Intelligent forging production line and whole-line trajectory planning method

Abstract

The invention provides an intelligent forging production line and a whole line trajectory planning method, and belongs to the technical field of intelligent forging. A blank making press, a forging manipulator, a die forging press, a blank heating furnace, a secondary heating furnace, a blank placing area, a workpiece placing area and a die heating furnace are arranged around the moving guide rail. According to the method, whole-line trajectory planning is conducted on the intelligent forging production line, time distribution and total time limitation of each working procedure are determined, a main program is optimized by executing whole-line time distribution, time distribution of all sub-stages of the whole forging production line is planned, and operation space and joint space of the forging transfer robot are planned. Therefore, by optimizing the movement conditions and carrying tracks of all the joints, automation, unmanned operation and intelligentization of the forging production process are achieved, and the operation stability and production efficiency of the whole forging production line are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent forging, and particularly relates to an intelligent forging production line and a whole-line trajectory planning method. BACKGROUND

[0002] As a key link in the field of metal processing, forging is widely used in the industries of aerospace, automobile, ship, engineering machinery and the like, and is often used for manufacturing key parts with heavy work load, complex shape and harsh working environment.

[0003] At present, the traditional forging production line generally relies on manual operation and experience accumulation, and has many defects: firstly, manual operation is low in efficiency, relies on the experience and physical strength of workers, is high in labor intensity, and is difficult to improve the production speed; secondly, manual operation is poor in consistency, and the product quality is greatly affected by the technical level and fatigue degree of workers, so it is difficult to guarantee the product precision and quality stability; in addition, there are problems such as low equipment utilization rate and unstable whole-line operation. Especially in a complex process flow, the frequency of manual intervention is high, which not only increases the labor intensity of workers, but also causes the workers to work in a harsh environment of high temperature and high pressure, and easily causes safety accidents. SUMMARY

[0004] The application aims to provide an intelligent forging production line and a whole-line trajectory planning method.

[0005] To solve the above technical problems, the technical scheme adopted by the application is as follows: an intelligent forging production line, comprising a mobile guide rail, a forging transfer robot for transferring a blank and a workpiece is installed on the mobile guide rail, a blanking press and a forging manipulator are arranged at one end of the mobile guide rail, a die forging press is arranged at the other end of the mobile guide rail, a blank heating furnace and a secondary heating furnace are arranged at one side of the mobile guide rail, and a blank placing area, a workpiece placing area and a die heating furnace are arranged at the other side of the mobile guide rail; a clamping groove for mechanical positioning is installed on the placing plane of the blank placing area and the workpiece placing area; the clamping grooves of the blank placing area and the workpiece placing area are arrayed and used for mechanical positioning of the blank and the workpiece in the plane; a sensor for controlling the opening and closing of a furnace door is installed on the blank heating furnace, the secondary heating furnace and the die heating furnace, when the sensor for controlling the opening and closing of the furnace door detects that the forging transfer robot approaches, the furnace door is controlled to be opened, and when the sensor detects that the forging transfer robot moves away, the furnace door is controlled to be closed.

[0006] The die heating furnace is used for slowly preheating a cold die to an optimal working temperature before forging, and uniformly maintaining the internal temperature through heat preservation, so as to eliminate the thermal stress impact caused by temperature difference, thereby guaranteeing the size precision of a forged part, improving the service life of the die and preventing the die from cracking.

[0007] By the above whole-line trajectory planning of the intelligent forging production line, the movement of each joint and the carrying trajectory are optimized, the automation, unmanned and intelligence of the forging production process are realized, and the whole-line operation stability and production efficiency of the forging production line are improved.

[0008] Further, the forging carrying robot comprises a vehicle body mechanism, a connecting rod mechanism and a tongs mechanism, the tongs mechanism is connected with the vehicle body mechanism through the connecting rod mechanism, the vehicle body mechanism controls the whole movement and rotation of the forging carrying robot, the connecting rod mechanism controls the extension, lifting and pitching of the tongs of the forging carrying robot, and the tongs mechanism controls the clamping and rotation of the tongs of the forging carrying robot.

[0009] Further, the connecting rod mechanism of the forging carrying robot is a planar three-degree-of-freedom mechanism, facilitating trajectory planning.

[0010] The application also provides a whole-line trajectory planning method of an intelligent forging production line, comprising the following steps: S1, determining forging process information according to a forging product, and preheating a forging die to preliminarily determine the time distribution of each process of the forging production line and the total time limit; S2, executing a whole-line trajectory planning program, and returning the forging carrying robot to zero position to start the product forging production process; S3, the forging carrying robot first rotates counterclockwise by 90° and translates to a first slot position of a billet placing area, and then grabs a billet in the first slot, rotates 180° and translates to a first slot position of a corresponding billet heating furnace, a sensor on the billet heating furnace senses the approach of the forging carrying robot, controls the furnace door to open, and the forging carrying robot places the billet in the first slot of the billet heating furnace; S4, repeating step S3 until all billets are transferred from the slots of the billet placing area to the slots of the corresponding billet heating furnace, then the furnace door is closed, and the billet heating furnace heats the billets to a specified temperature; S5, the forging carrying robot translates to the first slot position of the billet heating furnace, the sensor on the billet heating furnace senses the approach of the forging carrying robot, controls the furnace door to open, the forging carrying robot grabs the billet in the first slot of the billet heating furnace, then the furnace door is closed, the forging carrying robot rotates clockwise by 90° and translates to a billet making press, and places the billet in the billet making press, and the billet making press works with a forging manipulator to make a billet; S6, according to the workpiece forging process information, confirming whether the workpiece needs secondary heating; If the forging needs to be heated twice, the forging handling robot takes out the forging in the blank making press, rotates counterclockwise by 90° and translates to the secondary heating furnace, the sensor on the secondary heating furnace senses that the forging handling robot is close, controls the furnace door to open, the forging handling robot places the blank in the secondary heating furnace, and performs secondary heating. After heating is completed, the forging handling robot takes out the forging, rotates counterclockwise by 90° and translates to the die forging press; If the forging does not need to be heated twice, the forging handling robot takes out the forging in the blank making press, rotates 180° and directly translates to the die forging press; S7, the forging handling robot places the forging in the die forging press for die forging, and after the forging in the die forging press is completed, the forging handling robot takes out the completed workpiece in the die forging press, rotates counterclockwise by 90° and translates to the first slot position of the workpiece placement area, and places the workpiece in the first slot of the workpiece placement area; S8, repeat steps S5-S7 until all workpieces are completed and placed in the corresponding slots of the workpiece placement area for subsequent work.

[0011] Further, in step S2, the whole line trajectory planning program is executed, and first the whole line time allocation optimization main program needs to be executed to plan the time allocation of each sub-stage of the intelligent forging production line; The whole line trajectory planning of the intelligent forging production line includes four parts: taking and heating, sending blank making, sending forging, and placing and cooling. The taking and heating and the placing and cooling have no time limit, and the whole line time allocation optimization takes the sending blank making and the sending forging as the target objects; The sending blank making and the sending forging include four sub-stages of taking, placing, translating, and rotating, and each sub-stage needs to be allocated a certain time. The sub-stages of the sending blank making and the sending forging have ; The input conditions of the whole line planning time allocation optimization algorithm of the intelligent forging production line are: The input variables are: The time allocation of each sub-stage of the ; The constraint conditions are: the impact of the sub-stage is set to an extreme value; the sum of the times of all sub-stages is set to an extreme value, , wherein is the longest time without secondary heating, is a safety factor, i is the i sub-stage of the whole line planning; The objective function is that the time of each sub-stage is .

[0012] The intelligent forging production line whole line planning optimization program is executed as follows: First, execute the overall planning time allocation procedure, allocating a certain amount of time to each sub-stage, with the allocation requirements meeting the following conditions. ; Secondly, under the above time allocation, the trajectory planning program for each sub-stage is executed. If the time allocation of a sub-stage cannot meet the optimization requirements of the sub-stage program during the execution of the sub-stage program, the time allocation for each sub-stage is redistributed until all sub-stage programs are executed successfully. Finally, multiple sets of input variables are obtained, and the optimal set is selected for simulation verification and control, which is the whole-line trajectory planning of the intelligent forging production line.

[0013] Furthermore, specific motion trajectory planning is performed on the forging and handling robot during the execution of the material picking, feeding, rotation, and translation sub-stages.

[0014] When executing the material handling and unloading sub-stages, the operating space and joint space of the forging handling robot are planned according to the material handling and unloading paths. The planning steps are as follows: The operational space is planned as follows: Based on the location of the intelligent forging production line, determine the path of the clamping mechanism at the end of the forging handling robot. The key points are initially given their position coordinates, the angle between the clamping mechanism and the horizontal plane is always set to 0°, and a preliminary time allocation is made. Operational space fitting: By using the discrete time and position coordinates of the clamping mechanism at each key point, the motion path of the clamping mechanism is fitted with a fifth-order B-spline, and the motion laws of the clamping mechanism's displacement, velocity, acceleration, and jerk are obtained. With the goal of minimizing the impact of the manipulator clamp mechanism, the time increment of each key point and the coordinates of each position to be optimized are used as input variables, and the velocity, acceleration, impact and total time of the manipulator clamp mechanism are used as constraints. The particle swarm optimization algorithm is used to optimize a set of optimal input parameters as the planning path of the manipulator clamp mechanism of the forging handling robot. The joint space planning is as follows: Based on the operational space planning path, the optimized path will be... Key points are discretely encrypted to one, and to Preliminary time allocation for each key encryption point; Inverse kinematics is used to determine the analytical relationship between the motion of each driving joint and the coordinates of key points using the closed-loop vector method. Joint space fitting: By fitting the discrete time of each driving joint at each key point with a fifth-order B-spline, the motion law of each driving joint can be obtained, and the displacement, velocity, acceleration and jerk of each driving joint can be obtained. Kinematics modeling, the forging transfer robot is modeled by using the Lagrange method, and the driving force variation law of each driving joint in the movement process is obtained; With the minimum impact of the joint space driving joint as the optimization goal, the time increment of each key point as the input variable, the joint space driving joint speed, acceleration, impact, joint driving force and total time as the constraint condition, and the particle swarm optimization algorithm, a set of optimal input parameters is optimized as the planning path of the joint space driving joint of the forging transfer robot.

[0015] Further, when the rotation and translation sub-stage programs are executed, the forging transfer robot involves point-to-point trajectory planning, and a quintic polynomial interpolation is used to fit the trajectory curve connecting two points.

[0016] Due to the above technical scheme, the present application has the following beneficial effects: In terms of automation, the intelligent forging production line of the present application effectively avoids the shortcomings of manual operation through automated and intelligent production methods, significantly improving production efficiency, product quality and production safety.

[0017] In terms of trajectory planning, the present application uses advanced quintic B-spline curves and particle swarm optimization algorithms for trajectory planning. In view of the mechanical impact and vibration problems caused by the sudden changes in speed and acceleration in traditional trajectory planning methods, the quintic B-spline curve can achieve smooth transition, significantly reducing the impact force, thereby improving the service life and running stability of the equipment. In the face of the challenge of multi-stage motion coordination in complex process flow, the present application decomposes the motion into key points and combines the particle swarm optimization algorithm to realize efficient coordination and optimization of each stage under the premise of meeting multiple constraint conditions such as speed, acceleration, impact and total time; In view of the real-time adjustment requirement in dynamic environment; the present application combines sensor feedback to dynamically optimize the motion trajectory, enhancing the flexibility and adaptability of the production line.

[0018] In terms of high-precision motion control, the present application combines the precise modeling capability of quintic B-spline curves with the global optimization capability of the particle swarm optimization algorithm, not only realizing high-precision motion control, but also optimizing load distribution and energy consumption, improving the running efficiency and service life of the equipment, making the motion control of the forging transfer robot smoother and more accurate; Compared with manual operation relying on experience, the trajectory planning of the present application can fully utilize the computing advantage to accurately calculate the optimal motion path and time distribution, avoiding the problems of unstable motion, low efficiency and other problems caused by insufficient experience in manual operation, significantly improving the control precision and running efficiency of the robot.

[0019] In terms of trajectory optimization, the present application has significant advantages compared to direct drive or traditional trapezoidal line and S-shaped curve with unoptimized trajectory planning. In unoptimized trajectory planning, the speed and load of each motion joint change dramatically, which can easily lead to mechanical impact, vibration and increased energy consumption, affecting the service life and production efficiency of the equipment. However, the quintic B-spline curve trajectory optimization adopted by the present application can make the speed, acceleration and impact force of each motion joint more stable, reduce mechanical vibration and energy consumption, and improve the service life and production efficiency of the equipment. For example, direct drive mode can cause sudden changes in joint speed and acceleration, while quintic B-spline curve can reduce such sudden changes through smooth transition, making the movement more smooth; compared with trapezoidal line and S-shaped curve, quintic B-spline curve can better optimize time allocation and load distribution while ensuring smooth movement, further improving production efficiency and equipment performance.

[0020] In summary, the present application optimizes the speed and load of each motion joint through the intelligent forging production line and whole-line trajectory planning method, not only realizes the automation, unmanned and intelligentization of the production process, but also significantly improves the production efficiency, product quality, running stability and production safety, which has wide application prospect and important practical significance. BRIEF DESCRIPTION OF DRAWINGS

[0021] The advantages and implementation modes of the present application will be more obvious by referring to the accompanying drawings and combining the examples, wherein the contents shown in the drawings are only used to explain and describe the present application, and do not constitute any limitation on the present application. In the drawings: Figure 1 It is a structural schematic diagram of the present application.

[0022] Figure 2 It is a structural schematic diagram of the workpiece placing area clamping groove of the present application.

[0023] Figure 3 It is a working state schematic diagram of the forging handling robot of the present application Figure 1 .

[0024] Figure 4 It is a working state schematic diagram of the forging handling robot of the present application Figure 2 .

[0025] Figure 5 It is a distribution schematic diagram of the end gripper path key point of the forging handling robot of the present application.

[0026] Figure 6 It is a mechanism diagram of the forging handling robot of the present application.

[0027] In the drawings: 1. Forging press; 2. Moving guide rail; 3. Forging handling robot; 4. Billet press; 5. Forging manipulator; 6. Die heating furnace; 7. Secondary heating furnace; 8. Billet heating furnace; 9. Workpiece placement area; 10. Billet placement area; 11. Sensor; 301. Clamping head mechanism; 302. Linkage mechanism; 303. Car body mechanism; 901. Slot. Detailed Implementation

[0028] like Figures 1 to 6 As shown, this invention discloses an intelligent forging production line, comprising a moving guide rail 2, on which a forging handling robot 3 for transferring billets and workpieces is mounted. One end of the moving guide rail 2 is equipped with a billet pressing machine 4 and a forging manipulator 5, while the other end is equipped with a die forging press 1. M billet heating furnaces 8 and N secondary heating furnaces 7 (M and N are natural numbers greater than 0) are arranged on one side of the moving guide rail 2, and a billet placement area 10, a workpiece placement area 9, and a die heating furnace 6 are arranged on the other side of the moving guide rail 2. The placement plane of 9 is equipped with slots 901 for mechanical positioning; multiple slots 901 in the billet placement area 10 and the workpiece placement area 9 are arranged in an array for mechanical positioning of the billet and workpiece in the plane; sensors 11 for controlling the opening and closing of the furnace door are installed on the billet heating furnace 8, the secondary heating furnace 7 and the mold heating furnace 6. When the sensors 11 detect that the forging handling robot 3 is approaching, they control the furnace door to open; when they detect that the forging handling robot 3 is moving away, they control the furnace door to close.

[0029] The mold heating furnace 6 is used to slowly preheat the cold mold to the optimal working temperature before forging, and to keep it warm to make its internal temperature uniform, thereby eliminating the thermal stress impact caused by temperature difference, thus ensuring the dimensional accuracy of the forging, improving the service life of the mold and preventing the mold from cracking.

[0030] By performing overall trajectory planning on the intelligent forging production line as described above, optimizing the movement of each joint and the transport trajectory, the automation, unmanned operation, and intelligence of the forging production process are achieved, thereby improving the overall stability and production efficiency of the forging production line.

[0031] like Figure 3 and Figure 4 As shown, the forging and handling robot 3 includes a vehicle body mechanism 303, a linkage mechanism 302, and a clamping head mechanism 301. The clamping head mechanism 301 is connected to the vehicle body mechanism 303 through the linkage mechanism 302. The vehicle body mechanism 303 controls the overall movement and rotation of the forging and handling robot 3. The linkage mechanism 302 controls the extension, lifting, and pitching of the clamping head of the forging and handling robot 3. The clamping head mechanism 301 controls the clamping and rotation of the clamping head of the forging and handling robot 3.

[0032] The linkage mechanism 302 of the forging transfer robot 3 is a planar three-degree-of-freedom mechanism, facilitating trajectory planning.

[0033] The application also provides a whole-line trajectory planning method for the intelligent forging production line, comprising the following steps: S1, determining the forging process information according to the forging product, such as the number of workpieces, the category, whether secondary heating is needed, determining the forging die, etc., and preliminarily determining the time distribution of each process of the forging production line and the total time limit by completing the preheating of the forging die in advance; S2, executing the whole-line trajectory planning program, setting the forging transfer robot 3 to zero, and starting the product forging production process; S3, the forging transfer robot 3 first rotates counterclockwise by 90° and translates to the No. 1 slot position of the billet placing area 10, grabs the billet in the No. 1 slot, then rotates by 180° and translates to the corresponding No. 1 slot position of the billet heating furnace 8, the sensor on the billet heating furnace 8 senses the approach of the forging transfer robot 3, controls the furnace door to open, and the forging transfer robot 3 places the billet in the No. 1 slot of the billet heating furnace 8; S4, repeating step S3 until all the billets are transferred from the slots of the billet placing area 10 to the corresponding slots of the billet heating furnace 8, then the furnace door is closed, and the billet heating furnace 8 heats the billets to the specified temperature; S5, the forging transfer robot 3 translates to the No. 1 slot position of the billet heating furnace 8, the sensor on the billet heating furnace 8 senses the approach of the forging transfer robot 3, controls the furnace door to open, the forging transfer robot 3 grabs the billet in the No. 1 slot of the billet heating furnace 8, then the furnace door is closed, the forging transfer robot 3 rotates clockwise by 90° and translates to the billet making press 4, and places the billet in the billet making press 4, which works in cooperation with the forging manipulator 5 to perform the billet making forging on the billet; S6, according to the workpiece forging process information, confirming whether the workpiece needs secondary heating; If the workpiece needs secondary heating, the forging transfer robot 3 takes out the workpiece from the billet making press 4, rotates counterclockwise by 90° and translates to the secondary heating furnace 7, the sensor on the secondary heating furnace 7 senses the approach of the forging transfer robot 3, controls the furnace door to open, the forging transfer robot 3 places the billet in the secondary heating furnace, performs secondary heating, after the heating is completed, the forging transfer robot 3 takes out the workpiece, rotates counterclockwise by 90° and translates to the die forging press 1; If the workpiece does not need secondary heating, the forging transfer robot 3 takes out the workpiece from the billet making press 4, rotates by 180° and directly translates to the die forging press 1; S7, the forging handling robot 3 places the forging in the die forging press 1 for die forging, after the forging is completed in the die forging press 1, the forging handling robot 3 takes out the completed forging in the die forging press 1, rotates 90° counterclockwise and translates to the first slot position of the workpiece placing area 9, and places the workpiece in the first slot of the workpiece placing area 9; S8, repeat steps S5-S7 until all workpieces are completed and placed in the corresponding slots of the workpiece placing area 9 for subsequent work.

[0034] In step S2, the whole line trajectory planning program is executed, which first needs to execute the whole line time allocation optimization main program to plan the time allocation of each sub-stage of the intelligent forging production line; The whole line trajectory planning of the intelligent forging production line includes four parts: taking and heating, feeding blank, feeding forging, and placing and cooling, among which taking and heating and placing and cooling have no time limit; therefore, the whole line time allocation optimization takes feeding blank and feeding forging as the target object; Among them, feeding blank and feeding forging include four sub-stages of taking, placing, translating, and rotating, and each sub-stage needs to be allocated a certain time, and the sub-stages of feeding blank and feeding forging have ; Therefore, the input conditions of the intelligent forging production line whole line planning time allocation optimization algorithm are: The input variables are: The time allocation of each sub-stage of the ; The constraint conditions are: the impact of the sub-stage is set to an extreme value; the sum of the times of all sub-stages is set to an extreme value, , wherein is the longest time without secondary heating, is the safety factor, is the sub-stage of the whole line planning; The objective function is to minimize the time of each sub-stage .

[0035] Therefore, the intelligent forging production line whole line planning optimization program is executed as follows: First, execute the whole line planning time allocation program to allocate a certain time to each sub-stage, and the allocation requirement meets ; Secondly, under the above time allocation, execute the sub-stage trajectory planning program, and if the sub-stage time allocation cannot meet the optimization requirements of the sub-stage program during the execution of the sub-stage program, re-allocate the time of each sub-stage until all sub-stage programs are successfully executed; Finally, a plurality of groups of input variables are obtained, and the optimal one is selected for simulation verification and control, which is the intelligent forging production line whole line trajectory planning.

[0036] Furthermore, when executing the sub-stages of material picking, feeding, rotation, and translation, specific motion trajectory planning is required for the forging and handling robot.

[0037] like Figure 5 As shown, when executing the material picking and unloading sub-stage program, the operating space and joint space of the forging handling robot 3 need to be planned according to the material picking and unloading path. The planning steps are as follows: The operational space is planned as follows: Based on the location of the intelligent forging production line, determine the path of the clamp mechanism 301 at the end of the forging handling robot 3. Key points ( It is a natural number, and The coordinates of each key point are initially given, the angle (i.e., attitude angle) between the clamping head mechanism 301 and the horizontal plane is set to 0° at all times, and a preliminary time allocation is performed. By fitting the operating space, the motion path of the clamping mechanism 301 is obtained by fitting the discrete time and position coordinates of each key point through the clamping mechanism 301 with a fifth-order B-spline, and the displacement, velocity, acceleration, and jerk (impact) motion law of the clamping mechanism 301 can be obtained. The above steps are treated as a whole, with the goal of minimizing the impact of the manipulator clamp mechanism 301. The time increment of each key point and the coordinates of each position to be optimized are used as input variables. The velocity, acceleration, impact and total time of the manipulator clamp mechanism 301 are used as constraints. The particle swarm optimization algorithm is used to optimize a set of optimal input parameters as the planning path of the manipulator clamp mechanism 301 of the forging and handling robot 3. The joint space planning is as follows: Based on the operational space planning path, the optimized path will be... Key points are discretely encrypted to indivual( It is a natural number, and ), and on Preliminary time allocation for each key encryption point; Inverse kinematics is used to determine the analytical relationship between the motion of each driving joint and the coordinates of key points using the closed-loop vector method. Joint space fitting: By fitting the discrete time of each driving joint at each key point with a fifth-order B-spline, the motion law of each driving joint can be obtained, and the displacement, velocity, acceleration, and jerk (impact) of each driving joint can be obtained. Dynamic modeling: The Lagrange method is used to model the dynamics of the forging and handling robot 3, which can obtain the dynamic change law of each drive joint during the motion. The above steps are taken as a whole to optimize the joint space driven joint impact minimization, with the time increment of each key point as the input variable, the joint space driven joint speed, acceleration, impact, and joint driving force and total time as the constraint conditions, and the particle swarm optimization algorithm is adopted to optimize a set of optimal input parameters as the planning path of the forging transfer robot 3 joint space driven joint. Further, when executing the rotation and translation sub-stage program, the forging transfer robot 3 involves point-to-point trajectory planning, using a quintic polynomial interpolation to fit a trajectory curve connecting two points, and the planning steps are as follows: Let the quintic polynomial be: In the formula, is a function of the rotation angle or the translation position with respect to time, is the initial time, t is the arbitrary time, is the constant term of the quintic polynomial, is the first-order term coefficient of the quintic polynomial, is the second-order term coefficient of the quintic polynomial, is the third-order term coefficient of the quintic polynomial, is the fourth-order term coefficient of the quintic polynomial, is the fifth-order term coefficient of the quintic polynomial; The first derivative is: The second derivative is: The third derivative is: For this segment of the quintic polynomial, the constraints at the initial time and the termination time are used: In the formula, is a function of the displacement with respect to time, is a function of the velocity with respect to time, is a function of the acceleration with respect to time, i.e. is a function of the displacement with respect to the initial time, is a function of the velocity with respect to the initial time, is a function of the acceleration with respect to the initial time; is a function of the displacement with respect to the termination time, is a function of the velocity with respect to the termination time, is a function of the acceleration with respect to the termination time; is the termination time, and These refer to the angles or positions of the starting and ending points, respectively. and These are the velocities at the starting and ending points, respectively. and These are the accelerations at the starting point and the ending point, respectively. The coefficients of the polynomial are obtained as follows: in, , ; The final result is the motion curve of the trajectory connecting the two points, including displacement, velocity, acceleration, and jerk (impact).

[0038] Furthermore, the fifth-order B-spline interpolation is as follows: for Control points A node vector ,structure The polynomial expression for a B-spline curve. : In the formula, For the first One control vertex; To control the total number of vertices; For node intervals A certain variable within; For the first part The subnormalized B-spline basis functions are expressed using the Cox-deBuhr recurrence formula. for: In the formula, Let be the degree of the B-spline basis function. and For the first and the 1 node For the first Segment 0 B-spline basis function, For the first 1 node For the first part B-spline basis functions For the first 1 node For the first part B-spline basis functions For the first One node; When calculating the control vertices, given the key points and node vectors, the repetition of the curve's start and end points is set to... The cumulative chord length parameterization method is used to normalize the time nodes, resulting in the values ​​of the node vectors. for: In the formula, It represents the difference between two adjacent time points. For the first The difference between two adjacent time points in a segment. For the first The difference between two adjacent time points in a segment; Differentiation yields the B-spline curve. r expression of the first derivative for: In the formula, r For B-spline curves The order of the derivative, For the first Each control vertex First derivative, For the first part B-spline basis functions for of The first derivative and the second derivative part The product of B-spline basis functions; In the formula, for of First derivative, For the first One control vertex, for of order derivative and of Difference of first derivative and second derivative The node and the first The ratio of the differences between the nodes; A key point can be constructed A quintic B-spline curve is needed. Several control points are used to control the shape of the curve for solving... Each control point needs to be constructed. Solve the equations, given the following: A discrete time location sequence of points ， The start time is , the end time is , the first segment 5th order B-spline curve can be expressed as: In the formula: is the ordinal of the first control vertex, is the ordinal of the first control vertex, is the first segment 5th order B-spline basis function, is the product of the first control vertex and the first segment 5th order B-spline basis function; Given the position of each segment curve end point, according to the continuity requirement, the connecting point of the two connected B-spline curves satisfies the following conditions, which can constitute equations; In the formula, is the end value of the first segment 5th order B-spline curve; is the first end value of the first segment 5th order B-spline curve. Because the motion at the initial position and the end position is known, the start and stop positions are respectively , the start and stop speeds are respectively , and the start and stop accelerations are respectively , and thus 6 more equations can be added, so the total number of equations is

[0039] , and the first control vertex can be solved; Given the node vector, basis function and control vertex, the final B-spline curve can be obtained, and the velocity , acceleration and jerk (impact) of the 5th order B-spline curve can be obtained by differentiating the B-spline curve as follows: In the formula, is the function of velocity and time; is the function of acceleration and time; is the function of impact and time; is the first order derivative of the 5th order B-spline curve; In the formula, is the function of velocity and time; is the function of acceleration and time; is the function of impact and time; is the first order derivative of the 5th order B-spline curve; The second derivative of a quintic B-spline curve; The third derivative of a quintic B-spline curve; For the first The first derivative of each control vertex; For the first The second derivative of each control vertex; For the first The third derivative of each control vertex; For the first part B-spline basis functions; For the first part B-spline basis functions; For the first part B-spline basis functions; Here is the specific formula for summing the first derivative of a quintic B-spline curve; Here is the specific formula for summing the second derivative of a quintic B-spline curve; Here is the specific formula for summing the third derivative of a quintic B-spline curve.

[0040] Furthermore, such as Figure 6 As shown, the inverse kinematics solution in the planning steps is as follows: The inverse kinematics solution is obtained using the closed-loop vector method. This inverse kinematics solution for the forging and handling robot 3 is expressed as follows: given the coordinates of the key points through which the end effector 301 passes... Solve for the motion of each driven joint. , The closed-loop vectors can be listed as follows: In the formula, Represented as a link The coordinate vector, the direction from arrive Size is Length; Represented as a link The coordinate vector, the direction from arrive Size is Length; M Represented as a link M The coordinate vector, the direction from arrive Size is M Length; Represented as a link The coordinate vector, the direction from To , the size is length, and so on.

[0041] Solving the vector equation set, the motion amount of each driving joint (inverse kinematics solution) is as follows: In the formula, is the driving amount of the first joint, is the driving amount of the second joint, is the component in the direction of , is the component in the direction of , is the component in the direction of , is the component in the direction of ; Further, the dynamics modeling is as follows: Compared with the driving force of each driving joint, the joint friction of the forging transfer robot 3 is negligible in numerical value, so the joint friction is not considered when using the Lagrange method to model the dynamics; The Lagrange equation is as follows: In the formula, is the generalized coordinate; is the generalized velocity; is the generalized force, when the generalized coordinate is linear displacement, is the force, when the generalized coordinate is angular displacement, is the torque; the Lagrange function , is the total kinetic energy of the mechanical system, is the total potential energy of the mechanical system, represents the joint, is the driving force or driving torque; can be further expressed as: The premise of establishing a dynamics model based on the Lagrange equation is that there is a set of generalized coordinates representing the kinetic and potential energy of the system; The kinetic energy of a rigid body is expressed as: In the formula, is the total mass of the rigid body, the translational velocity of the rigid body, the angular velocity of the rigid body, the translational kinetic energy of the rigid body, the rotational kinetic energy of the rigid body; where the inertia tensor of the rigid body depends only on the shape and mass distribution of the object, is expressed as: where the principal diagonal elements of the matrix , , are the moments of inertia of the rigid body about , , the non-principal diagonal elements of the matrix , , are the products of inertia of the rigid body; , , are equal to , , ; the linear velocity and angular velocity of any point on the rigid body can be expressed through the Jacobian matrix and joint velocities: where is the linear velocity Jacobian of the rigid body f on the robot, is the angular velocity Jacobian of the rigid body f on the robot, is the joint driving quantity, is the joint velocity; Therefore, the total kinetic energy of the system is expressed as: where is expressed as the inertia tensor of the rigid body at the initial position, m f is the mass of the rigid body, is expressed as the rotation matrix of the rigid body , n is always the total number of rigid bodies of the robot; where is expressed as the inertia matrix of the system; Potential energy of rigid body is expressed as: where, is the centroid coordinate of the rigid body, is the local gravity acceleration; Therefore, the total potential energy of the system is is expressed as: where, is the centroid coordinate of the rigid body, is the centroid coordinate of the rigid body; The above formula is arranged to establish the robot dynamics equation, and finally the Lagrange equation is obtained as: where, represents the inertial force term; represents the velocity product term, including the Coriolis force and centrifugal force; represents the gravity term, is the driving force or driving torque to be solved.

[0042] Further, the input conditions of the particle swarm algorithm are: Operation space: The input variables are: a time increment , and position coordinates; The constraint conditions are: the velocity, acceleration, impact, and total time of the jaw mechanism 301 are set to extreme values; The objective function is: the impact of the jaw mechanism 301 is minimized; Joint space: The input variables are: a time increment ; The constraint conditions are: the driving joint velocity, acceleration, impact, and joint driving force and total time are set to extreme values; The objective function is: the driving joint impact is minimized; Wherein, the extreme value setting depends on engineering experience or experimental analysis.

[0043] The embodiments of the application are described in detail above, but the content described is only the preferred embodiments of the application, and cannot be considered as limiting the scope of the implementation of the application. Any equivalent changes and improvements made within the scope of the application should still be included in the scope of the application.

Claims

1. A method for whole-line trajectory planning of an intelligent forging production line, characterized in that: The intelligent forging production line comprises a mobile guide rail, a forging transfer robot mounted on the mobile guide rail, a blanking press and a forging manipulator arranged at one end of the mobile guide rail, a die forging press arranged at the other end of the mobile guide rail, a blank heating furnace and a secondary heating furnace arranged at one side of the mobile guide rail, and a blank placing area, a workpiece placing area and a die heating furnace arranged at the other side of the mobile guide rail; a clamping groove for mechanical positioning is mounted on the placing plane of the blank placing area and the workpiece placing area; the forging transfer robot comprises a vehicle body mechanism, a connecting rod mechanism and a tongs head mechanism, and the tongs head mechanism is connected with the vehicle body mechanism through the connecting rod mechanism; The whole-line trajectory planning method of the intelligent forging production line comprises the following steps: S1, preheating of a forging die; S2, performing whole-line trajectory planning and starting a forging process; S3, grabbing a blank into a furnace; S4, heating the blank; S5, blanking forging; S6, confirming secondary heating; S7, placing a workpiece after die forging; and S8, completing all forgings. In step S2, the whole-line trajectory planning is performed, and first, the whole-line time allocation optimization is performed to plan the time allocation of each sub-stage of the intelligent forging production line; the feeding and forging of the blank includes four sub-stages of taking, placing, translation, and rotation, and each sub-stage needs to be allocated a certain time, and the sub-stages of the feeding and forging of the blank have ​ The input conditions of the intelligent forging production line whole-line planning time distribution optimization algorithm are as follows: The input variables are: The time allocation for each sub-stage is as follows: ; The constraints are: sub-phase impact set to an extreme value; the sum of all sub-phase times set to an extreme value, wherein, is the longest time without secondary heating, is a safety factor, i is the first sub-phase of the overall line plan; and i is the last sub-phase of the overall line plan. Objective function: Minimize the total time of all sub-phases minimize the total time of all sub-phases Then the whole line planning time allocation is executed, and a certain time is allocated to each sub-stage, and the allocation requirements are met Under the above time allocation, the trajectory planning of each sub-stage is executed. If the time allocation of the sub-stage cannot meet the optimization requirements of the sub-stage during the execution of the sub-stage, the time allocation of each sub-stage is re-executed until all the sub-stages are successfully executed. Finally, a plurality of groups of input variables are obtained, and an optimal group is selected for simulation verification and control.

2. The method of claim 1, wherein: When the material taking and placing sub-stage is executed, the path of the material taking and placing is planned for the forging transfer robot operation space and joint space, The operation space planning is as follows: According to the intelligent forging production line position, determine the key points of the tongs mechanism path of the forging transfer robot end , preliminarily give the position coordinates of each key point, set the angle between the tongs mechanism and the horizontal plane to be always 0°, and preliminarily allocate time; Operation space fitting: the motion path of the tongs head mechanism is fitted by using five B-splines through the discrete time and position coordinates of each key point, and the displacement, velocity, acceleration and jerk motion law of the tongs head mechanism are obtained; Taking the minimum impact of the operation space tongs head mechanism as the optimization objective, taking the time increment of each key point and each to-be-optimized position coordinate as the input variable, taking the operation space tongs head mechanism velocity, acceleration, jerk and total time as the constraint condition, adopting the particle swarm optimization algorithm, a group of optimal input parameters are overall optimized as the planning path of the operation space tongs head mechanism of the forging transfer robot; The joint space planning is as follows: According to the operation space planning path, the optimized key points are discretely encrypted to , and the preliminary time allocation is performed on encrypted key points; Kinematics inverse solution: the analytical relationship between the motion amount of each driving joint and the key point coordinates is determined by using the closed-loop vector method; Joint space fitting: the motion law of each driving joint is fitted by using five B-splines through the discrete time of each driving joint at each key point, and the displacement, velocity, acceleration and jerk of each driving joint are obtained; Dynamics modeling: the dynamics modeling of the forging transfer robot is performed by using the Lagrange method, and the driving force variation law of each driving joint in the motion process is obtained; Taking the minimum impact of the joint space driving joint as the optimization objective, taking the time increment of each key point as the input variable, taking the joint space driving joint velocity, acceleration, jerk and joint driving force and total time as the constraint condition, adopting the particle swarm optimization algorithm, a group of optimal input parameters are overall optimized as the planning path of the joint space driving joint of the forging transfer robot. 3.The method of claim 1, wherein: When the rotation and translation sub-stage is executed, the forging transfer robot involves point-to-point trajectory planning, and a five-order polynomial interpolation is used to fit the trajectory curve connecting two points.

4. The method of claim 1, wherein: The multiple clamping slots of the blank placing area and the workpiece placing area are arrayed for mechanical positioning of the blanks and workpieces in the plane; and the blank heating furnace, the secondary heating furnace and the die heating furnace are provided with sensors for controlling the opening and closing of the furnace doors.

5. The method of claim 4, wherein: In step S3, the forging handling robot first rotates counterclockwise by 90° and translates to the No. 1 clamping slot position of the blank placing area, grabs the blank in the No. 1 clamping slot, then rotates by 180° and translates to the corresponding No. 1 clamping slot position of the blank heating furnace, the sensor on the blank heating furnace senses the approach of the forging handling robot, controls the furnace door to open, and the forging handling robot places the blank in the No. 1 clamping slot of the blank heating furnace.

6. The method of claim 5, wherein: In step S4, step S3 is repeated until all blanks are transferred from the clamping slots of the blank placing area to the corresponding clamping slots of the blank heating furnace, and then the blank heating furnace heats the blanks to the specified temperature.

7. The method of claim 6, wherein: In step S5, the forging handling robot translates to the No. 1 clamping slot position of the blank heating furnace, the sensor on the blank heating furnace senses the approach of the forging handling robot, controls the furnace door to open, the forging handling robot grabs the blank in the No. 1 clamping slot of the blank heating furnace, then the furnace door is closed, the forging handling robot rotates clockwise by 90° and translates to the blanking press, and places the blank in the blanking press, which works in cooperation with the forging manipulator to blank the blank.

8. The method of claim 7, wherein: In step S7, the forging handling robot places the workpiece in the die forging press for die forging, after forging, the forging handling robot takes out the finished workpiece from the die forging press, rotates counterclockwise by 90° and translates to the No. 1 clamping slot position of the workpiece placing area, and places the workpiece in the No. 1 clamping slot of the workpiece placing area.

9. The method of claim 7, wherein: In step S6, if the workpiece needs to be heated again, the forging handling robot takes out the workpiece from the blanking press, rotates counterclockwise by 90° and translates to the secondary heating furnace, the sensor on the secondary heating furnace senses the approach of the forging handling robot, controls the furnace door to open, the forging handling robot places the blank in the secondary heating furnace, and performs secondary heating, after heating is completed, the forging handling robot takes out the workpiece, rotates counterclockwise by 90° and translates to the die forging press; if the workpiece does not need to be heated again, the forging handling robot takes out the workpiece from the blanking press, rotates by 180° and directly translates to the die forging press.

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