Digitized additive manufacturing workshop implementation method

Abstract

The invention relates to a digital additive manufacturing workshop implementation method and system, belongs to the technical field of additive manufacturing, and solves the problems of low production efficiency and high production cost of an additive manufacturing workshop in the prior art. The method comprises the steps that a plurality of external orders are received and analyzed, order information of each order is obtained, a production task is formulated based on the order information of the orders, and the order information comprises delivery time, process model geometric data and material types; distributing the production task to target single-machine equipment according to the equipment states of a plurality of single-machine equipment in a workshop; controlling the target single machine equipment to execute the distributed production task; in the execution process of the production task, equipment operation parameters of the target single-machine equipment are collected in real time, and product quality data are collected according to a preset production cycle; and the production task is monitored in combination with the equipment operation parameters and the product quality data, a monitoring result is sent to a preset terminal in real time, and an alarm is given when the monitoring result is abnormal.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and in particular to a method for realizing a digital additive manufacturing workshop. Background Technology

[0002] Additive manufacturing technology has been widely used in aerospace, medical devices, mold manufacturing and other fields due to its advantages in the rapid prototyping of complex structural parts. With the diversification of market demand, a single workshop often needs to handle orders for multiple models and specifications of products at the same time, which puts forward higher requirements for the flexibility, efficiency and cost control of production organization.

[0003] Traditional additive manufacturing workshops typically employ a single production line or fixed production line scheduling. When faced with variable-batch orders involving multiple product specifications, this model struggles to flexibly allocate resources across multiple machines for collaborative production. Task allocation relies heavily on manual experience, failing to dynamically optimize based on real-time equipment status, order urgency, and geometric compatibility between models. Furthermore, equipment parameters, quality data, and cost information are isolated during production, lacking traceability across the entire process from order placement and scheduling to printing, post-processing, and quality inspection. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a method for implementing a digital additive manufacturing workshop, in order to solve the problems of low production efficiency and high production costs in existing additive manufacturing workshops.

[0005] In a first aspect, embodiments of the present invention provide a method for implementing a digital additive manufacturing workshop, comprising:

[0006] Receive and parse multiple external orders to obtain order information for each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data, and material type.

[0007] Based on the equipment status of multiple individual machines in the workshop, the production tasks are assigned to the target individual machines;

[0008] Control the target single-machine equipment to execute the assigned production tasks;

[0009] During the execution of production tasks, the equipment operating parameters of the target single equipment are collected in real time, and product quality data are collected according to the preset production cycle.

[0010] By combining equipment operating parameters and product quality data to monitor production tasks, the monitoring results are sent to preset terminals in real time, and an alarm is triggered when the monitoring results are abnormal.

[0011] Furthermore, formulating production tasks based on order information from multiple orders includes:

[0012] Based on the geometric data and material types of the process models of multiple orders, the process models of multiple different orders are laid out to generate N layout schemes, and estimated cost information is generated for each layout scheme, where N≥1. The layout schemes include merged layout schemes and individual layout schemes.

[0013] Production tasks are formulated based on the aforementioned layout scheme.

[0014] Furthermore, the layout of process models for multiple different orders includes:

[0015] M orders with the same material type and whose process model geometry data meet the preset compatibility conditions are grouped into the same consolidated order batch, where M≥2;

[0016] Treat each order that does not belong to any of the combined order batches as a separate batch;

[0017] For each consolidated order batch, a consolidated layout scheme is generated based on the delivery time and process model geometry data of all orders in that batch, according to the predetermined consolidated order strategy.

[0018] For each individual batch, a separate layout scheme is generated based on the order's delivery time and process model geometry data.

[0019] Furthermore, generating a merged layout scheme according to the predetermined order merging strategy includes:

[0020] The production urgency of a consolidated order is determined based on the delivery times of all orders within that consolidated order.

[0021] Calculate the dimensional matching degree of the consolidated batch based on the geometric data of the process models of all orders within the consolidated batch;

[0022] Based on the production urgency and the size matching degree, the corresponding order calculation method is selected to generate a merged layout scheme.

[0023] Furthermore, the process of generating a merged layout scheme by selecting the corresponding order calculation method based on the production urgency and the size matching degree includes:

[0024] When the production urgency is higher than the preset urgency threshold, select the first merge calculation method to generate a merged layout scheme;

[0025] When the production urgency is not higher than the preset urgency threshold and the size matching degree is higher than the preset matching threshold, select the second order calculation method to generate a merged layout scheme.

[0026] When the production urgency is not higher than the preset urgency threshold and the size matching degree is not higher than the preset matching threshold, the third merge calculation method is selected to generate a merged layout scheme.

[0027] Furthermore, the calculation method for the first consolidated order is as follows:

[0028] Calculate the minimum package cube of the process model for each order in the consolidated batch; based on the minimum package cube size of each model, arrange the minimum package cubes of each model on the same substrate plane, determine the position and printing orientation of each process model on the substrate, and thus generate a consolidated layout scheme.

[0029] The second consolidated order calculation method is as follows:

[0030] Within a preset set of rotation angles, adjust the printing orientation of the process model for each order in the consolidated batch and determine the spatial relative position of each process model to minimize the projected outer envelope area of ​​all models on the substrate plane; based on the determined printing orientation and spatial relative position of each process model, calculate the overall minimum enveloping cube of the process models for all orders in the consolidated batch; based on the minimum enveloping cube, determine the position of each model on the substrate, thereby generating a consolidated layout scheme;

[0031] Furthermore, the calculation method for the third consolidated order is as follows:

[0032] Based on the geometric data of the process models of each order in the consolidated batch, the process models of each order in the consolidated batch are divided into multiple model subsets, where the geometric feature similarity of each process model within the same model subset is higher than the preset similarity threshold.

[0033] For each subset of models, perform the following operations: Adjust the printing orientation of each process model within the subset within a preset set of rotation angles, and determine the spatial relative position of each process model to minimize the projected outer envelope area of ​​all models within the subset on the substrate plane; treat each subset as a whole and calculate the overall minimum enveloping cube of each subset; arrange the overall minimum enveloping cubes of each subset onto the same substrate based on the size of the overall minimum enveloping cube of each subset; determine the position and printing orientation of each process model within each subset on the substrate to generate a merged layout scheme.

[0034] Furthermore, assigning the production task to the target single machine based on the equipment status of multiple single machines in the workshop includes:

[0035] Obtain the current status information of each individual device, including the current task load, device health status, maximum forming area, and remaining powder amount;

[0036] Based on the production task, the maximum forming area of ​​each single machine, and the amount of remaining powder, all candidate single machines that meet the production task requirements are screened out.

[0037] According to the predetermined allocation rules, a target single-machine device is selected from the candidate single-machine devices. The allocation rules include giving priority to the device with the lightest current task load and normal device status.

[0038] Furthermore, the abnormal monitoring results include quality abnormalities and cost abnormalities; the monitoring of production tasks by combining equipment operating parameters and product quality data includes:

[0039] The product quality data collected according to the preset cycle will be compared with the corresponding product quality standard tolerance range. If the product quality data exceeds the quality standard tolerance range, it will be determined that there is a quality abnormality.

[0040] Based on real-time collected equipment operating parameters, the actual execution cost of the production task is calculated; the actual execution cost is compared with the estimated cost information of the layout scheme corresponding to the production task; if the deviation between the actual execution cost and the estimated cost exceeds the allowable range, it is determined that there is a cost anomaly.

[0041] Secondly, embodiments of the present invention provide a digital additive manufacturing workshop implementation system, the system comprising:

[0042] The parsing module is used to receive and parse multiple external orders, obtain the order information of each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data, and material type.

[0043] The allocation module is used to allocate the production task to the target single machine based on the equipment status of multiple single machines in the workshop;

[0044] The execution module is used to control the target single-machine device to execute the assigned production tasks;

[0045] The data acquisition module is used to collect the equipment operating parameters of the target single machine in real time during the execution of production tasks, and to collect product quality data according to the preset production cycle.

[0046] The monitoring module is used to monitor production tasks by combining equipment operating parameters and product quality data, send the monitoring results to the preset terminal in real time, and issue an alarm when the monitoring results are abnormal.

[0047] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0048] 1. M orders with the same material type and process model geometric data that meet the preset compatibility conditions are divided into the same batch of merged orders. The corresponding merged order calculation method is selected to generate a merged layout scheme based on the production urgency and size matching degree of the merged order batch. The geometric matching between the order attributes and the model is evaluated, and the first, second or third merged order calculation method is selected to generate the optimal layout scheme. In this way, multiple order tasks are arranged on the same printing substrate, maximizing equipment utilization and single printing output, and improving the production efficiency of the additive manufacturing workshop.

[0049] 2. By generating estimated cost information for each layout scheme during the production scheduling stage, and calculating the actual execution cost of the production task based on the real-time collected equipment operating parameters after production execution, the actual execution cost is compared with the estimated cost information to monitor cost anomalies, including furnace printing cost and post-processing cost. The cost allocation is also corrected by incorporating historical printing success rate, thus solving the problem of high production costs.

[0050] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0051] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0052] Figure 1 This is a schematic diagram of a method for implementing a digital additive manufacturing workshop according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the structure of a digital additive manufacturing workshop system provided in an embodiment of the present invention. Detailed Implementation

[0054] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0055] Example 1

[0056] like Figure 1 As shown, a method for implementing a digital additive manufacturing workshop includes:

[0057] S1. Receive and parse multiple external orders to obtain the order information of each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data and material type.

[0058] In the additive manufacturing (3D printing) workshop of this invention, a deployed shop floor manufacturing execution system (MES) interfaces with an upstream enterprise resource planning (ERP) system or customer order management platform. When an external order is placed, the MES system receives the order data packet in real time via a Web Service API or message queue, such as RabbitMQ or Kafka. An order refers to a production request from a customer that includes one or more specific product requirements. Each order data packet is structured data, such as JSON or XML format, which is parsed to extract order information. Order information includes delivery time, process model geometry data, and material type.

[0059] The customer's requested final product delivery date or time; the process model geometric data refers to the triangular facets and bounding boxes obtained by parsing STL files, typically 3D model files in formats such as STL, STEP, or 3MF, which contain geometric information such as the product's shape and dimensions; the material type refers to the product's material, such as specific grades of metal powder or photosensitive resin, like 316L stainless steel, TC4 titanium alloy, or AlSi10Mg aluminum alloy.

[0060] Creating production tasks based on order information from multiple orders includes:

[0061] Based on the geometric data and material types of the process models for multiple orders, the process models for multiple different orders are laid out to generate N layout schemes, and estimated cost information is generated for each layout scheme, where N≥1. The layout schemes include merged layout schemes and individual layout schemes; production tasks are formulated based on the layout schemes.

[0062] Production task formulation is a process of clustering, arranging, and optimizing solutions for orders based on the order information received from multiple orders. A production task can be defined as a set of instructions, including which specific single machine to use, what materials to use, what layout scheme to follow, which specific process models to print, and is associated with the estimated cost and planned time.

[0063] Furthermore, the layout of process models for multiple different orders includes:

[0064] S11. Divide M orders with the same material type and process model geometric data that meet the preset compatibility conditions into the same batch of orders, where M≥2; the preset compatibility conditions refer to whether the sum of the dimensions of the outer cuboids calculated from the process model geometric data of the two models is less than the maximum forming area of ​​the equipment and a safety gap is left; or whether the expected printing heights of the models are significantly different.

[0065] Significant differences in print height can impact print time. If one model is significantly taller than the others, its print time will dominate the entire batch, causing printing equipment resources for lower models to be idle for most of the time. A significant height difference can be determined by setting a relative ratio followed by an absolute difference. For example, a height ratio exceeding 2:1 between the tallest and shortest models is considered a significant difference, or a difference exceeding 150mm is also considered significant. Orders with the same material type and meeting preset compatibility conditions should be grouped into a single batch for combined printing.

[0066] If multiple order models using the same material do not have significantly different printing heights, determine whether the sum of the outer bounding box dimensions calculated from the process model's geometric data is less than the maximum forming area of ​​the equipment and leaves a safety margin. For two or more models to be evaluated, create a 2D plane corresponding to the substrate in software such as Materialise Magics, 3D Systems 3DXpert, or EOSPRECIOUS AM. Align the bounding boxes of the models to be evaluated with their axes in the XY plane and arrange them tightly along the X-axis for initial layout. During layout, insert a preset safety margin between adjacent bounding boxes, typically between 5 mm and 20 mm. Calculate the overall outer bounding dimensions in the X and Y directions, including the distance from the leftmost to the rightmost point as the total length and the distance from the frontmost to the backmost point as the total width. If neither the total length nor the total width exceeds the maximum forming area, then all orders corresponding to the models to be evaluated are grouped into the same consolidated batch. If the total length or total width exceeds the maximum forming area, the order corresponding to the model with the largest geometric size will be treated as a separate batch. The sum of the dimensions of the remaining models will be calculated to see if it is less than the maximum forming area of ​​the equipment. If so, the orders corresponding to the remaining models will be treated as a combined batch.

[0067] S12. Treat each order that does not belong to any merged batch as a separate batch; treat each order that cannot be merged with other orders as a separate batch, and arrange separate printing layout for its model directly for the separate batch.

[0068] S13. For each consolidated order batch, generate a consolidated layout scheme according to the delivery time and process model geometry data of all orders in the batch, based on the predetermined consolidated order strategy; for each individual batch, generate an individual layout scheme according to the delivery time and process model geometry data of that order.

[0069] Furthermore, generating a merged layout scheme according to the predetermined order merging strategy includes:

[0070] S131. Determine the production urgency of a consolidated order batch based on the delivery times of all orders within that batch. First, set a planned production baseline time for each consolidated order batch. Calculate the difference between the delivery time of each order within the batch and this baseline time to obtain the remaining time margin for each order. Select the minimum remaining time margin among all orders in the batch as the most urgent time margin for that batch. Convert the most urgent time margin into a production urgency score. Using a predefined mapping function, map the time margin to a range of 0 to 100. The shorter the margin, the higher the score and the higher the urgency. For example, if the most urgent order in a batch needs to be delivered within 24 hours, its production urgency score might be 95; if the most urgent delivery time is one month later, the production urgency score might be 30.

[0071] S132. Calculate the dimensional matching degree of the consolidated order batch based on the geometric data of the process models of all orders within the consolidated order batch. The dimensional matching degree is calculated using the geometric data of the process models of all orders within the consolidated order batch, without considering specific placement positions. Calculate the overall axis-aligned bounding box that can completely enclose all models within the batch, and record its volume as V1. Calculate the sum of the actual volumes of all model entities within the batch, V2. The dimensional matching degree is the ratio of V2 to V1. The dimensional matching degree reflects the maximum theoretical proportion that the consolidated order batch can fill a standard space under ideal conditions. The closer the ratio is to 1, the more compact the model combination, the better the matching of size and shape, the less space wasted, and the higher the matching degree. For example, if the calculated ratio is 0.65, it means that the space filling rate of the models in this consolidated order batch is 65%, and the matching degree is relatively high; if the ratio is only 0.25, it means that the matching degree is low, and the economic efficiency of consolidated printing is poor.

[0072] S133. Based on the production urgency and the size matching degree, select the corresponding order calculation method to generate a merged layout scheme, including:

[0073] When the production urgency level exceeds the preset urgency threshold, the first order consolidation calculation method is selected to generate a merged layout scheme. The preset urgency threshold can be set based on experience by analyzing historical orders, such as setting it to 75. If the production urgency level exceeds the preset urgency threshold, the first order consolidation calculation method is selected to generate a merged layout scheme, including:

[0074] Calculate the minimum package cube of the process model for each order in the consolidated batch; based on the minimum package cube size of each model, arrange the minimum package cubes of each model on the same substrate plane, determine the position and printing orientation of each process model on the substrate, and thus generate a consolidated layout scheme.

[0075] The first batch consolidation calculation method prioritizes production speed, independently calculating the minimum wrapping cube for each process model in the batch. The minimum wrapping cube is a cuboid with all sides parallel to the machine coordinate system axes, capable of completely enclosing all geometric features of the model, and its volume is the minimum possible value for that orientation. After obtaining the cube for each model, the cube of the first model is placed at the bottom left corner of the virtual substrate. Then, along the positive X-axis, the cubes of subsequent models are arranged closely together, with only a fixed safety gap between each cube. During the arrangement process, the models maintain their default printing orientation when imported, or only try a few stable orientations. When all cubes are placed on the substrate plane, the position of each cube determines the coordinates of its internal model on the substrate, thus quickly generating a consolidation layout scheme. The first batch consolidation calculation method has low complexity. It allows for rapid production scheduling and is suitable for scenarios with extremely high production urgency.

[0076] When the production urgency is not higher than the preset urgency threshold and the size matching degree is higher than the preset matching threshold, the second order merging calculation method is selected to generate a merged layout scheme; the preset matching threshold is set based on the statistical analysis of the space utilization rate of historical successful order merging cases, such as 0.5.

[0077] The second consolidated order calculation method is as follows:

[0078] Within a preset set of rotation angles, adjust the printing orientation of the process model for each order in the consolidated batch and determine the spatial relative position of each process model to minimize the projected outer envelope area of ​​all models on the substrate plane; based on the determined printing orientation and spatial relative position of each process model, calculate the overall minimum enveloping cube of the process models for all orders in the consolidated batch; based on the minimum enveloping cube, determine the position of each model on the substrate, thereby generating a consolidated layout scheme;

[0079] The second batch printing calculation method aims to maximize the utilization of printing space per print run. By optimizing the spatial pose of multiple models, it ensures that all models within a batch are embedded into the printing space as a whole, reducing the material and equipment costs per unit product. During implementation, within a preset set of rotation angles, for example, allowing the model to rotate once every 90 degrees around the Z-axis (4 orientations), different printing orientations can be tried for each process model; alternatively, it can rotate once every 45 degrees (8 orientations).

[0080] Metaheuristic optimization algorithms, such as genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms, are used to search for the optimal relative positions of the models in 3D space. The optimization objective is to minimize the projected envelope area of ​​all models on the substrate plane, i.e., to find a rectangle with the smallest area that can completely cover the projections of all models onto the substrate in their current poses. Through iterative optimization, a set of model orientation and position combinations that minimize the overall projected area is eventually converged. Based on this optimal set of poses, the minimum overall envelope cube that encloses all these models is calculated, and the precise coordinates of each model in the device coordinate system are calculated accordingly to generate a layout scheme. This method is computationally time-consuming but can significantly improve material utilization and single-furnace output, making it suitable for production batches with high model size matching and low time urgency.

[0081] When the production urgency is not higher than the preset urgency threshold and the size matching degree is not higher than the preset matching threshold, the third merge calculation method is selected to generate a merged layout scheme.

[0082] The third method of single-unit calculation reduces the complexity of the single optimization problem by grouping, and approximates the theoretical overall cost optimum in both intra-group and inter-group aspects.

[0083] Based on the geometric data of the process models of each order in the consolidated batch, the process models of each order in the consolidated batch are divided into multiple model subsets, where the geometric feature similarity of each process model within the same model subset is higher than the preset similarity threshold.

[0084] For each subset of models, perform the following operations: Adjust the printing orientation of each process model within the subset within a preset set of rotation angles, and determine the spatial relative position of each process model to minimize the projected outer envelope area of ​​all models within the subset on the substrate plane; treat each subset as a whole and calculate the overall minimum enveloping cube of each subset; arrange the overall minimum enveloping cubes of each subset onto the same substrate based on the size of the overall minimum enveloping cube of each subset; determine the position and printing orientation of each process model within each subset on the substrate to generate a merged layout scheme.

[0085] Based on the geometric data of the process models of each model in the batch, clustering algorithms, such as the K-means algorithm, are used to divide all models into several model subsets with the model size and aspect ratio as feature vectors. This ensures that the geometric feature similarity of models in the same subset is higher than a preset similarity threshold, that is, models with similar shapes and sizes are grouped into one model subset.

[0086] Similar to the second calculation method, iterative optimization is performed independently on each model subset to obtain the optimal relative position and orientation of the models within that subset. The subset is then treated as a whole to calculate the overall minimum wrapping cube. The minimum wrapping cube of each model subset is used as a new object to be arranged. Using the same method as the first merge calculation, the objects to be arranged are placed on the same substrate to determine the position of each model subset on the substrate. Based on this, the final specific position and orientation of each model within each module are calculated, thus generating the final merged layout scheme. The third calculation method ensures local optima through optimization within groups and performs rapid geometric arrangement between groups. It is suitable for regular order batches seeking the optimal overall printing and post-processing costs.

[0087] As an example, when receiving a batch of urgently needed engineering support orders, all of which are made of aluminum alloy, the production urgency is determined to be high. Using the first batch calculation method, the extreme values ​​of the coordinates of all vertices in the X, Y, and Z directions are calculated by parsing the 3D model file of each support, thus determining the minimum outer envelope cuboid that can completely enclose the model with all sides parallel to the coordinate axes. A bottom-left corner priority nesting algorithm is used, placing the cuboid of the first support at the bottom-left corner origin of the virtual construction plane. Subsequent cuboids are arranged sequentially along the right edge of the previous one, maintaining a preset 8mm process interval, until all cuboids are arranged on the 500mm x 500mm construction plane of the equipment. All supports retain their original design orientation, and the printing coordinates of each support are directly output based on the position of each cuboid, thus generating a production plan that can be directly issued and executed.

[0088] When processing orders for medical implants with complex shapes but flexible delivery cycles, a second order-matching method is employed to ensure high dimensional matching. This involves calculating the minimum outer envelope cuboid for each implant model and allowing each implant to rotate around the Z-axis in 15-degree increments. A simulated annealing optimization algorithm is used for iterative search, randomly generating initial combinations of implant orientations and positions, and calculating the total area of ​​their projected contours on the substrate. During the iteration process, the initial temperature is set to 1000℃, and the cooling coefficient is 0.95. After several preset iterations, a pose combination that minimizes the total projected area is converged as the final pose. Based on the final pose, the precise placement and rotation angle of each implant on the substrate are calculated, generating a layout scheme with optimal space utilization.

[0089] When receiving orders for automotive engine parts containing multiple specifications, a third-party consolidated order calculation method is employed. The minimum outer envelope cuboid size of each part is extracted as a feature, and a K-means clustering algorithm is used to automatically divide all parts into six groups with similar geometric dimensions. For each group, a genetic algorithm is used, employing selection, crossover, and mutation operations to iteratively find the relative positions and orientations that maximize the compactness of the projected contours of all parts within that group. After optimization of each group, all parts within that group are considered as a single unit, and the minimum outer envelope cuboid of this unit is calculated. A fast nesting algorithm is used to arrange the cuboids representing different groups in the construction space, thereby determining the position of each group on the substrate, and from this, the final printing coordinates and orientation of each part within the group are derived to form the production plan.

[0090] S2. Based on the equipment status of multiple individual machines in the workshop, the production task is assigned to the target individual machine;

[0091] Each individual machine in the workshop is an independent 3D printer, such as laser powder bed melting equipment and electron beam melting equipment. It is connected to the Internet of Things (IoT) platform via the network to report its equipment status in real time.

[0092] Furthermore, it includes: acquiring the current status information of each individual device, wherein the current status information includes the current task load, device health status, maximum forming area, and remaining powder amount;

[0093] The current task load includes the progress of the currently executing task and the number of tasks in the queue. Equipment health status displays equipment fault information. The maximum forming area refers to the maximum three-dimensional dimension of the effective space inside the build cylinder or forming chamber of a single machine, allowing material to be deposited layer by layer to form a solid part, including length, width, and height. The remaining powder quantity is obtained through a hopper weighing sensor or flow meter.

[0094] Based on the production task, the maximum forming area of ​​each single machine, and the amount of remaining powder, all candidate single machines that meet the production task requirements are screened out.

[0095] Based on the material type specified in the task, screen all equipment that contains that material in its hoppers. Calculate the maximum length, width, and height dimensions based on the layout scheme, and select equipment whose maximum forming area completely accommodates the scheme, thus obtaining all candidate single-machine equipment.

[0096] According to the predetermined allocation rules, a target single-machine device is selected from the candidate single-machine devices. The allocation rules include giving priority to the device with the lightest current task load and normal device status.

[0097] The allocation rule prioritizes the device with the lightest current task load and normal device status. The task load is the length of the task queue to be executed. If there is no idle single device, the device with the shortest queue length and normal status is selected as the target single device.

[0098] S3. Control the target single-machine equipment to execute the assigned production tasks;

[0099] After the task is assigned, the target standalone device receives the production task instruction package from the upper-level system. The instruction package contains printing codes, such as CLI or custom G codes, after slicing and process parameter configuration. After the corresponding substrate and materials are prepared, the product is printed according to the predetermined path and parameters at the scheduled production time, thus executing the assigned production task.

[0100] S4. During the execution of production tasks, the equipment operating parameters of the target single equipment are collected in real time, and product quality data are collected according to the preset production cycle.

[0101] During the printing process, various sensors installed on the standalone equipment collect the equipment's operating parameters in real time. These parameters include substrate temperature, squeegee speed, oxygen content in the forming cavity, laser power, powder usage, and printing height.

[0102] Specifically, substrate temperature refers to the real-time temperature of the printed substrate, measured by a thermocouple or resistance temperature detector embedded inside the substrate or in close contact with its back surface. Squeegee speed refers to the linear velocity of the squeegee or powder-spreading roller moving horizontally when laying each layer of powder. The squeegee is typically driven by a servo motor, with the actual rotational speed feedback signal provided by a servo driver. Forming cavity oxygen content refers to the percentage of oxygen volume concentration in the protective gas (usually argon or nitrogen) within the sealed forming cavity of the equipment, monitored by a laser oxygen analyzer or electrochemical oxygen sensor. Laser power refers to the actual output power of the processing laser, measured by an external beam diagnostic instrument or power meter probe embedded in the optical path. Powder usage refers to the weight or volume of specific material powder consumed to complete the current production task, measured by weighing or volumetric measurement. Printing height refers to the Z-axis height coordinate corresponding to the completed printed layer during the layer-by-layer manufacturing process.

[0103] Each completed production task (i.e., one printing batch) constitutes a pre-defined production cycle, and the product quality data consists of the product's critical geometric dimensions. These critical dimensions can be measured using a coordinate measuring machine (CMM) and compared against pre-defined or production task-specified tolerance ranges. The measured product dimensions must be determined according to the parameters within the quality standard tolerance range and correspond to that range.

[0104] S5. Monitor production tasks by combining equipment operating parameters and product quality data, send the monitoring results to a preset terminal in real time, and issue an alarm when the monitoring results are abnormal. The abnormal monitoring results include quality abnormalities and cost abnormalities; the monitoring of production tasks by combining equipment operating parameters and product quality data includes:

[0105] The product quality data collected according to a preset cycle will be compared with the corresponding product quality standard tolerance range. If the product quality data exceeds the quality standard tolerance range, it will be determined that there is a quality abnormality and an alarm will be triggered; if the measured value falls within the tolerance range, it will be determined as qualified.

[0106] Based on real-time collected equipment operating parameters, the actual execution cost of the production task is calculated; the actual execution cost is compared with the estimated cost information of the layout scheme corresponding to the production task; if the deviation between the actual execution cost and the estimated cost exceeds the allowable range, it is determined that there is a cost anomaly.

[0107] The actual execution cost includes furnace printing cost and post-processing cost. Calculating furnace printing cost involves calculating the total actual cost of the print run based on the actual substrate area occupied, actual printing height, actual material consumption, and actual printing time recorded during the production task execution, combined with the unit cost parameters corresponding to the material type. This total cost is then allocated to each process model based on the effective volume ratio of each process model entity and the ineffective volume ratio of the reserved safety area between models. Calculating post-processing cost involves calculating the actual post-processing cost based on the wire cutting area and grinding removal volume actually experienced by each process model, combined with the corresponding unit process cost, and allocating this cost to each process model. The calculation formula for furnace printing cost is shown below.

[0108] C print =W×P m +T×R e ;

[0109] Where T is the actual printing time, and R is the actual printing time. e The equipment's comprehensive hourly rate is given, where W represents the actual weight of materials consumed, and P represents the total hourly rate. m This is the unit price of the material.

[0110] The post-processing cost is calculated as shown in the following formula;

[0111] C post =L×R c +V p ×R p ;

[0112] L is the actual cutting length of the product, V p R represents the actual polishing volume. cR represents the unit wire EDM cost rate, which is the processing cost per unit length cut when the product is cut and separated from the printed circuit board; p The unit grinding actual cost rate is the processing cost per unit volume removed when performing post-processing such as surface grinding, polishing, or support removal on a product.

[0113] The estimated cost information is shown in the following formula;

[0114] C plan =W plan ×P m +T plan ×R e ;

[0115] W plan =(∑V i ×ρ) / η;

[0116] Among them, C plan To estimate cost information, V i Let T be the volume of the i-th printed model, ρ be the material density, and η be the material utilization coefficient, determined based on the support structure and splash loss, where η < 1; plan To determine the theoretical printing time, first calculate the ratio of the maximum printing height to the standard layer thickness of the layout scheme to obtain the total number of layers. Then, calculate the theoretical printing time based on the average scanning time of each layer.

[0117] The deviation of the actual execution cost from the estimated cost is shown in the following formula;

[0118] ε=((C print +C post )-C plan ) / C plan ;

[0119] Here, ε represents the deviation between the actual execution cost and the estimated cost. If ε is greater than a preset deviation threshold, an anomaly in cost is determined. The preset deviation threshold can be set according to actual production needs, such as 10%.

[0120] Preset terminals can include large digital display systems in the workshop, industrial computers or touchscreens integrated into the control consoles and equipment operation panels of each production line, and mobile devices of designated personnel. When monitoring results are abnormal, alarms are sent to the preset terminals, including audible and visual alarms, visual highlighting, and message push notifications.

[0121] Example 2

[0122] A digital additive manufacturing workshop implementation system, the system comprising:

[0123] The parsing module is used to receive and parse multiple external orders, obtain the order information of each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data, and material type.

[0124] The allocation module is used to allocate the production task to the target single machine based on the equipment status of multiple single machines in the workshop;

[0125] The execution module is used to control the target single-machine device to execute the assigned production tasks;

[0126] The data acquisition module is used to collect the equipment operating parameters of the target single machine in real time during the execution of production tasks, and to collect product quality data according to the preset production cycle.

[0127] The monitoring module is used to monitor production tasks by combining equipment operating parameters and product quality data, send the monitoring results to the preset terminal in real time, and issue an alarm when the monitoring results are abnormal.

[0128] It is understandable that this digital additive manufacturing workshop implements the modules recorded in the system and references. Figure 1 The steps described in the digital additive manufacturing workshop implementation method correspond to each other. Therefore, the operations, characteristics, and beneficial effects described above for the digital additive manufacturing workshop implementation method also apply to the digital additive manufacturing workshop implementation system and its included modules, and will not be repeated here.

[0129] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0130] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for implementing a digital additive manufacturing workshop, characterized in that, include: Receive and parse multiple external orders to obtain order information for each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data, and material type. Based on the equipment status of multiple individual machines in the workshop, the production tasks are assigned to the target individual machines; Control the target single-machine equipment to execute the assigned production tasks; During the execution of production tasks, the equipment operating parameters of the target single equipment are collected in real time, and product quality data are collected according to the preset production cycle. The system monitors production tasks by combining equipment operating parameters and product quality data, sends the monitoring results to preset terminals in real time, and issues alarms when the monitoring results are abnormal.

2. The method according to claim 1, characterized in that, Creating production tasks based on order information from multiple orders includes: Based on the geometric data and material types of the process models of multiple orders, the process models of multiple different orders are laid out to generate N layout schemes, and estimated cost information is generated for each layout scheme, where N≥1. The layout schemes include merged layout schemes and individual layout schemes. Production tasks are formulated based on the aforementioned layout scheme.

3. The method according to claim 2, characterized in that, Layout of process models for multiple different orders includes: M orders with the same material type and whose process model geometry data meet the preset compatibility conditions are grouped into the same consolidated order batch, where M≥2; Treat each order that does not belong to any of the combined order batches as a separate batch; For each consolidated order batch, a consolidated layout scheme is generated based on the delivery time and process model geometry data of all orders in that batch, according to the predetermined consolidated order strategy. For each individual batch, a separate layout scheme is generated based on the order's delivery time and process model geometry data.

4. The method according to claim 3, characterized in that, The merged layout scheme generated according to the predetermined order merging strategy includes: The production urgency of a consolidated order is determined based on the delivery times of all orders within that consolidated order. Calculate the dimensional matching degree of the consolidated batch based on the geometric data of the process models of all orders within the consolidated batch; Based on the production urgency and the size matching degree, the corresponding order calculation method is selected to generate a merged layout scheme.

5. The method according to claim 4, characterized in that, Based on the production urgency and the size matching degree, the corresponding order calculation method is selected to generate a merged layout scheme, including: When the production urgency is higher than the preset urgency threshold, select the first merge calculation method to generate a merged layout scheme; When the production urgency is not higher than the preset urgency threshold and the size matching degree is higher than the preset matching threshold, select the second order calculation method to generate a merged layout scheme. When the production urgency is not higher than the preset urgency threshold and the size matching degree is not higher than the preset matching threshold, the third merge calculation method is selected to generate a merged layout scheme.

6. The method according to claim 5, characterized in that, The first consolidated order calculation method is as follows: Calculate the minimum package cube of the process model for each order in the consolidated batch; based on the minimum package cube size of each model, arrange the minimum package cubes of each model on the same substrate plane, determine the position and printing orientation of each process model on the substrate, and thus generate a consolidated layout scheme. The second consolidated order calculation method is as follows: Within a preset set of rotation angles, adjust the printing orientation of the process model for each order in the batch and determine the spatial relative position of each process model to minimize the projected outer envelope area of ​​all models on the substrate plane. Based on the determined printing orientation and spatial relative position of each process model, the overall minimum package cube of the process models of all orders in the consolidated batch is calculated; based on the minimum package cube, the position of each model on the substrate is determined, thereby generating a consolidated layout scheme.

7. The method according to claim 5, characterized in that, The calculation method for the third consolidated order is as follows: Based on the geometric data of the process models of each order in the consolidated batch, the process models of each order in the consolidated batch are divided into multiple model subsets, where the geometric feature similarity of each process model within the same model subset is higher than the preset similarity threshold. For each model subset, perform the following operations: adjust the printing orientation of each process model in the subset within the preset rotation angle set, and determine the spatial relative position of each process model so that the projection outer envelope area of ​​all models in the subset on the substrate plane is minimized; treat each model subset as a whole and calculate the overall minimum enclosed cube of each model subset. Arrange the overall minimum wrapping cubes of each model subset onto the same substrate based on the overall minimum wrapping cube size of each model subset; The position and printing orientation of each process model within each model subset on the substrate are determined to generate a merged layout scheme.

8. The method according to claim 1, characterized in that, The allocation of production tasks to target individual machines based on the equipment status of multiple individual machines in the workshop includes: Obtain the current status information of each individual device, including the current task load, device health status, maximum forming area, and remaining powder amount; Based on the production task, the maximum forming area of ​​each single machine, and the amount of remaining powder, all candidate single machines that meet the production task requirements are screened out. According to the predetermined allocation rules, a target single-machine device is selected from the candidate single-machine devices. The allocation rules include giving priority to the device with the lightest current task load and normal device status.

9. The method according to claim 1, characterized in that, The abnormal monitoring results include quality abnormalities and cost abnormalities; The monitoring of production tasks by combining equipment operating parameters and product quality data includes: The product quality data collected according to the preset cycle will be compared with the corresponding product quality standard tolerance range. If the product quality data exceeds the quality standard tolerance range, it will be determined that there is a quality abnormality. Based on real-time collected equipment operating parameters, the actual execution cost of the production task is calculated; the actual execution cost is compared with the estimated cost information of the layout scheme corresponding to the production task; if the deviation between the actual execution cost and the estimated cost exceeds the allowable range, it is determined that there is a cost anomaly.

10. A digital additive manufacturing workshop implementation system, characterized in that, The system includes: The parsing module is used to receive and parse multiple external orders, obtain the order information of each order, and formulate production tasks based on the order information of multiple orders. The order information includes delivery time, process model geometric data, and material type. The allocation module is used to allocate the production task to the target single machine based on the equipment status of multiple single machines in the workshop; The execution module is used to control the target single-machine device to execute the assigned production tasks; The data acquisition module is used to collect the equipment operating parameters of the target single machine in real time during the execution of production tasks, and to collect product quality data according to the preset production cycle. The monitoring module is used to monitor production tasks by combining equipment operating parameters and product quality data, send the monitoring results to the preset terminal in real time, and issue an alarm when the monitoring results are abnormal.

Images