least one spring is deformed and / or crushed in the at least one mat.

[0291] Ml 14. The method according to any of the preceding method embodiments with the features of method embodiments S81 or S82 wherein the deciding step comprises identifying if at least one protruding feature of at least one spring can extend out of the at least one spring's general diameter in the at least one mat. M115. The method according to any of the preceding method embodiments with the features of method embodiment M77, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from wood.

[0292] Ml 16. The method according to any of the preceding method embodiments with the features of method embodiment M77, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least a partially wood plant.

[0293] Ml 17. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least bamboo.

[0294] Ml 18. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least willow.

[0295] Ml 19. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least rattan.

[0296] M120. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least reed.

[0297] M121. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least cane.

[0298] M122. The method according to any of the preceding method embodiments with the features of method embodiment Ml 16, wherein the at least one material comprising wood and / or the at least one product comprising wood is substantially made from at least dried palm leaves.

[0299] M123. The method according to any of the preceding method embodiments with the features of method embodiment M77, wherein the at least one material comprising wood and / or the at least one product comprising wood comprise a mass fraction of woody substance amounting to at least 35%, preferably at least 50%, more preferably at least 70% and most preferably at least 90%.

[0300] M124. The method according to any of the preceding method embodiments with the features of method embodiment M80, wherein the at least one material comprising wood and / or the at least one manufactured spring comprise a mass fraction of woody substance amounting to at least 50%, preferably at least 70% and most preferably at least 90%.

[0301] M125. The method according to any of the preceding method embodiments with the features of method embodiment M80, wherein the at least one manufactured spring comprises an outer diameter of 5mm to 20 mm, preferably 8 mm to 15mm, and more preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0302] M126. The method according to any of the preceding method embodiments with the features of method embodiment M80, wherein the at least one manufactured spring comprises a length of 10-5000 mm, preferably 150 mm to 1500 mm, more preferably 200 mm to 1000 mm, and most preferably 200 mm to 900 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position. M127. The method according to any of the preceding method embodiments with the features of method embodiment M80, wherein the at least one manufactured spring's strip comprises a width of 1 mm to 20 mm, preferably 2 mm to 7 mm, more preferably 3 mm to 7 mm, and most preferably 4 mm to 6 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0303] M128. The method according to any of the preceding method embodiments with the features of method embodiment M80, wherein the at least one manufactured spring comprises an outer diameter of 5 mm to 60 mm, preferably 5 mm to 20 mm, more preferably 8 mm to 15 mm, and most preferably 10 mm to 12 mm in a substantially unbiased state, wherein a substantially unbiased state is a state where no external force apart from gravity is applied to the spring and wherein the spring is placed in a substantially horizontal position.

[0304] M129. The method according to any of the preceding method embodiments with the features of method embodiment M78, wherein the method is independent from the dimension of the at least one sheet comprising wood.

[0305] 5130. The system according to any of the previous system embodiments wherein the system comprises a logger module.

[0306] 5131. The system according to the preceding embodiment wherein the logger module comprises instructions which, when executed, cause the system to carry out the method according to any of the preceding method embodiments.

[0307] 5132. The system according to any of preceding system embodiments S130-S131 wherein the logger module comprises logs of all operations performed by the system.

[0308] 5133. The system according to any of preceding system embodiments S130-S132 wherein the logger module comprises logs of all operations induced by the method according to any of the preceding method embodiments.

[0309] S134. The system according to any of preceding system embodiments S130-S133 wherein the logger module comprises logs of all metadata required for assessing the system. S135. The system according to any of preceding system embodiments S130-S134 wherein the logger module comprises logs of all metadata required for evaluating the decider module.

[0310] 5136. The system according any of preceding system embodiments S130-S135 with features of system embodiment S47 and wherein the logger module comprises logs of all metadata required for training the at least one Al module.

[0311] 5137. The system according to any of preceding system embodiments S130-S136 with features of system embodiment S47 wherein the logger module comprises logs of all data required for training the at least one Al module.

[0312] 5138. The system according to any of the previous embodiment wherein the system comprises a data processing system comprising at least one data processing device wherein the data processing system is configured to perform the method according to any of the preceding method embodiments.

[0313] Below, computer-program embodiments will be discussed. These embodiments are abbreviated with the letter C followed by a number. Whenever reference is herein made to system embodiments, these embodiments are meant.

[0314] Cl. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any of the preceding method embodiments.

[0315] Brief Description of the drawings

[0316] The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.

[0317] Fig. 1 schematically depicts a system for a production system configured to manufacture at least one product from at least one material according to embodiments of the present invention;

[0318] Fig. 2 schematically depicts an example of a data acquisition module according to embodiments of the present invention.

[0319] Fig. 3 schematically depicts an example of one of the at least one Al modules of the system according to embodiment of the present invention. Fig. 4 schematically depicts an example of part of the system according to embodiments of the present invention.

[0320] Fig. 5 depicts some examples of manufactured mats that pass through the system according to embodiments of the present invention.

[0321] Fig. 6 depicts some examples of defects in wood sheets identifiable by the system according to embodiments of the present invention.

[0322] Detailed description of the drawings

[0323] In the following description, a series of features and / or steps are described. The skilled person will appreciate that unless explicitly required and / or unless required by context, the order of the features and steps is not critical for the resulting configuration and its effect.

[0324] It is noted that not all the drawings carry all the aspects related to the subject(s) of the figures. Instead, in some of the drawings, some of these aspects have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to accompanying drawings.

[0325] Fig. 1 schematically depicts a system 100 for a production system 200 configured to manufacture at least one product from at least one material. In simple terms, the system 100 comprises a data acquisition module 130, a decider module 150 and a controller module 170.

[0326] In one embodiment, production system 200 is configured to receive operational commands from controller module 170 for either retaining or discarding the product / material 210 being worked on.

[0327] In another embodiment, system 100 can transmit at least one production system heartbeat state and / or at least one system status to production system 200.

[0328] In a further embodiment, the data acquisition module 130 may be configured to acquire data from at least one part of at least one product and / or at least one material 210. In another embodiment, the data acquisition module 130 may comprise a data acquisition device 135. The data may be acquired in real-time as well as in a sequential way. The data acquired is then sent to the decider module 150.

[0329] In another embodiment, the decider module 150 may comprise a preprocessing module 152 and at least one Al module 154. Decider module 150 and all modules comprising it may perform the steps of the method disclosed in the method embodiments. The at least one Al module result may be transmitted to the controller module 170.

[0330] In a further embodiment, the controller module 170 is configured to output the at least one operation command to the production system 200 based on the at least one Al module result. The production system 200 may also transmit at least one system heartbeat state and / or at least one system status state to the controller module 170. The controller module 170 may also transmit at least one system decision and / or at least one defect ID to the production system 200. The controller module 170 may also transmit an acquiring status signal to the decider module 130 wherein the decider module 130 is configured to acquire or stop acquiring data according to the at least one acquiring status signal.

[0331] It should be understood that in some embodiment, any of modules 130, 150 and 170 may be at least partially integrated in a single module. For instance, the decider module 150 and the controller module 170 may be integrated into a single module.

[0332] Fig. 2 schematically depicts an example of a data acquisition module 130 according to embodiments of the present invention.

[0333] In one embodiment, the data acquisition module 130 can comprise a data acquisition device 135 configured for acquiring at least one data sample from at least part of the at least one product and / or material 210. For instance, the data acquisition device 135 may comprise a camera, a sensor etc.

[0334] The data acquisition device 135 is positioned essentially perpendicularly and preferably perpendicularly with respect to the product / material 210 in a manner conducive to acquiring the at least one data sample. In simple terms, the data acquisition device 135 is positioned in a way that allows the centre axis of the data acquisition device 135's coverage area to form essentially a perpendicular and preferably a perpendicular with the facet or the surface of the part of the product / material 210 that is most relevant for the current segment of the quality control process.

[0335] In a further embodiment, the data acquisition module 130 may be configured to acquire or provide a plurality of data samples while preferably ensuring these data samples overlap. Thus, the data acquisition device 135, which is comprised in the data acquisition module, may be configured to move its coverage area to acquire these data samples while ensuring overlap. For instance, the data acquisition device may move along a certain axis while acquiring the data samples falling within its coverage area. In another instance, the product / material may be moved along a certain axis in such a way that the data acquisition device's area of coverage encompasses the product / material.

[0336] The sample data may be acquired in a sequential way and in real-time. This data may be sent in real time to the decider module 150.

[0337] Fig. 3 schematically depicts an example of one of the at least one Al modules 154 of the system according to embodiments of the present invention. In one embodiment, the at least one data sample may have passed through the preprocessing module 152 according to any previous embodiment (not present in this figure). Furthermore, the at least one data sample may be passed through the Al module.

[0338] In another embodiment, the Al module may comprise at least one convolution 1541 and at least one transformer block 1543 to output at least one Al module result 1545. For instance, the at least one Al module result may refer to the detections of defects in the data sample.

[0339] For example, at least one data sample relating to a wood sheet, a wooden spring or a wooden mat in production may be passed through the Al module which would then output at least one Al module result indicative of the quality of the products and materials. These results may reflect the defects present in the wood sheet, the wooden spring or the wooden mat.

[0340] Fig 4. schematically depicts an example of part of the system 100 according to embodiments of the present invention. To be more precise, Fig. 4 schematically depicts at least some of the communication channels that may be present between the data acquisition device 130, the decider 150, the controller 170, a logger 180 and a display device 190.

[0341] In one embodiment, the logger 180 is configured to store logs of all operations performed by the system including but not limited to logs of all the operations performed by the data acquisition system 130, the decider 150 and the controller 170, logs of all metadata required for assessing the system and thus its modules, logs of all metadata required for evaluating the decider module, logs of all metadata and data required for training the at least one Al module 154 (not shown in this figure).

[0342] In simple terms, the decider 130 may transmit metadata and data to the logger 180, the decider 150 may transmit metadata required for its evaluation to logger 180. The logger 180 may also transmit all the metadata and data required for training the at least one Al module 154 to the decider module. The controller 170 may also transmit all logs of all operations it performed to the logger 180. The logger may also transmit all metadata required to the controller for assessing the system.

[0343] In a further embodiment, the controller may transmit all important data to a display device 190.

[0344] It should be understood that in some embodiment, any of modules 130, 150, 170, 180 and 190 may be at least partially integrated in a single module. For instance, logger 180, decider module 150 and controller module 170 may be integrated into a single module. Another example may be, logger 180 being located on a server and being accessed by a module that comprises the data acquisition module 130, decider module 150 and controller module 170. Display device 190 may also be integrated in this single module, or may be a part of a separate module.

[0345] Fig 5. depicts some examples of manufactured mats of varying sizes that pass through the system according to embodiments of the present invention. The production of these mats is dictated by the system during and after the manufacturing process.

[0346] Fig 6. depicts some examples of multiple defects in wood sheets identifiable by the system according to embodiments of the present invention. Fig. 6a and Fig. 6b depict examples of loss of materials in a wood sheet. Fig. 6c depicts an example of undesirable edge in a wood sheet.

[0347] While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.

[0348] Whenever a relative term, such as "about", "substantially" or "approximately" is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., "substantially straight" should be construed to also include "(exactly) straight".

[0349] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z). Corresponding considerations apply when terms like "after" or "before" are used.

Claims

Claims1. A system for a production system configured to manufacture at least one product comprising wood from at least one material comprising wood, the system comprising: a data acquisition module configured to acquire at least one data sample from the at least one product and / or the at least one material, a decider module configured to process of the at least one data sample related to the at least one product and / or the at least one material resulting in at least one decider module result, a controller module configured to provide operational commands to the production system according to the at least one decider module result.

2. The system according to the preceding claim wherein the system is configured to perform quality control on the at least one material and / or the at least one product and / or at least one material while being manufactured into the at least one product.

3. The system according to any of the preceding claims wherein the production system is configured to discard or retain the at least one material and / or the at least one product being manufactured according to the operational commands provided by the controller module.

4. The system according to any of the preceding claims wherein the data acquisition module is configured to acquire data from at least one part of the at least one product and / or at least one part of the at least one material in real-time.

5. The system according to any of the preceding claims wherein a data acquisition module configured to acquire or provide a plurality of data samples and wherein the data acquisition module is configured to ensure overlap between the data samples.

6. The system according to any of the preceding claims wherein the data acquisition module comprises at least one data acquisition device wherein the at least one data acquisition device is positioned relative to the production system, essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material, in a manner conducive to acquiring the at least one data sample from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material, in real-time.

7. The system according to the preceding claim wherein the at least one data acquisition device is configured to move its coverage area to acquire a plurality of data samples.

8. The system according to any of the preceding system claims wherein the decider comprises at least one Al module wherein the at least one Al module is configured to execute at least one sequence modelling algorithm wherein the at least one sequence modelling algorithm is configured to execute at least one Al algorithm that makes use of any kind of working and / or short-term memory.

9. The system according to any of the preceding claims wherein the controller module comprises at least one probability threshold module wherein the at least one probability threshold module comprises at least one probability threshold wherein the at least one probability threshold is adjustable.

10. The system according to any of the preceding claims wherein the at least one decider module result is indicative of the quality of at least one wood-based material / at least one wood-based product.

11. A method for a production system configured to manufacture at least one product comprising wood from at least one material comprising wood, the method comprising: an acquiring step, wherein the acquiring step comprises acquiring at least one data sample from the at least one product and / or the at least one material, a deciding step wherein the deciding step comprises processing the at least one data sample related to the at least one product and / or the at least one material resulting in at least one deciding step result, and a controlling step, wherein the controlling step comprises providing operational commands to the production system according to the at least one deciding step result.

12. The method according to the preceding method claim wherein the method comprises performing quality control on the at least one material and / or the at least one product.

13. The method according to any of the preceding method claims wherein the production system is configured to discard or retain the at least one material and / orthe at least one product being manufactured according to the operational commands generated by the controlling step.

14. The method according to any of the preceding method claims wherein the acquiring step comprises acquiring data from at least one part of the at least one product and / or at least one part of the at least one material in real-time.

15. The method according to any of the preceding method claims wherein the acquiring step comprises acquiring or providing a plurality of data samples and wherein the acquiring step comprises ensuring overlap between the data samples.

16. The method according to any of the preceding method claims wherein acquiring step comprises acquiring the at least one data sample provided from at least one data acquisition device, wherein the at least one data acquisition device is positioned relative to the production system, essentially perpendicularly and preferably perpendicularly with respect to the at least one product and / or at least one material, in a manner conducive to acquiring the at least one data sample from at least one part of the at least one product and / or at least one part of the at least one material in a sequential way, preferably a longitudinal way with respect to the at least one product and / or at least one material, in real-time.

17. The method according to the preceding claim wherein the at least one data acquisition device is configured to move its coverage area to acquire a plurality of data samples.

18. The method according to any of the preceding method claims wherein the deciding step comprises executing at least one sequence modelling algorithm wherein the at least one sequence modelling algorithm comprises executing at least one Al algorithm that makes use of any kind of working and / or short-term memory.

19. The method according to any of the preceding method claims wherein the controlling step comprises accessing at least one probability threshold module wherein the at least one probability threshold module comprises at least one probability threshold wherein the method comprises adjusting the at least one probability threshold.

20. The method according to any of the preceding method claims wherein the at least one deciding step result is indicative of the quality of at least one wood-based material / at least one wood-based product.

21. The system according to any of the previous system claims wherein the system comprises a logger module wherein the logger module comprises instructions which, when executed, cause the system to carry out the method according to any of the preceding method claims.

22. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any of the preceding method claims.

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