Intelligent production line for manufacturing new energy automobile support parts and operation method and application of intelligent production line

Abstract

The invention relates to the technical field of high-end equipment manufacturing, and discloses an intelligent production line for new energy automobile support part manufacturing and an operation method and application of the intelligent production line. The production line comprises a storage and dispatching unit, an AGV logistics and transfer unit, a 3D visual recognition and positioning unit, a self-adaptive intelligent clamping unit, a multi-axis numerical control machining center unit and an online detection and quality feedback unit. The production line carries out global optimization production scheduling through a central scheduling model, rapid remodeling and accurate clamping are achieved through 3D vision and a flexible clamp, parameters are adjusted in real time through a self-adaptive model according to force and vibration signals in the machining process, and a quality closed loop is formed through online detection. The problems of frequent die change, low efficiency and poor precision consistency in a multi-variety small-batch production mode are effectively solved, high-degree flexible production is realized, the production efficiency and the product quality stability are remarkably improved, and the dependence on manpower is reduced.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of high-end equipment manufacturing, and in particular to an intelligent production line for manufacturing new energy automobile support parts and an operation method and application thereof. BACKGROUND

[0002] The rapid development of new energy automobiles has greatly driven the market demand for support parts (such as battery pack supports, motor suspension supports and vehicle body connecting supports) which are key structural parts. Such parts usually belong to the typical "multi-variety, small-batch" production category, because: first, the structures of different vehicle models and different battery pack designs are different, resulting in great differences in the size, shape and hole layout of the supports; second, in order to adapt to the rapid iteration of vehicle design, the production batch is small and the modification is frequent. At present, the manufacturing of such parts is mainly applied to the parts factories of new energy automobile manufacturers, professional large stamping and welding factories and precision machining enterprises.

[0003] In the specific production process, the traditional mode mainly relies on a discrete manufacturing unit of "general numerical control machine tool + special tooling fixture + manual operation". A typical production line usually includes: blanking (laser cutting or sawing) -> initial forming (stamping or bending) -> machining (numerical control milling machine / machining center for hole system, contour and plane finishing) -> deburring -> detection -> surface treatment (outsourcing or another line). Among them, machining is the core link to ensure the assembly accuracy and interchangeability of the parts. At present, in order to cope with multi-variety demand, the factory usually designs and manufactures special gauges and fixtures for each type of support, and on the same or several machining centers, the production switching is carried out by manually replacing the fixture and calling the corresponding machining program.

[0004] Although the above mode is the general practice in the industry, it exposes many technical problems that need to be solved in actual application: Firstly, the existing system has low production efficiency due to frequent mold changing in multi-product machining. "Multi-variety, small-batch" means that the production line needs to frequently switch between the production tasks of different parts; each switching involves a series of preparations such as physical mold changing (dismantling old fixtures, installing and debugging new fixtures), program calling, tool setting. These activities occupy a lot of non-processing time, resulting in a generally low overall equipment effectiveness (OEE), usually less than 50%. Frequent start-stop also affects the service life and rhythm of production; Secondly, the existing system's multi-product processing process has high dependence on artificial skills, and the precision consistency is difficult to guarantee, especially the installation precision of the fixture, the alignment and clamping of the workpiece, and the setting of the coordinate system, which highly depend on the experience and technical level of the operators, and there are many manual intervention links, which introduces a large number of uncontrollable variation factors, easily leading to size precision fluctuation within or between batches, and it is difficult to meet the high reliability requirements of new energy vehicles on key structural parts. Especially after the mold is changed, the first piece debugging and testing are time-consuming and labor-intensive, which affects the production rhythm; Thirdly, the existing system has the problems of complex production management and scheduling and low informatization degree, because there is no unified production control system, the order progress, material state, equipment state and quality information are in an island state; the production planner arranges production by experience, and it is difficult to accurately predict and optimize the balance of mold changing time and processing time, which easily leads to queuing and waiting of some devices on the production line, while some devices are idle, the overall production cycle is long, and the work-in-process inventory is high. In the face of emergency orders and the like, the response is slow; Finally, the existing system's processing process lacks monitoring and is rigid. It is known that traditional numerical control machining strictly follows the preset program, and lacks real-time perception and feedback of the processing process; when encountering uneven blank allowance, tool micro-wear, slight change in clamping force and the like, it cannot be adjusted adaptively, which may affect the surface quality, or even cause tool breakage, workpiece scrap and equipment damage, and the process robustness is poor.

[0005] Therefore, the technical personnel in the field urgently need an intelligent manufacturing production line that can deeply integrate automation technology, information technology and intelligent decision-making technology. SUMMARY

[0006] The purpose of the present application is to solve the above problems, and an intelligent production line for manufacturing new energy automobile support parts and an operation method and application thereof are designed.

[0007] To achieve the above purpose, the technical scheme of the present application is an intelligent production line for manufacturing new energy automobile support parts, which comprises a warehouse and scheduling unit, an AGV logistics and transfer unit, a 3D visual identification and positioning unit, an adaptive intelligent clamping unit, a multi-axis numerical control machining center unit and an online detection and quality feedback unit connected in sequence; wherein, The warehouse and scheduling unit is used for receiving orders and scheduling production, and scheduling material and workpiece flow; The AGV logistics and transfer unit is used for automatically transferring materials and workpieces between units according to scheduling instructions; The 3D visual identification and positioning unit is used for identifying the model of the workpiece and obtaining its three-dimensional pose data; The adaptive intelligent clamping unit is used for automatically grabbing and clamping the workpiece according to the three-dimensional pose data; The multi-axis numerical control machining center unit is used for machining the clamped workpiece and adjusting the machining parameters in real time, and is integrated with a force sensor and a vibration sensor; The online detection and quality feedback unit is used for online measurement of the machined workpiece and feedback of the results.

[0008] The warehouse and scheduling unit includes a stereoscopic warehouse and a central production scheduling system, and the central production scheduling system runs a production scheduling optimization model, and the objective function of the model is: In the formula, T total is the total production cycle (targeting for minimization), i is the order index (i = 1, 2,..., N), j is the workstation / equipment index (j = 1, 2,..., M), t setup,ij is the die changing / preparation time of order i on equipment j, X ij is a binary decision variable (1 if order i is processed on equipment j, otherwise 0), t process，ij is the net processing time of order i on equipment j, k is the workpiece / semi-finished product index, P k is the priority weight of workpiece k, t delay，k is the waiting / delay time of workpiece k.

[0009] The adaptive intelligent clamping unit includes a six-axis robot and a flexible clamp, and the flexible clamp is a reconfigurable vice or a multi-point array vacuum suction cup, which can automatically adjust the clamping surface according to the workpiece profile.

[0010] The multi-axis numerical control machining center unit can run an adaptive machining parameter compensation model, and the expression of the model is: In the formula, F adj is the adjusted spindle feed rate, F base is the preset standard feed rate, ΔV is the deviation of the real-time vibration amplitude from the reference value, V threshold is the maximum allowable vibration deviation threshold, |ΔF| is the absolute deviation of the real-time cutting force amplitude from the reference value, F threshold is the maximum allowable cutting force deviation threshold, and α and β are weight coefficients (α + β = 1), representing the influence weight of vibration and cutting force on process stability, respectively.

[0011] An operation method of an intelligent production line, the operation method comprising the following steps: S1: The central production scheduling system receives an order, runs a production scheduling optimization model, and generates a production instruction; S2: AGV logistics and transfer unit transports raw materials to the 3D visual recognition and positioning unit according to the instructions; S3: The 3D visual recognition and positioning unit locates the workpiece and obtains pose data, which are then sent to the adaptive intelligent clamping unit; S4: The adaptive intelligent clamping unit automatically grabs and clamps the workpiece to the multi-axis numerical control machining center unit; S5: The multi-axis numerical control machining center unit executes machining and adjusts the machining parameters in real time based on the adaptive machining parameter compensation model; S6: After machining is completed, the workpiece is transferred to the online detection and quality feedback unit for measurement, and the results are fed back to the central production scheduling system.

[0012] The process of real-time adjustment of machining parameters is as follows: real-time acquisition of cutting force and vibration signals, calculation of the deviation of cutting force and vibration signals from the reference value, and automatic adjustment of the spindle feed rate F adj , when the deviation approaches the preset threshold value.

[0013] A production scheduling optimization system, which comprises the following modules: A data input module for obtaining order information, equipment information and work time information; A model processing module with a production scheduling optimization model for optimizing production scheduling plans; An instruction output module for issuing optimized production scheduling plans to each execution unit of the intelligent production line.

[0014] An adaptive machining control system, which comprises the following modules: A signal acquisition module for real-time acquisition of spindle vibration signals and cutting force signals; A calculation module for calculating the deviation ΔV and |ΔF| of the above-mentioned spindle vibration signals and cutting force signals from the reference value, and calculating the adjusted spindle feed rate F adj based on the adaptive machining parameter compensation model. A control module for issuing the F adj to the multi-axis numerical control machining center unit and real-time controlling the machining process.

[0015] A computer readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the steps of the operation method.

[0016] An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being capable of implementing the steps of the operation method when executing the computer program.

[0017] Compared with the prior art, the present application has the following non-obvious technical features: Firstly, the present application realizes a "dispatch-logistics-execution-detection" whole-process model driven closed loop, and the prior art is mostly single-point automation or simple connection; and the present application deeply integrates a central dispatch model (global optimization), AGV path planning, an adaptive processing model (local optimization) and online detection feedback through a unified data platform to form a closed loop intelligent system of "perception-decision-execution-learning", and realizes the whole-process intelligentization from an order to a finished product, and the overall architecture has non-obviousness. Secondly, the present application adopts a "zero die changing" intelligent clamping technology based on 3D vision and flexible clamps, and a traditional scheme is "one part one clamp"; and the present application creatively combines 3D vision identification positioning and a reconfigurable flexible clamp, guides a robot through vision, enables the clamp to automatically adapt to parts of different shapes, realizes "die changing" at a software level, and greatly shortens the physical preparation time, and the combined application mode has creativity. Thirdly, the present application integrates an online adaptive processing parameter compensation model of multi-source sensing data, and the prior art is mostly open loop program control, and the adaptive model proposed by the present application can simultaneously process two key physical signals of vibration and cutting force, and dynamically and quantitatively adjusts a feed rate to pursue optimal efficiency under the premise of ensuring process stability, and the multi-parameter fusion real-time control strategy based on a specific process (support machining) has non-obviousness. Finally, the present application introduces a priority weighted delay penalty term in the dispatch model, and a general dispatch model takes minimizing total time or maximum completion time as a target, and the present application adds a Σ k P k * t delay，k term in the objective function, so that the dispatch strategy can not only improve overall efficiency, but also take into account the urgency of different orders / workpieces (such as trial production and supplementary production), and is more in line with actual production management needs, and the modeling method has creativity.

[0018] Compared with the prior art, the present application has the following beneficial effects: 1、The present application can significantly improve the production efficiency and flexibility of the system, reduces equipment waiting through intelligent dispatch, greatly shortens the preparation time through the "zero die changing" clamping technology, optimizes the cutting parameters through adaptive processing, and comprehensively realizes more than 50% improvement of production efficiency, and can quickly respond to multi-variety and variable-batch production demands; 2、The present application can greatly improve product quality and consistency, reduce manual intervention links, eliminate human errors through automatic positioning, online automatic detection and feedback loop, ensure the consistency and stability of machining precision, and significantly improve product qualification rate; 3、The application effectively reduces operating costs and dependence on skilled workers, has high automation, reduces the number of required operators, and reduces the skill requirements for personnel. At the same time, the improvement of equipment utilization (OEE) and the reduction of scrap rate further reduce the cost of single piece; 4、The application realizes the digitization and transparency of the production process, full-process data acquisition and monitoring, so that the production progress, equipment status and quality data are clear at a glance, providing a data basis for lean management, predictive maintenance and continuous process optimization; 5、The application enhances the process robustness and safety of the system, and can prevent processing failures caused by abnormal working conditions through the adaptive processing model, protecting expensive tools and equipment and improving the operation reliability of the entire production system. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a schematic diagram of the intelligent production line for manufacturing new energy automobile support parts according to the application; Figure 2 is a running parameter table of the intelligent production line according to embodiment 1 of the application; Figure 3 is a comparison table of technical effects achieved by the intelligent production line according to embodiment 1 of the application; Figure 4 is a flowchart of the operation method according to embodiment 2 of the application; Figure 5 is a time parameter table of the operation method according to embodiment 2 of the application; Figure 6 is a comparison table of technical effects achieved by the operation method according to embodiment 2 of the application; Figure 7 is a schematic diagram of the production scheduling optimization system according to embodiment 3 of the application; Figure 8 is a running parameter table of the production scheduling optimization system according to embodiment 3 of the application; Figure 9 is a schematic diagram of the adaptive processing control system according to embodiment 4 of the application; Figure 10 is a running parameter table of the adaptive processing control system according to embodiment 4 of the application. DETAILED DESCRIPTION

[0020] The technical solutions of the application will be described in detail below with reference to the accompanying drawings; An intelligent production line for manufacturing new energy automobile support parts, as shown in Figure 1 The production line is a flexible manufacturing system that cooperates with multiple intelligent functional units, and includes the following parts: A warehouse and scheduling unit, which mainly serves as a material inlet and scheduling center. Different specifications of raw materials, semi-finished products and finished products are stored in the stereoscopic warehouse; orders are received through the central production scheduling system, production scheduling is carried out according to the production scheduling optimization model, and instructions are issued to each unit; the mathematical expression (i.e. objective function) of the production scheduling optimization model is: In the formula, T total is the total production cycle (target is to minimize), i is the order index (i = 1, 2,..., N), j is the workstation / equipment index (j = 1, 2,..., M), t setup,ij is the order i on the equipment j change mode / preparation time, X ij is a binary decision variable (1 if order i is processed on equipment j, otherwise 0), t process，ij is the net processing time of order i on equipment j, k is the workpiece / semi-finished product index, P k is the priority weight of workpiece k, t delay，k is the waiting / delay time of workpiece k; it should be noted that this unit is the "brain" of the production line, and its output instructions drive the AGV unit and all subsequent processing units.

[0021] AGV logistics and transfer unit, which mainly uses automatic guided vehicles to transport raw materials from the warehouse to the clamping station according to the scheduling instructions, and automatically transfers workpieces between processing units and between processing units and detection units.

[0022] This unit is the "blood vessel" connecting the warehouse, processing and detection units, receiving instructions from the warehouse and scheduling unit, and providing services for subsequent units, including 3D visual recognition and positioning unit, adaptive intelligent clamping unit and multi-axis numerical control machining center unit.

[0023] 3D visual recognition and positioning unit, which mainly scans the blank or semi-finished product through 3D visual camera before clamping to obtain its accurate three-dimensional point cloud data, identifies its model, attitude and possible machining allowance deviation. This unit mainly provides accurate positioning and compensation data input for the adaptive intelligent clamping unit.

[0024] The adaptive intelligent clamping unit includes flexible fixtures (such as reconfigurable vises and multi-point vacuum suction cups) and a six-axis robot. Based on the model and posture data provided by the 3D vision recognition and positioning unit, the robot automatically calls the corresponding fixture scheme to grab the workpiece and clamp it onto the universal pallet of the machining center. The fixture can adaptively adjust according to the contour of different support parts. This unit can receive materials from the AGV logistics and transfer unit and data sent by the 3D vision recognition and positioning unit, and provide the multi-axis CNC machining center unit with the precisely positioned workpiece to be processed.

[0025] The multi-axis CNC machining center unit, as the core machining unit of the entire system, is equipped with a high-speed electric spindle, a multi-axis linkage system, and a tool magazine. It automatically changes tools according to the order program to complete milling, drilling, tapping, and other processes. Integrated force and vibration sensors are used for real-time monitoring of the machining status. This unit incorporates an adaptive machining parameter compensation model, the mathematical expression of which is: In the formula, F adj F is the adjusted spindle feed rate. base The preset standard feed rate is given, and ΔV is the deviation between the real-time vibration amplitude and the reference value. threshold The maximum permissible vibration deviation threshold is defined by |ΔF|, which represents the absolute deviation between the real-time cutting force amplitude and the reference value. threshold The maximum allowable cutting force deviation threshold is defined by α and β, which are weighting coefficients (α + β = 1), representing the weights of vibration and cutting force on process stability, respectively.

[0026] It should be noted that this unit clamps the workpiece using an adaptive intelligent clamping unit, executes machining instructions, and feeds back the machining process data to the central system.

[0027] The online inspection and quality feedback unit's main function is to rapidly inspect the critical dimensions of workpieces using an online coordinate measuring machine or high-precision laser scanner after key processes or machining, and upload the inspection data to the central system in real time. This unit can receive workpieces processed by the multi-axis CNC machining center and inspect them, then feed the inspection results back to the warehousing and scheduling unit and the multi-axis CNC machining center, forming a quality closed loop. Qualified products are sent to the warehouse, while unqualified products trigger an alarm or enter the rework process.

[0028] In this system, the warehousing and scheduling unit acts as the command center, planning the entire production process. The AGV logistics and transfer unit executes logistics handling according to the plan. Materials first arrive at the 3D vision recognition and positioning unit for identification and positioning, and then the adaptive intelligent clamping unit completes intelligent clamping. Next, the multi-axis CNC machining center unit performs processing and uses an adaptive model to optimize the processing process. Finally, the online detection and quality feedback unit verifies the processing results and feeds the data back to the warehousing and scheduling unit to optimize subsequent scheduling and quality control, forming a complete closed loop of "perception-decision-execution-feedback".

[0029] The specific working process of this production line is as follows: The first step is order input and scheduling. The central production scheduling system (warehousing and scheduling unit) receives production orders containing various support parts. The scheduling optimization model runs, generates the optimal production sequence and mold change plan, and issues instructions to the automated warehouse and AGVs (AGV logistics and transfer unit). The second step is material outbound and transfer. The AGV (AGV logistics and transfer unit) takes out the corresponding raw materials or semi-finished blanks from the automated warehouse according to the instructions and transports them to the vision recognition station (3D vision recognition and positioning unit). The third step is workpiece identification and positioning. The 3D vision system (3D vision identification and positioning unit) scans the blank, generates a point cloud model, matches it with the standard CAD model, identifies its specific model and spatial pose, and sends the data to the clamping unit (adaptive intelligent clamping unit). The fourth step is adaptive clamping: Based on the received model data, the robot (adaptive intelligent clamping unit) selects and configures the corresponding gripper or clamping module from the fixture library. Simultaneously, based on the pose data provided by vision, it plans the gripping path, accurately grips the workpiece, and installs it onto the flexible pallet of the machining center (multi-axis CNC machining center unit), completing the automatic setting of the zero point and coordinate system. Step 5, Intelligent Machining and Process Optimization: The machining center (multi-axis CNC machining center unit) calls the machining program for the corresponding part and begins machining. During the process, force and vibration sensors collect data in real time. The adaptive machining parameter compensation model runs in real time; if ΔV or |ΔF| approaches the threshold, F is automatically adjusted downwards. adj To prevent chatter or tool overload; if the condition is stable, increase F appropriately. adj To improve efficiency; Step 6, Online Inspection and Feedback: After one process or all processing is completed, the AGV (AGV logistics and transfer unit) transfers the workpiece to the online inspection station (online inspection and quality feedback unit). The inspection unit quickly measures key dimensions, and the data is uploaded in real time. If it passes the inspection, the workpiece is sent to the finished product warehouse; if it exceeds the tolerance but can be reworked, the system marks it and arranges rework; if it is scrapped, an alarm is triggered and the raw material replenishment process is initiated. The inspection data is also used to update the tool wear compensation parameters and is fed back to the machining unit (multi-axis CNC machining center unit). Step 7, Closed Loop and Iteration: All production data (processing time, mold changeover time, quality data, equipment status) is collected in the central scheduling system (warehousing and scheduling unit). The scheduling model uses this actual data to continuously learn and optimize, providing more accurate time-to-schedule data for subsequent order scheduling. setup and t process It is anticipated that production efficiency will continue to improve.

[0030] It is important to note that the independent variable in the production scheduling optimization model is the binary decision variable X. ij (Indicates whether order i is assigned to device j), and by X ij The determined order processing sequence; the dependent variable is the total production cycle T. total The model was established as follows: First, the problem is defined, and the objective is to minimize the total completion time under the conditions of multiple devices, multiple orders, and mold changeover time. Secondly, parameterization involves collecting historical data, defining N orders and M devices, and determining the time (t) for each order on each device. process t setup And the order priority P k ; Third, formalize the constraints by listing them, such as: each order must be completed, the equipment can only process one order at a time, and the process route constraints for the order, etc. Fourth, construct the objective function by expressing the total cycle as the sum of the mold change time, processing time, and weighted delay time caused by improper scheduling for all orders, thus obtaining the expression.

[0031] In the production scheduling optimization model, the total time consists of effective operation time and non-value-added waiting time, i.e., Σ i Σ j t setup，ij * X ij + t process_ij It is the sum of the necessary processing time for all orders; Σ k P k *t delay_k This is a penalty term used to quantify the losses caused by high-priority orders waiting due to unreasonable scheduling. By solving for T...total The smallest X ij By combining these methods, the optimal production scheduling plan can be obtained. This is typically achieved using intelligent optimization algorithms such as genetic algorithms and simulated annealing.

[0032] Furthermore, the independent variables of the adaptive machining parameter compensation model are the real-time monitored vibration deviation ΔV and cutting force deviation |ΔF|, and the dependent variable is the adjusted spindle feed rate F. adj The model was established as follows: First, data acquisition was performed. Under stable machining conditions, vibration signals V were collected under different materials, tools, and cutting parameters. base and cutting force signal F base As a benchmark for health; Secondly, the threshold is determined through process experiments or simulations to determine the V value that ensures machining quality and tool safety. threshold and F threshold ; Third, relationship modeling is used, assuming that the adjustment of the feed rate is linearly negatively correlated with the degree of deviation of vibration and cutting force; weighting coefficients α and β are introduced to reflect the degree of influence of vibration and force on stability in different processes (e.g., α is larger during finishing and β is larger during heavy cutting).

[0033] In the adaptive machining parameter compensation model, the core idea is to dynamically balance efficiency and stability; the expression [α * (1 - (ΔV / V)] threshold )) + β * (1 - (|ΔF| / F threshold ))] is a dynamic adjustment coefficient between 0 and 1; when ΔV and |ΔF| are both 0 (ideal state), the coefficient is 1, and F adj = F base ; As ΔV or |ΔF| increases, the coefficient decreases, and F... adj The ratio is reduced proportionally to suppress the unstable state; When any deviation reaches the threshold, the coefficient drops to α or β, ensuring that the feed rate will not be zero and cause processing interruption, but has been reduced to a safe level.

[0034] This model enables adaptive fine-tuning based on process state.

[0035] Example 1; An intelligent production line for manufacturing bracket parts for new energy vehicles is presented in this embodiment. This embodiment constructs a complete intelligent production line for producing three typical new energy vehicle bracket parts: battery pack brackets, motor mount brackets, and body connection brackets. The production line principle is as follows: Figure 1 As shown, it includes the following units: Warehousing and Scheduling Unit: The automated warehouse uses high-bay racking to store aluminum profile blanks and semi-finished products. The central scheduling system runs a production scheduling optimization model and generates a production plan after receiving orders. AGV logistics and transfer unit: Utilizes laser-guided AGVs with a load capacity of 500kg, which transport materials according to scheduling instructions; 3D Vision Recognition and Positioning Unit: Uses a binocular structured light camera to scan the blank and generate point cloud data to identify the workpiece model and pose; Adaptive intelligent clamping unit: It adopts a six-axis robot (load 20kg) with a reconfigurable vise, which automatically adjusts the clamping surface according to visual data; Multi-axis CNC machining center unit: adopts a five-axis linkage machining center, equipped with force sensors and vibration sensors, and runs an adaptive machining parameter compensation model; Online inspection and quality feedback unit: Equipped with an online coordinate measuring machine, it inspects key dimensions and provides real-time feedback to the central system.

[0036] The operating parameters of Example 1 are as follows: Figure 2 As shown, the technical effects it achieves are as follows: Figure 3 As shown.

[0037] Example 2; An operation method for an intelligent production line, such as Figure 4 As shown, this method takes a mixed order containing 5 types of bracket parts as an example, and its operation process is as follows: S1: Order scheduling. The central scheduling system receives orders, runs the optimization model, and generates the production sequence: Bracket A → Bracket C → Bracket B → Bracket E → Bracket D; S2: Logistics transfer, AGV takes out the corresponding aluminum blank from the automated warehouse and transports it to the vision workstation; S3: Visual recognition, the 3D camera scans the blank and identifies it as "bracket A", and the pose data is sent to the clamping unit; S4: Adaptive clamping, the robot calls the preset clamping scheme, grabs and clamps the processing center pallet; S5: Intelligent machining. The machining center executes a preset program and collects vibration and cutting force data in real time. When the vibration deviation ΔV reaches 80% of the threshold, the adaptive model adjusts the feed rate from F... base =1000 mm / min adjusted to F adj =850 mm / min; S6: Online inspection and feedback. After processing, the workpiece is transferred to the inspection unit. Once the key dimensions are qualified, it is put into storage. The data is fed back to the scheduling system to optimize subsequent orders.

[0038] The operating parameters of Example 2 are as follows: Figure 5 As shown, the technical effects it achieves are as follows:Figure 6 As shown.

[0039] Example 3; A production scheduling optimization system, such as Figure 7 As shown, the system is deployed in the warehousing and scheduling unit and includes the following modules: Data input module: Receives order information from the ERP system, obtains equipment status from the MES system, and retrieves working hour data from the historical database; Model processing module: Built-in genetic algorithm solves the production scheduling optimization model. Model parameters are as follows: number of orders N=10, number of devices M=4, priority P... k Divided into: High = 3, Medium = 2, Low = 1; Command output module: Sends the optimized production schedule to execution units such as AGVs and machining centers.

[0040] The operating parameters of Example 3 are as follows: Figure 8 As shown.

[0041] Example 4; An adaptive machining control system, such as Figure 9 As shown, this system embeds a multi-axis CNC machining center unit to achieve real-time parameter adjustment during the machining process. The system includes the following modules: Signal acquisition module: Acquires cutting force and vibration signals through piezoelectric force sensors and accelerometers, with a sampling frequency of 10kHz; Calculation module: Calculates ΔV and |ΔF| in real time and substitutes them into the adaptive machining parameter compensation model. Where: α=0.6, β=0.4 (finishing scenario); Control module: Calculates F adj The data is sent to the CNC system to achieve dynamic adjustment of the feed rate.

[0042] The operating parameters of Example 4 are as follows: Figure 10 As shown. The above technical solution only embodies the preferred technical solution of the present invention. Any modifications that may be made by those skilled in the art to certain parts thereof embody the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. An intelligent production line for manufacturing new energy automobile support parts, characterized in that, The system comprises a warehouse and scheduling unit, an AGV logistics and transfer unit, a 3D visual recognition and positioning unit, an adaptive intelligent clamping unit, a multi-axis numerical control machining center unit and an online detection and quality feedback unit connected in sequence, wherein, The warehouse and scheduling unit is used for receiving orders and scheduling production, and scheduling material and workpiece flow; The AGV logistics and transfer unit is used for automatically transferring materials and workpieces between units according to scheduling instructions; The 3D visual recognition and positioning unit is used for identifying the model of the workpiece and obtaining its three-dimensional pose data; The adaptive intelligent clamping unit is used for automatically grabbing and clamping the workpiece according to the three-dimensional pose data; The multi-axis numerical control machining center unit is used for processing the clamped workpiece and adjusting the processing parameters in real time; The online detection and quality feedback unit is used for measuring the processed workpiece online and feeding back the results.

2. The smart production line of claim 1, wherein, The warehouse and scheduling unit comprises a stereoscopic warehouse and a central production scheduling system, and the central production scheduling system runs a production scheduling optimization model, and the objective function of the model is: where T total is the total production cycle, i is the order index, j is the station / equipment index, t setup,ij is the setup / preparation time for order i on equipment j, X ij is a binary decision variable, t process，ij is the net processing time for order i on equipment j, k is the workpiece / semi-finished product index, P k is the priority weight for workpiece k, t delay，k is the waiting / delay time for workpiece k.

3. The smart production line of claim 1, wherein, The adaptive intelligent clamping unit comprises a six-axis robot and a flexible clamp, and the flexible clamp is a reconfigurable vice or a multi-point array vacuum suction cup.

4. The smart production line of claim 1, wherein, The multi-axis numerical control machining center unit can run an adaptive processing parameter compensation model, and the expression of the model is: In the formula, F adj is the adjusted spindle feed rate, F base is the preset standard feed rate, ΔV is the deviation of the real-time vibration amplitude from the reference value, V threshold is the allowable maximum vibration deviation threshold, |ΔF| is the absolute deviation of the real-time cutting force amplitude from the reference value, F threshold is the allowable maximum cutting force deviation threshold, and α and β are weight coefficients.

5. A method of operating an intelligent production line, characterized by, The operation method is used for operating the intelligent production line of any one of claims 1-4, and comprises the following steps: S1: The central production scheduling system receives orders, runs a production scheduling optimization model, and generates production instructions; S2: The AGV logistics and transfer unit transports raw materials to the 3D visual recognition and positioning unit according to the production instructions; S3: The 3D visual recognition and positioning unit positions the workpiece and obtains the pose data, and then sends it to the adaptive intelligent clamping unit; S4: The adaptive intelligent clamping unit automatically grabs and clamps the workpiece to the multi-axis numerical control machining center unit; S5: The multi-axis numerical control machining center unit executes processing and adjusts the processing parameters in real time based on the adaptive processing parameter compensation model; S6: After processing is completed, the workpiece is transferred to the online detection and quality feedback unit for measurement, and the results are fed back to the central production scheduling system.

6. The method of operation of claim 5, wherein, The process of adjusting the processing parameters in real time is: real-time acquisition of cutting force and vibration signals, calculation of the deviation of the cutting force and vibration signals from the reference value, and automatic adjustment of the spindle feed rate when the deviation approaches the preset threshold.

7. A production dispatch optimization system, characterized by, The system is applied to the warehouse and scheduling unit of the intelligent production line of any one of claims 1-4, and comprises the following modules: A data input module is used to obtain order information, equipment information and work time information; A model processing module is built-in with a production scheduling optimization model for optimizing production scheduling plan; An instruction output module is used to issue the optimized production scheduling plan to each execution unit of the intelligent production line.

8. An adaptive machining control system, characterized by, The system is applied to the multi-axis numerical control machining center unit of the intelligent production line of any one of claims 1-4, and comprises the following modules: A signal acquisition module is used to acquire spindle vibration signals and cutting force signals in real time; A computing module is configured to calculate the deviation of the spindle vibration signal and the cutting force signal from the reference value, and calculate an adjusted spindle feed rate based on an adaptive machining parameter compensation model; A control module is configured to issue the adjusted spindle feed rate to the multi-axis numerical control machining center unit, and control the machining process in real time.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, can implement the steps of the operation method in claim 5 or 6.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the computer program, can implement the steps of the operation method in any one of claims 5 or 6.

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