Discrete intelligent manufacturing method and system

Abstract

The invention belongs to the technical field of discrete intelligent manufacturing, and particularly relates to a discrete intelligent manufacturing method and system.The discrete intelligent manufacturing method comprises the following specific steps that product information in order products is split to form a bill of materials, the received order information is stored based on ERP software, and according to data in the bill of materials, the order information is stored according to the data in the bill of materials; computer aided design software is adopted to create a three-dimensional detailed model of the product, parts of the three-dimensional detailed model of the product are split, and the production quantity and time nodes of each part are determined; through the cooperation of the structure, the materials required by the product orders are split and listed according to the number of the product orders, so that workers can purchase the materials required by the products more conveniently, and through the mutual cooperation of the ERP system and the MES system, according to the materials listed on the material list, the materials required by the products can be purchased more conveniently. According to the method, the production steps are split while the number and the time node of the parts processed by the materials are determined, so that the parts are produced in batches in the production process.

Description

Technical Field

[0001] The present invention belongs to the technical field of discrete intelligent manufacturing, and specifically relates to a discrete intelligent manufacturing method and system. Background Art

[0002] Discrete manufacturing means that the production process of a product is usually broken down into many processing tasks. Each task requires only a small part of the company's capabilities and resources. Companies generally build some production organizations (departments, sections or groups) with similar equipment according to space and administrative management. In each department, the workpiece is processed from one work center to another for different types of processes. Companies often arrange the location of production equipment according to the main process flow to minimize the material transmission distance. In addition, the process route and equipment use of its processing are also very flexible, with many changes in product design, processing requirements and order quantity.

[0003] A Chinese invention patent publication number CN117668518B discloses a discrete intelligent manufacturing method and system, the key points of which are: the method first captures the acoustic wave signal generated during the assembly process through an acoustic wave sensor. A pressure sensor is used to monitor the pressure change during assembly. In addition, a high-precision optical monitoring device is used to observe the assembly process in real time to obtain optical monitoring data of component assembly. The method extracts key features from the acoustic wave signal and conducts a comprehensive analysis in combination with pressure and visual data. Through this multi-dimensional data analysis, it is possible to accurately determine whether the assembly quality of the component meets the preset standard. When the assembly quality does not meet the preset standard, the system can automatically adjust the parameters of the assembly machinery or issue an alarm to correct possible assembly problems in a timely manner. This intelligent manufacturing method not only improves assembly quality and production efficiency, but also reduces human errors and duplication of work, providing a new solution for the field of modern intelligent manufacturing.

[0004] However, the above technologies often have the following defects: Although the assembly process is monitored through sound wave signals and pressure changes to determine whether the product assembly quality meets the preset standards, human errors and repetitive work are reduced, providing new solutions for the field of modern intelligent manufacturing, when receiving an order, the parts required for the order cannot be marked, and the quantity of materials and processing steps required for the order product cannot be automatically preset. This not only makes it inconvenient for the staff to purchase the materials required for the product, but also makes it impossible to control the production progress when the order product is being produced, which may lead to vague control over the delivery time of the order product and affect the rapid production of the order product.

[0005] To this end, the present invention provides a discrete intelligent manufacturing method and system. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a discrete intelligent manufacturing method described in the present invention comprises the following specific steps: S1: Split the product information in the order product to form a bill of materials, store the received order information based on the ERP software, and use computer-aided design software to create a 3D detailed model of the product based on the data in the bill of materials and split the parts of the 3D detailed model of the product to determine the production quantity and time node of each part. Receive the production data in ERP based on MES, and generate specific production tasks and issue them to each equipment in the workshop; S2: Use the OPC server to receive production tasks issued by MES, control the DNC controller based on the hardware driver, and the DNC controller controls the machine tool equipment and processes the parts. Based on the OPC server, commands are issued to the controller and ground control console to control the industrial robot and AGV body to complete the processing and assembly of parts; S3: After the parts are assembled, the industrial robot places the parts on the scanning platform and scans the parts based on the calibrated laser scanner. The noise points of the scanned data are removed based on the filtering algorithm, and the data obtained from multiple scans are aligned and spliced ​​to form a three-dimensional model, which is then compared with the three-dimensional detailed model created by the computer-aided design software; S4: Based on model comparison, determine the assembly effect of order parts, scan products that do not meet the requirements again, confirm the location of product defects, and determine and adjust the assembly steps in the product production process; S5: Products that meet the preset standards provide feedback on product data based on the OPC server, and transmit the feedback data to the ERP system to complete the warehousing processing of the production products and the production of the order products.

[0008] As a further solution of the present invention: the computer-aided design software uses CAD to create a three-dimensional model of the product and accurately designs each component of the product, and the MES monitors the production status in real time, including but not limited to the production progress of product parts, equipment utilization, and quality status.

[0009] As a further solution of the present invention: the MES cooperates with the OPC server to issue control commands to control the industrial robots and AGV bodies on the production line, complete the response of the industrial robots and AGV bodies to the production tasks issued by the MES, complete the synchronous control and operation of machine tools, industrial robots and AGV bodies, and process and produce parts.

[0010] As a further solution of the present invention: an RFID tag is provided on the ordered product, and the RFID tag is read based on the OPC server in combination with the RFID reader, so that the industrial robot and the AGV body are controlled to accurately pick up the parts, thereby completing the transportation and assembly of the parts.

[0011] As a further solution of the present invention: the filtering algorithm is an RC filter, and the transfer function of the RC filter is:

[0012] in , R is the resistance value, C is the capacitance value, s is the complex frequency domain variable, the filtering effect of the RC filter depends on the filtering time constant , represents the transfer function of the filter, which represents the ratio of the output signal y(s) to the input signal x(s), so the low-pass filter can be obtained:

[0013] Where T is the sampling period; After finishing, we can get:

[0014] in:

[0015] The value range of the filter coefficient is: <a<1。

[0016] As a further solution of the present invention: the laser scanner is equipped with operating software, and the operating software has a visual interface for displaying the scanning progress generated by the laser scanner. The operating software displays the amount of data collected and the estimated total amount of data in real time, and completes the comparison between the amount of data collected and the estimated total amount of data.

[0017] As a further solution of the present invention: the OPC server completes the control of the vehicle-mounted controller and the drive system based on infrared communication, combines the vehicle-mounted controller and the drive system to complete the control of the AGV body and the mechanical body, completes the control of the AGV body and the industrial robot, controls the AGV body to transport parts, and synchronously controls the industrial robot to move and assemble parts.

[0018] As a further solution of the present invention: the discrete intelligent manufacturing method completes data monitoring of the production process of workshop equipment based on MES, and displays the monitored data based on a visualization device to complete visualization of the monitoring data.

[0019] A discrete intelligent manufacturing system, applied to the above-mentioned discrete intelligent manufacturing method, comprises: An assembly judgment module is used to scan and judge the assembled order products. The laser scanner is used to collect product data and display the formed three-dimensional model based on the visual interface. The three-dimensional model is used to compare with the created three-dimensional detailed model. The order information processing module is used to process the received orders, model the order products and split the production steps. The bill of materials breaks down the orders into production tasks and procurement tasks for each component, and determines the production quantity and time node for each component. The control module is used to control the equipment on the order product production line. The hardware driver is used to control the operation of the machine tool equipment and produce product parts. The OPC server and MES are used to issue and receive production tasks. The OPC server issues production tasks to the hardware driver, controller and ground control console, and is used to control the operation of the AGV body and industrial robot. The information warehousing module is used to process the warehousing of assembled order products and upload the warehousing information to the ERP system. The ERP system manages the quantity and batches of the warehousing order products. The ERP system displays the warehousing order products and the order products to be produced, and displays the production progress.

[0020] The beneficial effects of the present invention are as follows: 1. The discrete intelligent manufacturing method and system described in the present invention, through the mutual cooperation of the ERP system and the bill of materials, splits the materials required for the product order according to the quantity of the product order and lists the list, so that it is more convenient for the staff to purchase the materials required for the product, and through the mutual cooperation of the ERP system and the MES system, according to the materials listed on the bill of materials, the number and time nodes of the parts processed into the materials are determined, and the production steps are split, so that the parts are produced in batches during the production process, which is convenient for subsequent assembly, thereby improving the efficiency of discrete intelligent manufacturing for order product manufacturing; 2. The discrete intelligent manufacturing method and system described in the present invention, through the mutual cooperation between the MES system and the OPC server, controls the DNC controller through the hardware driver according to the issued production tasks, so that the machine tool equipment processes the parts, and simultaneously controls the AGV body and the mechanical body of the industrial robot through infrared communication, so that the production of ordered products can be multi-line parallel, further improving the efficiency of discrete intelligent manufacturing for order product manufacturing; 3. The discrete intelligent manufacturing method and system described in the present invention scans the assembled product through the setting of the assembly judgment module to form a three-dimensional model, compares the three-dimensional model with the three-dimensional detailed model of the order, judges the difference between the assembled product and the preset standard, and controls the quality of the ordered product when it is delivered. At the same time, through the setting of the filtering algorithm, the data scanned by the laser scanner is processed to make the data smoother and more accurately reflect the real surface conditions of the parts, so that the three-dimensional model is more accurate when compared with the three-dimensional detailed model; 4. A discrete intelligent manufacturing method and system described in the present invention, after comparing the three-dimensional model with the three-dimensional detailed model, the products that meet the preset standards are put into storage, the OPC server reads the RFID tag through the RFID reader, and transmits the read data to the ERP system, and the produced products are put into storage, so that the production progress can be displayed and managed, which is convenient for the staff to check the production progress; 5. The discrete intelligent manufacturing method and system described in the present invention, after comparing the three-dimensional model with the three-dimensional detailed model, determines the problem through the three-dimensional model, and changes the machine tool identification and the industrial robot's processing and assembly methods of parts according to the mutual cooperation between the MES system and the OPC server, thereby improving the product qualification rate, and moving defective products to other locations through the AGV body to prevent defective products from affecting the production of subsequent products. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] Figure 1 It is a flowchart of the present invention; Figure 2 is a flow chart of the present invention; Figure 3 It is a flowchart of determining product assembly in the present invention; Figure 4 It is a block diagram of a discrete intelligent manufacturing system in the present invention; Figure 5 yes Figure 2 Flow chart of the control module. DETAILED DESCRIPTION

[0023] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0024] The present invention provides a discrete intelligent manufacturing method and system, such as Figure 1-Figure 5 As shown, a discrete intelligent manufacturing method includes the following specific steps: S1: Split the product information in the order product to form a bill of materials, store the received order information based on the ERP software, and use computer-aided design software to create a 3D detailed model of the product based on the data in the bill of materials and split the parts of the 3D detailed model of the product to determine the production quantity and time node of each part. Receive the production data in ERP based on MES, and generate specific production tasks and issue them to each equipment in the workshop; S2: Use the OPC server to receive production tasks issued by MES, control the DNC controller based on the hardware driver, and the DNC controller controls the machine tool equipment and processes the parts. Based on the OPC server, commands are issued to the controller and ground control console to control the industrial robot and AGV body to complete the processing and assembly of parts; S3: After the parts are assembled, the industrial robot places the parts on the scanning platform and scans the parts based on the calibrated laser scanner. The noise points of the scanned data are removed based on the filtering algorithm, and the data obtained from multiple scans are aligned and spliced ​​to form a three-dimensional model, which is then compared with the three-dimensional detailed model created by the computer-aided design software; S4: Based on model comparison, determine the assembly effect of order parts, scan products that do not meet the requirements again, confirm the location of product defects, and determine and adjust the assembly steps in the product production process; S5: Products that meet the preset standards provide feedback on product data based on the OPC server, and transmit the feedback data to the ERP system to complete the warehousing processing of the production products and the production of the order products.

[0025] Computer-aided design software uses CAD to create three-dimensional modeling of products and accurately design each component of the product. MES monitors production status in real time, including but not limited to product parts production progress, equipment utilization, and quality status. By creating a three-dimensional model through CAD, product information can be observed more intuitively, so that the product can better meet the preset standards during production, and through real-time monitoring of MES, product production can be more precise.

[0026] MES and OPC servers cooperate with each other to issue control commands to the industrial robots and AGV bodies on the production line, complete the response of the industrial robots and AGV bodies to the production tasks issued by MES, complete the synchronous control and operation of machine tools, industrial robots and AGV bodies, and issue commands for the split production steps through MES for parts processing and production, so that the OPC service area can control the industrial robots and AGV bodies, automate the production and processing of parts, improve the efficiency of parts processing and assembly, and thus improve the production efficiency of products.

[0027] The ordered products are equipped with RFID tags. The RFID tags are read based on the OPC server combined with the RFID reader, and the industrial robots and AGV bodies are controlled to accurately pick up parts, complete the transportation and assembly of parts. The OPC server is used to read the RFID tags through the RFID reader, so that the OPC server can pick up and assemble parts more accurately when controlling the industrial robots and AGV bodies, thereby avoiding errors in parts assembly and improving the overall efficiency of product production.

[0028] The filtering algorithm is an RC filter, and the transfer function of the RC filter is:

[0029] in , R is the resistance value, C is the capacitance value, s is the complex frequency domain variable, the filtering effect of the RC filter depends on the filtering time constant , represents the transfer function of the filter, which represents the ratio of the output signal y(s) to the input signal x(s), so the low-pass filter can be obtained:

[0030] Where T is the sampling period; After finishing, we can get:

[0031] in:

[0032] The value range of the filter coefficient is: <a<1。

[0033] The above filtering algorithm is used to filter the data scanned by the laser scanner, so that the 3D scanned data can be smoother and accurately reflect the real surface conditions of the parts, thereby making the 3D model more accurate when compared with the 3D detailed model.

[0034] The laser scanner is equipped with operating software. The operating software has a visual interface that displays the scanning progress generated by the laser scanner. The operating software displays the amount of data collected and the estimated total data in real time, and completes the comparison of the collected data amount and the estimated total data amount. Through the setting of the visual interface, the progress observation of the laser scanner during the scanning process is more intuitive, and it is more convenient for the staff to compare the collected data amount with the prepaid data amount, thereby making the operation process of the laser scanner easier to control and facilitating the staff to inspect the assembled parts.

[0035] The OPC server completes the control of the on-board controller and drive system based on infrared communication, and completes the control of the AGV body and mechanical body in combination with the on-board controller and drive system, completes the control of the AGV body and industrial robot, controls the AGV body to transport parts, and synchronously controls the industrial robot to move and assemble parts. Through the setting of the OPC server, the on-board controller and drive system are controlled and information is fed back, so that when the on-board controller controls the AGV body and the mechanical body to control the industrial robot, it can not only complete the production tasks issued by MES, but also feedback the data after production is completed, making it more convenient for MES to monitor the progress of part production.

[0036] This discrete intelligent manufacturing method completes data monitoring of the production process of workshop equipment based on MES, and displays the monitored data based on visualization equipment to complete the visualization of monitoring data. By monitoring the data produced by workshop equipment through MES, the equipment can be more accurate in producing parts, avoiding problems arising in the production process. Through visualization settings, production data can be viewed more intuitively, making it convenient for staff to monitor the production process.

[0037] A discrete intelligent manufacturing system, applied to the above-mentioned discrete intelligent manufacturing method, comprises: The assembly judgment module is used to scan and judge the assembled order products. The laser scanner is used to collect product data and display the three-dimensional model formed based on the visual interface. The three-dimensional model is used to compare with the created three-dimensional detailed model. Through the assembly judgment module, the three-dimensional model of the assembled product is generated. Then, by comparing the three-dimensional model with the three-dimensional detailed model, it is judged whether the assembled product meets the preset standards, which avoids the simultaneous storage of defective products and finished products after production, which affects the quality of subsequent product delivery; The order information processing module is used to process received orders, model order products and split production steps. The bill of materials breaks down orders into production tasks and procurement tasks for each component, determines the production quantity and time node of each component, and through the setting of the order information processing module, models order products and sorts out the required parts to form a bill of materials, thereby facilitating procurement personnel to collect parts and materials, and through the decomposed production tasks, facilitates the subsequent production of products; The control module is used to control the equipment on the order product production line. The hardware driver is used to control the operation of machine tools and produce product parts. The OPC server and MES are used to issue and receive production tasks. The OPC server issues production tasks to the hardware driver, controller and ground control console, and is used to control the operation of the AGV body and industrial robot. Through the setting of the control module, the production equipment of the production line is controlled to complete the automated product production and improve the efficiency of product production. The information entry module is used to process the entry of assembled order products and upload the entry information to the ERP system. The ERP system manages the quantity and batches of the order products after entry. The ERP system displays the entry order products and the order products to be produced, and displays the production progress. Through the setting of the information entry module, the products after production are entered into the warehouse, and the production progress is displayed in real time, which is convenient for the staff to adjust the production speed later and ensure that the order completion time is more accurate, so that the production progress of the order products matches the plan, which facilitates the production of the order products.

[0038] The above-mentioned front, back, left, right, top and bottom are all based on the figures in the specification. Figure 1 As a benchmark, according to the person's observation perspective, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present invention.

[0040] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A discrete intelligent manufacturing method, characterized in that: The specific steps include: S1: Split the product information in the order product to form a bill of materials, store the received order information based on the ERP software, and use computer-aided design software to create a 3D detailed model of the product based on the data in the bill of materials and split the parts of the 3D detailed model of the product to determine the production quantity and time node of each part. Receive the production data in ERP based on MES, and generate specific production tasks and issue them to each equipment in the workshop; S2: Use the OPC server to receive production tasks issued by MES, control the DNC controller based on the hardware driver, and the DNC controller controls the machine tool equipment and processes the parts. Based on the OPC server, commands are issued to the controller and ground control console to control the industrial robot and AGV body to complete the processing and assembly of parts; S3: After the parts are assembled, the industrial robot places the parts on the scanning platform and scans the parts based on the calibrated laser scanner. The noise points of the scanned data are removed based on the filtering algorithm, and the data obtained from multiple scans are aligned and spliced ​​to form a three-dimensional model, which is then compared with the three-dimensional detailed model created by the computer-aided design software; S4: Based on model comparison, determine the assembly effect of order parts, scan products that do not meet the requirements again, confirm the location of product defects, and determine and adjust the assembly steps in the product production process; S5: Products that meet the preset standards provide feedback on product data based on the OPC server, and transmit the feedback data to the ERP system to complete the warehousing processing of the production products and the production of the order products.

2. A discrete intelligent manufacturing method according to claim 1, characterized in that: The computer-aided design software uses CAD to create three-dimensional modeling of products and accurately designs various parts of products. The MES monitors the production status in real time, including but not limited to the production progress of product parts, equipment utilization, and quality status.

3. A discrete intelligent manufacturing method according to claim 2, characterized in that: The MES and OPC server cooperate with each other to issue control commands to the industrial robots and AGV bodies on the production line, complete the response of the industrial robots and AGV bodies to the production tasks issued by MES, complete the synchronous control and operation of machine tools, industrial robots and AGV bodies, and process and produce parts.

4. A discrete intelligent manufacturing method according to claim 3, characterized in that: The ordered products are provided with RFID tags, which are read based on the OPC server combined with the RFID reader, and the industrial robot and AGV body are controlled to accurately pick up parts, thereby completing the transportation and assembly of parts.

5. A discrete intelligent manufacturing method according to claim 4, characterized in that: The filtering algorithm is an RC filter, and the transfer function of the RC filter is: in , R is the resistance value, C is the capacitance value, s is the complex frequency domain variable, the filtering effect of the RC filter depends on the filtering time constant , represents the transfer function of the filter, which represents the ratio of the output signal y(s) to the input signal x(s), so the low-pass filter can be obtained: Where T is the sampling period; After finishing, we can get: in: , where the filter coefficient range is: 0 <a<1。 6. A discrete intelligent manufacturing method according to claim 5, characterized in that: The laser scanner is equipped with operating software, which has a visual interface and displays the scanning progress generated by the laser scanner. The operating software displays the amount of data collected and the estimated total amount of data in real time, and completes the comparison between the amount of data collected and the estimated total amount of data.

7. A discrete intelligent manufacturing method according to claim 6, characterized in that: The OPC server completes the control of the vehicle controller and the drive system based on infrared communication, and completes the control of the AGV body and the mechanical body in combination with the vehicle controller and the drive system, completes the control of the AGV body and the industrial robot, controls the AGV body to transport parts, and synchronously controls the industrial robot to move and assemble parts.

8. A discrete intelligent manufacturing method according to claim 7, characterized in that: The discrete intelligent manufacturing method completes data monitoring of the production process of workshop equipment based on MES, and displays the monitored data based on visualization equipment to complete visualization of the monitoring data.

9. A discrete intelligent manufacturing system, applied to the discrete intelligent manufacturing method according to claim 7, characterized in that: include: An assembly judgment module is used to scan and judge the assembled order products. The laser scanner is used to collect product data and display the formed three-dimensional model based on the visual interface. The three-dimensional model is used to compare with the created three-dimensional detailed model. The order information processing module is used to process the received orders, model the order products and split the production steps. The bill of materials breaks down the orders into production tasks and procurement tasks for each component, and determines the production quantity and time node for each component. The control module is used to control the equipment on the order product production line. The hardware driver is used to control the operation of the machine tool equipment and produce product parts. The OPC server and MES are used to issue and receive production tasks. The OPC server issues production tasks to the hardware driver, controller and ground control console, and is used to control the operation of the AGV body and industrial robot. The information warehousing module is used to process the warehousing of assembled order products and upload the warehousing information to the ERP system. The ERP system manages the quantity and batches of the warehousing order products. The ERP system displays the warehousing order products and the order products to be produced, and displays the production progress.

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