Manufacturing plant for cast-in components, as well as a manufacturing process for a cast-in component

Abstract

1. Production plant (1) for cast-in components (2) of motor vehicles, comprising at least the following components: - a casting plant (3) for producing cast-in components (2); - a defect identification unit (4) for detecting defects (5) in the cast-in components (2); - a dynamically controllable rework robot (6) for reworking the cast-in components (2); - a sensor unit (7) for recording rework parameters of the rework robot (6); - a control unit (8) for controlling the rework robot (6) on the basis of the detected defects (5) and the rework parameters, characterized in that the defect identification unit (4) is configured to detect design weaknesses that regularly lead to identified defects (5) in the cast-in components (2).

Description

The invention relates to a manufacturing plant for cast-in components of motor vehicles, as well as a manufacturing process for a cast-in component. The increasing modularization of vehicles in the automotive industry necessitates new approaches to the production of automotive components. It is common practice in industry to manufacture certain automotive components, such as exterior body panels, as in-mold components. These exterior panels are used, for example, on vehicle parts like doors, side panels, or fenders. In-mold components (also known as in-mold parts) are manufactured using an in-mold process. Post-processing presents a key challenge. After injection molding, these components often require rework to eliminate errors or defects such as flash, uneven edges, and / or surface irregularities. This rework is currently performed either manually or with inflexible, pre-programmed robotic systems that cannot dynamically adjust to defects.Therefore, post-processing processes are time-consuming, expensive, inflexible and / or do not achieve the required quality. From KR 10 2 249 823 B1, a device for deburring an injection-molded product, such as a connector, is known, wherein burrs or injection skins are automatically removed from an area adjacent to a parting line that is in contact with a mold during an injection molding process, thereby ensuring the uniformity of the quality execution by means of a first conveyor transporting different workpieces.A first camera module unit captures identification information of the workpieces; a robot places the workpieces into suitable clamping fixtures according to this identification information; a second camera module detects burrs on the workpieces held in the clamping fixtures; a cutting machine removes the detected burrs; a third conveyor transports the processed workpieces further; a third camera module detects defective workpieces with remaining burrs; and an ejection actuator pushes these defective workpieces from the third conveyor into a separate reject box. From DE 10 2020 200 656 A1, a method for the subtractive machining of a workpiece, a control device and a machining system is known, wherein in the method for the subtractive machining of a workpiece by means of a machining element at least one physical quantity of a machining element is recorded and, based on the at least one quantity, a conclusion is drawn about the occurrence of a deviation in the machining from a target machining process. From KR 10 2 784 898 B1, a method for automating the deburring and inspection of an injection-molded product is known, wherein the deburring and good part inspection of a spherical ball valve component, into which a pin-shaped insert is inserted, is automated using an injection-molded product processing system with a collaborative robot, a deburring device, and an injection-molded product inspection device, by removing burrs remaining on the ball valve component in the injection molding process in a deburring step and checking in an injection-molded product inspection step whether the ball valve component, completely deburred by the deburring step, is a good part. From CN 2 05 997 290 U, a coordinated truss-like mechanical handling system for injection molding with a visually guided positioning function is known, wherein a visual image system is added to an existing coordinate support injection molding manipulator, which is in communication connection with a control CPU of the manipulator, so that the manipulator automatically recognizes whether a product is a good part or not, determines the position information of the product and then automatically performs further operations, thereby increasing the degree of automation of the manipulator, increasing production efficiency, reducing production costs and decreasing the space requirement. Based on this, the present invention aims to overcome, at least partially, the disadvantages known from the prior art. The features of the invention are defined in the independent claims, for which advantageous embodiments are shown in the dependent claims. The features of the claims can be combined in any technically meaningful way, whereby the explanations in the following description and features from the figures, which comprise supplementary embodiments of the invention, can also be used. The invention relates to a manufacturing plant for cast-in components of motor vehicles, comprising at least the following components: - a casting plant for producing cast-in components; - a defect identification unit for detecting defects in the cast-in components; - a dynamically controllable rework robot for reworking the cast-in components; - a sensor unit for recording rework parameters of the rework robot; - a control unit for controlling the rework robot based on the detected defects and the rework parameters, characterized in that the defect identification unit is configured to detect design weaknesses that regularly lead to identified defects in the cast-in components. Unless explicitly stated otherwise, ordinal numbers used in the preceding and following descriptions serve solely for unambiguous differentiation and do not indicate any order or ranking of the components referred to. An ordinal number greater than one does not necessarily imply the presence of another such component. The present invention relates to a manufacturing system for automotive components for a motor vehicle. The automotive components are cast-in parts. The manufacturing system comprises at least one casting system, a defect identification unit, a rework robot, a sensor unit, and a control unit. The casting system is designed for the production of cast-in components. It is designed for an in-mold process, which is suitable, for example, for the production of exterior body panels. Exterior body panels are visible components with external surfaces on the vehicle. These cast-in components preferably possess sufficient optical quality to be used as visible components without the need for painting. In the in-mold process, a plastic material, preferably injection molded, is poured around another component or material. This allows, for example, decorative layers, color layers, or structural elements to be cast in. This enables the described optical quality of cast-in components. The defect identification unit is designed to detect defects in the cast-in components. For example, surface defects, deviations from nominal contours, color variations, burrs, and other rework requirements can be identified using the defect identification unit. The defect identification unit comprises one or more sensors, such as optical sensors. The sensors are integrated, for example, into the rework robot and / or its tooling. The defect identification unit is positioned along a production direction before and / or after the rework robot. For example, the sensors of the defect identification unit are attached to robots and can be moved by them relative to the cast-in component. Alternatively or additionally, the cast-in components are mounted on movable supports to be moved relative to fixed sensors of the defect identification unit.For example, the sensors, or at least some of the sensors, of the fault identification unit are located on the rework robot for rework. The rework robot is dynamically controllable and designed for reworking the cast-in components. Preferably, the rework robot is designed for grinding, polishing, and / or deburring the cast-in components. For example, the rework robot comprises a feed mechanism, such as a robot arm, and a tool, preferably arranged at a distal end of the feed mechanism. The sensor unit is designed to acquire rework parameters of the rework robot. The sensor unit is preferably integrated into the rework robot or its tooling. For example, the sensor unit may include optical sensors, position sensors, force sensors, torque sensors, and / or pressure sensors. The control unit is designed to control the rework robot based on the identified defects and rework parameters. This allows the rework robot to be controlled dynamically, i.e., depending on the situation. The control unit comprises at least one processor and one data memory. The data memory and / or the processor are located locally in the production plant and / or in a backend system. The data memory is designed to store relevant process parameters, i.e., the rework parameters and positioning parameters, for controlling the rework robot. Automating post-processing reduces production time. Precise adjustments and error detection minimize the need for rework. The production system is adaptable to various component geometries and can respond to different errors or defects. Consistent and error-free post-processing can be achieved, for example, through the combination of camera systems, artificial intelligence, and adaptive tools. In an advantageous embodiment of the manufacturing plant, it is further proposed that the control unit includes a machine learning algorithm by means of which the control of the rework robot for reworking the cast-in components can be adapted. According to the embodiment proposed here, the control unit includes a machine learning algorithm. This algorithm enables the control of the rework robot for reworking the cast component to be adapted. During rework, the sensor unit, as already explained, acquires rework parameters of the robot. These parameters are stored in a database, or the data storage of the control unit. The stored parameters serve as the basis for a machine learning algorithm, preferably based on artificial intelligence. For example, the control unit determines optimized positioning parameters for the robot over time, perhaps using a reward algorithm. The robot is, for example, a human-robot collaboration (HRC) robot and has corresponding force and torque sensors in its sensor unit. For example, the final quality of the casting component and / or the quality improvement achieved through rework can be recorded or determined using the defect identification unit or another control device for checking the quality of the casting components. For example, a reward value for the machine learning algorithm is set based on such final quality or quality improvement. The rework robot can be controlled by the control unit using the positioning parameters. In a further advantageous embodiment of the manufacturing plant, it is proposed that the fault identification unit comprises at least one camera. According to this embodiment, the fault identification unit comprises at least one camera. In other words, at least one of the sensors of the fault identification unit, for example, several or all of the sensors of the fault identification unit, is a camera. In an advantageous embodiment of the manufacturing plant, it is further proposed that the rework parameters include at least one of the following values: - a contact force; - a torque; - a rework time; - a selected tool for rework; - a grinding parameter; - a feed rate; and - a rotational speed of a tool for reworking the cast-in components. According to this embodiment, the rework parameter of the rework robot, which can be detected by means of the sensor unit, is a contact force, a torque, a rework time, a selected tool for rework, a grinding parameter, feed rate and / or a speed of the tool. The contact force represents the force with which a tool, such as a grinding tool or a cutting tool, is pressed against a surface of the casting to be reworked. For example, the contact force can be detected by a pressure sensor or force sensor in the sensor unit. For example, several different tools can be used for post-processing. These tools include, for example, those for grinding, machining, polishing, recoloring or painting, heating and / or forming. For example, the rework robot includes several tools and / or is equipped to perform tool changes. For example, a tool currently picked up and / or used in the rework robot can be identified by means of an identification marker, for example, electronically or optically via the sensor unit. Grinding parameters include, for example, grit sizes, abrasives, or other values ​​that influence material removal and / or the resulting surface finish. The feed rate indicates, for example, the speed and / or force with which the tool is moved in a direction parallel to a surface of the casting component to be reworked. For example, the tool is a rotatable tool. For example, a rework parameter is the rotational speed of such a rotatable tool. In a further advantageous embodiment of the manufacturing plant, it is proposed that the rework robot has an adaptive tool which can be controlled by means of the control unit. According to this embodiment, the rework robot includes an adaptive tool that can be controlled by the control unit. The adaptive tool features, for example, dynamic Z-axis control. This allows, for instance, the automatic detection of defects such as burr formation by the tool and the adjustment of the positioning parameters of the rework robot and / or tool. In other words, the sensor unit and / or actuators that can be controlled by the control unit are not located, or not exclusively located, in the rework robot itself, but rather in the tool. According to a further aspect, a manufacturing process for a casting component is proposed, comprising the following steps: a. Manufacturing a casting component; b. Determining defects in the casting component by means of a defect identification unit; c. Acquiring rework parameters of a rework robot by means of a sensor unit; d. Determining positioning parameters for controlling rework of the casting component by means of the control unit, characterized in that the defect identification unit identifies design weaknesses which regularly lead to identified defects in the casting components. A manufacturing process for a cast-in component is proposed here. The manufacturing process includes at least steps a., b., c., and d., which are explained below. The production of an in-mold component in step a. is preferably carried out using an in-mold system. The in-mold system is designed for an in-mold process, which is suitable, for example, for the production of exterior body panels. In other words, the produced in-mold components are, for example, exterior body panels. Such in-mold components preferably have sufficient optical quality to be used as visible components without painting. For example, the in-mold system includes an injection mold into which a decorative element is placed and then overmolded with a plastic. Defects in the casting component are detected in step b using a defect identification unit. For example, the casting component is detected by sensors of the defect identification unit, and its surface, color, and / or shape are checked. For example, the corresponding sensors are arranged at a distal end of a rework robot. In step c, rework parameters of the rework robot are recorded using a sensor unit. In other words, as explained above, the rework parameters of the rework robot are recorded by the sensors of the sensor unit during the rework of the casting component in step d. Step c and step d thus preferably take place simultaneously, preferably in a control loop. In step d., the control unit defines positioning parameters for the rework robot to control the rework process. This is preferably done in a closed-loop control system based on the rework parameters recorded in step c. and / or the errors determined in step b. For example, the system continuously or at time intervals checks whether the rework parameters correspond to the desired target parameters, such as general target parameters or target parameters for a specific rework operation or rework location on the cast component. The rework robot or its tooling is then controlled using the positioning parameters to adjust the rework parameters accordingly. For example, the system continuously or at time intervals checks whether the errors have been corrected. If an error, such as a burr, has been sufficiently reduced, the rework can be aborted using the control unit. In this case, the casting component is finished and can be conveyed to the next work step, such as assembly on a vehicle body, or the casting component is inspected for further errors. In a further advantageous embodiment of the manufacturing process, it is proposed that the positioning parameters be determined by means of a machine learning algorithm via the control unit. According to this embodiment, the production system has a machine learning algorithm by which the control unit learns to determine positioning parameters based on the recorded rework parameters and / or the detected errors. In other words, the production system is designed to improve rework independently. For example, artificial intelligence is used to improve or learn the determination of control parameters. For instance, the results of rework, i.e., error correction, can be verified, preferably using the error identification unit. For example, the rework parameters used, the achieved quality of the rework, and / or the necessary time expenditure can be stored in a data memory of the control unit. For example, the control unit can be trained with an artificial intelligence or machine learning algorithm based on this data stored in the data memory. In a further advantageous embodiment of the manufacturing process, it is proposed that design weaknesses be identified using a machine learning algorithm. Design weaknesses are identified that regularly lead to defects in the cast-in components. These weaknesses are preferably identified using a machine learning algorithm. The machine learning algorithm is based, for example, on artificial intelligence. The artificial intelligence continuously analyzes the identified defects and their location on the cast-in component, particularly at critical points such as edges, radii, recesses, protrusions, and / or variations in material thickness. Based on this data, patterns and correlations are identified that indicate recurring defects. For example, the identified design weaknesses are stored in the database. The stored data then serves as a basis for optimizing production processes and / or the design, such as the design of the casting component. For example, a notification can be issued to designers when such a design weakness has been identified, and / or an automatic design adjustment, for example using artificial intelligence, can be suggested. By identifying and analyzing design weaknesses, the quality of the cast-in components is improved. In a further advantageous embodiment of the manufacturing process, it is proposed that the manufacturing process be carried out using a manufacturing plant according to an embodiment as described above. According to this embodiment, the manufacturing process can be carried out using a production plant as described above, to which reference is hereby made again. Accordingly, the production plant is preferably designed to carry out the described manufacturing process. The invention described above is explained in detail below against the relevant technical background with reference to the accompanying drawings, which show preferred embodiments. The invention is in no way limited by the purely schematic drawings, it should be noted that the drawings are not dimensionally accurate and are not suitable for defining dimensional relationships. Figure 1 shows a schematic representation of a manufacturing plant for a casting component; and Figure 2 shows a flowchart of the manufacturing process for a casting component. Figure 1 shows a schematic representation of a production plant 1 for an in-mold component 2. The production plant 1 comprises an in-mold unit 3, which is designed for the production of in-mold components 2, in this case, engine hoods. The in-mold components 2 are manufactured using an in-mold process. The in-mold components 2 can be conveyed through the production plant 1 along a production direction 15 by means of conveying means 14. A defect identification unit 4 is arranged along a production direction 15 downstream of the injection molding system 3 to detect defects 5 in the casting components 2. Defects 5, such as burrs, color deviations, or uneven edges, can be detected by means of the defect identification unit 4. The defect identification unit 4 comprises a camera 9, which is arranged at a distal end of a robot arm 16. The camera 9 thus forms a sensor for optically capturing a surface of the casting component 2. Defects 5 in the casting component 2 can therefore be detected, for example, by comparing the surface and shape of the casting component 2 with a target image. As shown, production plant 1 has two dynamically controllable rework robots 6, which are designed for reworking the cast-in components 2. The rework robots 6 are optionally arranged along the production direction 15 behind the defect identification unit 4. Alternatively or additionally (not shown here), the defect identification unit 4 or individual sensors, such as cameras 9, of the defect identification unit 4 are arranged in or on the rework robots 6. Each rework robot 6 includes a tool 10 for reworking the cast-in component 2. For example, the tools 10 are grinding units. A sensor unit 7 is designed to detect rework parameters of the rework robot 6. The sensor unit 7 is integrated into the rework robot 6 and / or its tools 10. The sensor unit 7 includes, for example, optical sensors, distance sensors, pressure sensors, torque sensors, and / or force sensors. In other words, the sensor unit 7 can detect, for example, the position of the rework robot 6 relative to the casting component 2 and / or the force with which it is machining the casting component 2. A control unit 8 is in communicative connection with the sensor unit 7 and the fault identification unit 4. The control unit 8 comprises at least one processor 12 and a data storage unit 13. The control unit 8 is designed to control or regulate the rework robots 6 or their tools 10 on the basis of the faults 5 determined by means of the fault identification unit 4 and the rework parameters recorded by means of the sensor unit 7. Fig. 2 shows a manufacturing process 11 for a casting component 2 in a flowchart. The manufacturing process 11 comprises steps a., b., c. and d., which are carried out using a manufacturing plant 1 according to Fig. 1. In step a., an in-mold component 2 is produced using the injection molding system 3. This is done using an in-mold process, in which a decorative element is overmolded with plastic, creating a visible component with a surface similar to a painted surface. During such injection molding, defects 5, such as burrs or unevenness, can occur on the in-mold component 2, which must be corrected by post-processing. For rework, the rework robot 6 is dynamically controlled by means of the control unit 8. In step b, defects 5 of the casting component 2 are identified using the defect identification unit 4. For example, the camera 9 of the defect identification unit 4 is moved across the casting component 2, thus detecting deviations from a target state of the casting component 2, which is stored, for example, on the data storage device 13. Based on the identified defects 5, the control unit 8 can then decide, for example, whether, where, and how rework of the casting component 2 is necessary. If no rework is necessary, for example, because no defects 5 or no relevant defects 5 were found, the casting component 2 can be transported in step e, for example, to the next production station, such as assembly on a vehicle body. Alternatively, for example, if defects 5 that cannot be remedied by the rework robot 6 are identified, the casting component 2 can be rejected. If 6 correctable defects 5 were identified using the rework robot, rework will take place using the rework robot 6. In step c., rework parameters of the rework robot 6 are preferably continuously recorded by means of a sensor unit 7. The rework parameters serve to dynamically control the rework robot 6 or its tool 10. In step d., the positioning parameters for controlling the reworking of the casting component 2 using the reworking robot 6 are defined and the reworking robot 6 is controlled using these positioning parameters, or the reworking parameters are regulated. For example, the rework, preferably continuously, is monitored by means of the defect identification unit 4, thus verifying whether the defect 5 has been sufficiently rectified. If the defect identification unit 4 determines that the defect 5, preferably all defects 5, of the cast-in component 2 have been sufficiently rectified, the rework is stopped and the cast-in component 2 is transported, for example in step e., to assembly on a motor vehicle body. For example, rework is optimized using a machine learning algorithm. For this purpose, the rework parameters and / or identified errors 5 are stored in a data memory 13 and evaluated using artificial intelligence in step f. This allows, for example, the improvement of setting the parameters for the rework robot 6 in step d. Alternatively or additionally, artificial intelligence can be used, for example, to identify weaknesses in the design of the casting component 2 in step g. For example, excessively sharp edges can lead to an increased risk of burrs, which can be avoided with minor design modifications. A notification to the designer could then be issued, for example. A manufacturing plant for cast components is proposed, which enables particularly efficient and cost-effective production of automotive components suitable as visible components. Reference symbol list 1 Production plant 2 Cast-in component 3 Casting plant 4 Defect identification unit 5 Defect 6 Rework robot 7 Sensor unit 8 Control unit 9 Camera 10 Tool 11 Manufacturing process 12 Processor 13 Data storage 14 Conveyor 15 Production direction 16 Robot arm

Claims

Manufacturing plant (1) for cast-in components (2) of motor vehicles, comprising at least the following components: - a casting plant (3) for producing cast-in components (2); - a defect identification unit (4) for detecting defects (5) in the cast-in components (2); - a dynamically controllable rework robot (6) for reworking the cast-in components (2); - a sensor unit (7) for recording rework parameters of the rework robot (6); - a control unit (8) for controlling the rework robot (6) on the basis of the detected defects (5) and the rework parameters, characterized in that the defect identification unit (4) is configured to detect design weaknesses which regularly lead to identified defects (5) in the cast-in components (2). Manufacturing plant (1) according to claim 1, wherein the control unit (8) comprises a machine learning algorithm by means of which the control of the rework robot (6) for reworking the casting components (2) can be adapted. Manufacturing plant (1) according to claim 1 or claim 2, wherein the fault identification unit (4) comprises at least one camera (9). Manufacturing plant (1) according to one of the preceding claims, wherein the rework parameters comprise at least one of the following values: - a contact force; - a torque; - a rework time; - a selected tool (10) for rework; - a grinding parameter; - a feed rate; and - a rotational speed of a tool (10) for reworking the casting components (2). Manufacturing plant (1) according to one of the preceding claims, wherein the rework robot (6) has an adaptive tool (10) which can be controlled by means of the control unit (8). Manufacturing method (11) for a casting component (2), comprising the following steps: a. Manufacturing a casting component (2); b. Determining defects (5) of the casting component (2) by means of a defect identification unit (4); c. Determining rework parameters of a rework robot (6) by means of a sensor unit (7); d. Determining positioning parameters for controlling rework of the casting component (2) by means of the control unit (8) by means of the rework robot (6), characterized in that the defect identification unit (4) identifies design weaknesses which regularly lead to identified defects (5) in the casting components (2). Manufacturing method (11) according to claim 6, wherein the positioning parameters are determined by means of the control unit (8) using a machine learning algorithm. Manufacturing method (11) according to claim 6 or claim 7, wherein the design weaknesses are determined by means of a machine learning algorithm. Manufacturing process (11) according to any one of claims 6 to 8, wherein the manufacturing process (11) is carried out by means of a manufacturing plant (1) according to any one of claims 1 to 5.

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