Plant for the production of high-performance metal components

Abstract

A plant (1) for the production of stamped and bent metal components, from raw material, e.g. a metal strip, to the finished product, e.g. a spring, consisting of: - an input (E) for the raw material; - an output (U) for the finished product; - Means for advancing (4) the raw material, arranged in accordance with the inlet (E); - a large number of sensors (3) capable of detecting a predefined set of parameters; The system (1) between the input (E) and the output (U) comprises the following: - a first device (10) configured to measure a predetermined mechanical property of the raw material; - a second processing device (20) configured to shear and bend the raw material to obtain the finished product; the system (1) comprises a third device (30) downstream of the output (U) which is configured to measure the geometric dimensions of the finished product; wherein the system (1) further comprises a first logical processing unit (2) which is operationally connected to the plurality of sensors (3) and to the first, second and third device (10, 20, 30) and is configured to perform the following steps sequentially and iteratively: - Receiving the measurement data of the predetermined mechanical property defined by the first device (10) and the data of the predetermined parameter set acquired by the plurality of sensors (3); - Comparison of the received data with predefined or adjustable reference values ​​in the same first logical unit (2); - Generation of the setting data of the second device (20) and transmission of the same data to it; - Receiving data regarding the geometric dimensions of the finished product, which were acquired by the third device (30); - Updating the reference values ​​in the first logical unit (2).

Description

Scope

[0001] The present invention is applicable in the technical field of industrial mechanics and relates in particular to a system for the production of highly efficient stamped and bent metal components. State of the art

[0002] The production of stamped and bent metal parts, such as springs or similar items, using stamping and bending machines is well known.

[0003] These machines combine punching and bending processes to produce metal components with tight tolerances and complex geometries.

[0004] However, setting up the machines and controlling the production parameters is predominantly done manually.

[0005] Therefore, manual interventions combined with the variability of the raw material lead to high waste production, a considerable amount of time spent setting up the machines with the associated downtime, and the need for dedicated specialist personnel.

[0006] This leads to enormous production costs. Description of the invention

[0007] The aim of the present invention is to overcome at least some of the disadvantages described above by providing a system for the production of highly efficient stamped and bent metal components.

[0008] Another goal is to provide a system that can minimize production waste.

[0009] Another goal is to provide a system that enables shorter setup times.

[0010] Another goal is to provide a system that does not require the use of specialist personnel.

[0011] Another goal is to provide a system that enables significant savings in production costs.

[0012] These and other purposes, which will become clearer below, are achieved by a system for manufacturing metal components according to the features described, illustrated and / or claimed herein.

[0013] The dependent claims define advantageous embodiments of the invention. Brief description of the drawings

[0014] Further features and advantages of the invention will become clearer from the detailed description of a preferred, but not exclusive, embodiment of the invention, which is illustrated by a non-limiting example with the aid of the accompanying drawing tables. These show: Fig. 1A is a schematic view of a part of system 1, where Fig. 1B is an enlarged view of a detail; Fig. 2A is a schematic view of another part of system 1, where the Fig. 2B and Fig. 2C are enlarged views of some details; Fig. Figure 3 is a schematic view of devices 20 and 30. Detailed description of some popular implementation examples

[0015] Referring to the figures mentioned, an installation 1 for the production of stamped and bent metal components is described, from the raw material, for example a metal strip, to the finished product, for example a spring.

[0016] The finished products also include components for the automotive industry, such as parts for engines and chassis, for electronics, for precision mechanics, clips, brackets, parts for household appliances and the like.

[0017] System 1 can include an input E for the raw material and an output U for the finished product.

[0018] System 1 may also include quality control equipment, which is described in more detail below and is positioned downstream of the U outlet to control the quality of the finished product.

[0019] The sequence of components of system 1 can expediently define a production line L.

[0020] At entrance E, there may be a feeding device 4, for example a reel for unwinding the metal strip to support and advance the raw material until the finished product is reached.

[0021] Suitablely, the system 1 can include a logical data processing unit 2 and a variety of sensors 3 capable of detecting a predetermined set of parameters, such as the ambient temperature, the temperature of the raw material, and the speed of its feed.

[0022] For this purpose, such sensors can include temperature sensors, motion sensors, presence sensors, strain gauges and digital cameras.

[0023] Advantageously, between the input E and the output U, system 1 can include two devices 10 and 20.

[0024] The first device 10 can be configured to measure a predefined property of the raw material.

[0025] This property could be the modulus of elasticity or the Young's modulus, a fundamental mechanical, physical, or geometric property of the raw material.

[0026] By measuring the elastic modulus inline, we can predict the material's behavior to optimize process parameters and reduce defects. This contributes to efficient and precise production, as described below.

[0027] The variability of the raw material properties significantly influences the final mechanical properties, such as the modulus of elasticity, and makes it difficult to achieve the desired behavior and thus error-free production with predefined settings.

[0028] For this purpose, the first device 10 can include one or more modules for measuring relevant properties of the specific raw material, such as the modulus of elasticity and other physical properties. For this purpose, the first device 10 can be configured to operate inline without interrupting the flow of the metal strip.

[0029] Device 10 can therefore contain one or more of the modules described below.

[0030] Advantageously, the device 10 can include an optical module 11 for digital correlation, comprising at least one pair of cameras 110, preferably with high resolution, and a coherent illumination system 111, wherein the cameras 110 and the latter can be connected to the data processing logic unit 2.

[0031] Cameras 110 capture images of the strip during feeding for quality control of the primary material. Simultaneously, the device can accommodate 10 additional modules for further measurements, such as controlled bending or torsional stress, temperature measurements, and roughness analyses. Logic unit 2 can also calculate surface deformation by digitally correlating the captured images with predefined images within logic unit 2 itself, in order to determine the surface deformation of the raw material under tension.

[0032] In this way, the logic unit 2 can derive typical material properties such as the modulus of elasticity, which is determined using an optimized contact technique that is particularly suitable for thin and moving materials.

[0033] Another module can be an ultrasound module 12, which includes at least one ultrasound transducer 121 and a receiver 122 connected to the data processing logic unit 2.

[0034] Module 12 can then be configured to generate and detect longitudinal or transverse elastic waves in the metal strip using the transducer 121 and the receiver 122.

[0035] Unit 2 can calculate the modulus of elasticity as a function of the propagation speed of the waves and the density of the material.

[0036] The integration of module 12 can take place in a contact or immersion section of the belt.

[0037] In this case too, the measurement can be carried out quickly and non-destructively.

[0038] Another module of the device 10 can be a bending or torsion measuring module 13, comprising a torsion or bending moment application system 131 and a curvature or torsion angle measuring system 132, which is connected to the data processing logic unit 2.

[0039] This module 13 can then determine the elastic modulus based on the mechanical response of the belt, which is detected by the measuring system 132 and then processed by the logic unit 2 by applying a continuous and controlled stress in a transition area via the application system 131.

[0040] System 1 may also include stand-alone modules that can perform sampling checks, batch validations or periodic calibrations and may be functionally connected to the above-mentioned devices 10 and 20 to provide reference data or to calibrate the devices in-line.

[0041] The independent modules include a strain measurement module 40 and a tensile testing module 50.

[0042] These modules can be operationally connected to the logical unit 2.

[0043] The strain measurement module 40 can be configured to detect the longitudinal deformation of the raw material using electrical resistance measuring strips and to provide a measured value for the deformation itself.

[0044] The tensile testing module 50 can include a system for gripping and pulling the belt 51, a linear drive 52 and a force sensor 53, while the data processing unit 2 can be able to generate a stress-strain curve.

[0045] The device 20 can be used to process the raw material and can therefore be configured to shear and bend the raw material to obtain the finished product.

[0046] Device 20 may therefore be a known shear bending machine.

[0047] Advantageously, the system 1 can include a device 30 behind the output U, which is configured to measure the geometric dimensions of the finished product.

[0048] For this purpose, the device 30 can include an artificial vision module 31, which may include an image acquisition unit 311, a platform for positioning the finished product and a coherent illumination system 313, which may be connected to the data processing logic unit 2.

[0049] The latter can implement dimensional processing and analysis software to process the captured images and determine the geometric dimensions of the finished product.

[0050] The image acquisition unit 311 can include one or more high-resolution cameras capable of capturing images of the product positioned on the platform from different angles.

[0051] The cameras can be synchronized with the coherent lighting system 313, for example with structured LEDs, to ensure maximum readability of product edges and surfaces.

[0052] In another embodiment, the system 1 can include a device for the automatic or robot-assisted transport of the finished product from device 20 to device 30.

[0053] The implemented software can also contain instructions for logic unit 2 to query databases in the cloud or on-premises to ensure part traceability, automated geometric quality control, and adaptive control of the forming unit 20. Advantageously, the adaptive control allows for automatic adjustment of the forming device's setting parameters after comparing the dimensions from the digital image of the finished part with the reference sample.

[0054] Furthermore, this software can contain additional instructions for communication with CNC machines, handling robots, or automatic marking systems of the finished product.

[0055] Advantageously, the logic unit 2, as described above, can be operationally connected to the sensors 3 and the devices 10, 20, 30, 40, 50, possibly to the data processing logic units of such devices, if any.

[0056] Logical unit 2 can execute the following phases sequentially and iteratively: - Receiving measurement data of mechanical properties, such as the modulus of elasticity, from the device 10 and data from sensors 3, such as temperature data and feed rate of the feed device 4; - Comparison of the received data with predefined or adjustable reference values ​​in the same logical unit 2; - Generation and transmission of hiring data to the second processing facility 20; - Receiving data regarding the geometric dimensions of the finished product from the third device 30; - Updating the reference values ​​present in logical unit 2.

[0057] It is evident from the above that the logic unit 2 will be able to automatically and predictively determine the data for processing the raw material and transmit it to the device 20, thanks to the comparison between the data acquired by the device 10 and the sensors 3 with a set of expected target data.

[0058] In any case, this last sentence can be continuously modified based on the data received from device 30 in order to understand how the setting of device 20 then affected the finished product.

[0059] For this purpose, the logical unit 2 can be equipped with an artificial neural network that can be configured to operate in closed mode.

[0060] This means that the neural network will be able to establish a continuous correlation between the mechanical properties data defined by the device 10, the parameter set data acquired by the sensors 3, and the geometric dimensions data acquired by the device 30, in order to ensure incremental learning of the neural network itself.

[0061] Therefore, based on the recorded measurement of the mechanical properties, the neural network can predict the expected dimensional deviations for each new batch of material.

[0062] Based on these predictions, the neural network can generate parametric corrections in real time, which are applied to the shear-bending device 20 and, for example, dynamically change the reference dimensions, the bending depth, the compensation angles and the feed rate of the raw material.

[0063] This allows us to neutralize the effects of elastic fluctuations in the raw material.

[0064] It goes without saying that such a system 1 is able to reduce production rejects, since the number of non-conforming parts can be minimized through the proactive measurement of the modulus of elasticity or other mechanical properties and the self-regulation of the shear bending machine.

[0065] Furthermore, setup times can be optimized, downtime and the need for manual intervention reduced, and the quality and precision of the finished product improved.

[0066] The continuous correlation between material properties, processing parameters and final geometric measurements ensures higher bending precision and consistent quality of the finished products.

[0067] All of this could lead to less dependence on highly skilled operators, as well as lower production costs through less waste, less downtime, and greater efficiency.

[0068] It follows that minimizing material waste and using resources more efficiently can contribute to the company's sustainability and reduce the environmental impact of production.

[0069] The present invention may comprise various similar or identical parts and / or elements. Unless otherwise specified, similar or identical parts and / or elements are designated by a single reference numeral. The described technical features apply to all similar or identical parts and / or elements.

[0070] The invention is highly modifiable and may fall within the scope of protection of the appended claims. All details can be replaced by other technically equivalent elements, and the materials can be different as required, without departing from the scope of protection of the invention as defined in the appended claims.

[0071] The invention can be better understood with the help of the following examples. These are intended to illustrate the invention but do not limit it.

Claims

[1] A plant (1) for the production of stamped and bent metal components, from raw material, e.g. a metal strip, to the finished product, e.g. a spring, consisting of: - an input (E) for the raw material; - an output (U) for the finished product; - Means for advancing (4) the raw material, arranged in accordance with the inlet (E); - a large number of sensors (3) capable of detecting a predefined set of parameters; The system (1) between the input (E) and the output (U) comprises the following: - a first device (10) configured to measure a predetermined mechanical property of the raw material; - a second processing device (20) configured to shear and bend the raw material to obtain the finished product; the system (1) comprises a third device (30) downstream of the output (U) which is configured to measure the geometric dimensions of the finished product; wherein the system (1) further comprises a first logical processing unit (2) which is operationally connected to the plurality of sensors (3) and to the first, second and third device (10, 20, 30) and is configured to perform the following steps sequentially and iteratively: - Receiving the measurement data of the predetermined mechanical property defined by the first device (10) and the data of the predetermined parameter set acquired by the plurality of sensors (3); - Comparison of the received data with predefined or adjustable reference values ​​in the same first logical unit (2); - Generation of the setting data of the second device (20) and transmission of the same data to it; - Receiving data regarding the geometric dimensions of the finished product, which were acquired by the third device (30); - Updating the reference values ​​in the first logical unit (2). [2] System according to the preceding claim, wherein the predetermined mechanical property is the modulus of elasticity. [3] System according to the preceding claim, wherein the first device (10) comprises an optical module (11) with at least one pair of cameras (110) and at least one coherent illumination system (111), the latter being operationally connected to the first processing logic unit (2), and the optical module (11) is configured to take pictures of the raw material during a predefined stress and compare the taken pictures with predefined pictures in the same first processing logic unit (2), thereby determining the surface deformation of the raw material in order to calculate the modulus of elasticity. [4] System according to claim 2 or 3, wherein the first device (10) comprises an ultrasonic module (12) with at least one ultrasonic transducer (121) and a receiver (122), the latter being operationally connected to the first processing logic unit (2), and the ultrasonic module (12) is configured to generate and detect longitudinal or transverse elastic waves in the raw material and to calculate the elastic modulus as a function of the wave propagation speed and the density of the raw material. [5] System according to one of claims 2 to 4, wherein the first device (10) comprises a bending or torsion measuring module (13) comprising a torsion or bending moment application system (131) and a torsion angle curvature measuring system (132), the latter being operationally connected to the first processing logic unit (2) which calculates the elastic modulus based on the mechanical response of the raw material. [6] System according to one of the preceding claims, wherein the third device (30) comprises an artificial vision module (31) with an image acquisition unit (311), a positioning platform for the finished product and a coherent lighting system (313), the same being operationally connected to the first logical processing unit (2), the latter being configured to process the captured images and determine the geometric dimensions of the finished product. [7] System according to one of the preceding claims, wherein the predetermined set of parameters includes the ambient temperature, the temperature of the raw material, the feed rate of the feed device (4) and the plurality of sensors (3) including temperature sensors and motion sensors. [8] System according to one of the preceding claims, wherein the first processing logic unit (2) is equipped with an artificial neural network. [9] System according to the preceding claim, wherein the neural network is configured to operate in closed mode by iterative correlation between the data of the predetermined mechanical property defined by the first device (10), the data of the predetermined parameter set acquired by the plurality of sensors (3) and the data relating to the geometric dimensions acquired by the third device (30) in order to ensure incremental learning of the neural network itself.

Images