Intelligent forging production line and whole-line trajectory planning method

Abstract

The application provides a kind of intelligent forging production line and whole line trajectory planning method, belongs to intelligent forging technical field, including mobile guide rail, mobile guide rail is installed for shifting blank and workpiece forging handling robot, blank making press, forging manipulator, die forging press, blank heating furnace, secondary heating furnace, blank placement area, workpiece placement area and die heating furnace are arranged around mobile guide rail.The application carries out whole line trajectory planning to the above-mentioned intelligent forging production line, determines the time distribution of each process and total time limit, by executing whole line time distribution optimization main program, the time distribution of each sub-stage of forging production line whole line is planned, and the operation space and joint space of forging handling robot are planned.Obviously, the application realizes the automation, unmanned and intelligentization of forging production process by optimizing the motion condition of each joint and handling trajectory, improves the smoothness of forging production line whole line operation and production efficiency.

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 motion 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 advancing and rotating of the forging carrying robot, the connecting rod mechanism controls the tongs stretching, lifting and pitching of the forging carrying robot, and the tongs mechanism controls the tongs clamping and rotating of the forging carrying robot.

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

[0010] The application also provides a whole line trajectory planning method of an intelligent forging production line, comprising the following steps:

[0011] S1, determining the forging process information according to the forging product, 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;

[0012] S2, executing the whole line trajectory planning program, and returning the forging carrying robot to zero position to start the product forging production process;

[0013] S3, the forging carrying robot first rotates counterclockwise by 90° and translates to the first card slot position of the billet placing area, grabs the billet in the first card slot, then rotates by 180° and translates to the first card slot position of the corresponding billet heating furnace, the 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 card slot of the billet heating furnace;

[0014] S4, repeating step S3 until all billets are transferred from the card slot of the billet placing area to the card slot of the corresponding billet heating furnace, then the furnace door is closed, and the billet heating furnace heats the billet to the specified temperature;

[0015] S5, the forging carrying robot translates to the first card 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 card slot of the billet heating furnace, then the furnace door is closed, the forging carrying robot rotates clockwise by 90° and translates to the billet making press, and places the billet in the billet making press, which works with the forging manipulator to make the billet;

[0016] S6, according to the workpiece forging process information, confirming whether the workpiece exists a secondary heating link;

[0017] 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, the control door is opened, the forging handling robot places the blank in the secondary heating furnace, and the secondary heating is carried out. After the heating is completed, the forging handling robot takes out the forging, rotates counterclockwise by 90° and translates to the die forging press;

[0018] 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;

[0019] 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, 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;

[0020] S8, repeat steps S5-S7 until all workpieces are completed and placed in the corresponding slots of the workpiece placement area for subsequent work.

[0021] 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;

[0022] 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;

[0023] 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 ;

[0024] The input conditions of the whole line planning time allocation optimization algorithm of the intelligent forging production line are:

[0025] The input variables are: The time allocation of each sub-stage of the ;

[0026] 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 for no secondary heating, is a safety factor, i is the i sub-stage of the whole line planning;

[0027] Objective function: each sub-stage time Min.

[0028] The intelligent forging production line whole line planning optimization program is executed as follows:

[0029] First, the whole line planning time allocation program is executed to allocate a certain time for each sub-stage, and the allocation requirement meets ;

[0030] Secondly, under the above time allocation, the trajectory planning program of each sub-stage is executed. In the execution of the sub-stage program, if the sub-stage time allocation cannot meet the optimization requirements of the sub-stage program, the time allocation for each sub-stage is re-executed until all sub-stage programs are successfully executed.

[0031] Finally, a plurality of groups of input variables are obtained, and an optimal group is selected for simulation verification and control, that is, the intelligent forging production line whole line trajectory planning.

[0032] Further, when the taking, placing, rotating and translating sub-stage programs are executed, specific motion trajectory planning is performed on the forging handling robot.

[0033] When the taking and placing sub-stage programs are executed, the forging handling robot operating space and joint space are planned according to the taking and placing paths, and the planning steps are as follows:

[0034] The operating space planning is as follows:

[0035] According to the position of the intelligent forging production line, the key points of the gripper mechanism path of the end of the forging handling robot are determined, the position coordinates of each key point are preliminarily given, the angle between the gripper mechanism and the horizontal plane is set to be always 0°, and preliminary time allocation is performed;

[0036] Operating space fitting: through the discrete time and position coordinates of the gripper mechanism passing through each key point, the motion path of the gripper mechanism is fitted by five B-splines to obtain the displacement, velocity, acceleration and jerk motion law of the gripper mechanism;

[0037] Taking the minimum impact of the operating space gripper 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 operating space gripper mechanism velocity, acceleration, impact and total time as the constraint condition, adopting the particle swarm optimization algorithm, a group of optimal input parameters are optimized as the planning path of the operating space gripper mechanism of the forging handling robot;

[0038] The joint space planning is as follows:

[0039] According to the operating space planning path, the optimized key points are discretely encrypted to a preliminary time allocation is made for the encrypted key points;

[0040] The kinematic inverse solution is determined by using a closed loop vector method to determine the analytical relationship between the motion amount of each driving joint and the coordinates of the key points.

[0041] Joint space fitting, by using the discrete time of each driving joint at each key point, the motion law of each driving joint is fitted by using a quintic B-spline, so that the displacement, velocity, acceleration and jerk of each driving joint are obtained.

[0042] Dynamics modeling, the dynamics modeling of the forging transfer robot is carried out by using the Lagrange method, and the driving force variation law of each driving joint in the motion process is obtained.

[0043] With the minimum impact of the joint space driving joint as the optimization target, the time increment of each key point as the input variable, the joint space driving joint velocity, acceleration, impact, joint driving force and total time as the constraint conditions, and the particle swarm optimization algorithm, a set of optimal input parameters are optimized as the planning path of the joint space driving joint of the forging transfer robot.

[0044] 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.

[0045] Due to the above technical scheme, the present application has the following beneficial effects:

[0046] 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.

[0047] 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 demand in dynamic environment; the present application combines sensor feedback to dynamically optimize the motion trajectory, enhancing the flexibility and adaptability of the production line.

[0048] ​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 realizes high-precision motion control, but also optimizes load distribution and energy consumption, improves the operation efficiency and service life of the equipment, and makes the motion control of the forging handling robot more smooth and accurate; compared with manual operation relying on experience for control, the trajectory planning of the present application can fully utilize the calculation advantage to accurately calculate the optimal motion path and time distribution, avoid the problems of unstable motion, low efficiency and the like caused by insufficient experience in manual operation, and significantly improve the control precision and operation efficiency of the robot.

[0049] In terms of trajectory optimization, the present application has significant advantages compared with direct driving or traditional trapezoidal lines and S-shaped curves using unoptimized trajectories. In unoptimized trajectory planning, the speed and load of each motion joint change sharply, which is easy to cause mechanical impact, vibration and increased energy consumption, affecting the service life and production efficiency of the equipment. 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 driving mode can cause sudden changes in joint speed and acceleration, while the quintic B-spline curve can reduce such sudden changes through smooth transition, making the motion more smooth; compared with trapezoidal lines and S-shaped curves, the quintic B-spline curve can better optimize time distribution and load distribution while ensuring motion smoothness, further improving production efficiency and equipment performance.

[0050] 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 automation, unmanned and intelligentization of the production process, but also significantly improves production efficiency, product quality, operation stability and production safety, and has wide application prospects and important practical significance. BRIEF DESCRIPTION OF DRAWINGS

[0051] The advantages and implementation modes of the present application will be more obvious by referring to the drawings and combining the examples, wherein the contents shown in the drawings are only used for explaining and describing the present application, and do not constitute any limitation on the present application, and in the drawings:

[0052] Figure 1 It is a structural schematic diagram of the present application.

[0053] Figure 2 It is a structural schematic diagram of the workpiece placing area clamping groove of the present application.

[0054] Figure 3 It is a working state schematic diagram of the forging handling robot of the present application Figure 1 .

[0055] Figure 4 Schematic diagram of working state of forging transfer robot Figure 2 .

[0056] Figure 5 Schematic diagram of distribution of end gripper path key points of forging transfer robot.

[0057] Figure 6 Schematic diagram of mechanism of forging transfer robot.

[0058] In the figure:

[0059] 1, die forging press; 2, moving guide rail; 3, forging transfer robot; 4, blanking press; 5, forging manipulator; 6, die heating furnace; 7, secondary heating furnace; 8, blank heating furnace; 9, workpiece placement area; 10, blank placement area; 11, sensor; 301, gripper mechanism; 302, connecting rod mechanism; 303, vehicle body mechanism; 901, clamping groove. DETAILED DESCRIPTION

[0060] As shown in Figures 1 to 6 , the intelligent forging production line of the present application comprises a moving guide rail 2, a forging transfer robot 3 installed on the moving guide rail 2 for transferring blanks and workpieces, a blanking press 4 and a forging manipulator 5 arranged at one end of the moving guide rail 2, a die forging press 1 arranged at the other end of the moving guide rail 2, M blank heating furnaces 8 and N secondary heating furnaces 7 (M, N are natural numbers greater than 0) arranged on one side of the moving guide rail 2, and a blank placement area 10, a workpiece placement area 9 and a die heating furnace 6 arranged on the other side of the moving guide rail 2; the placement planes of the blank placement area 10 and the workpiece placement area 9 are provided with clamping grooves 901 for mechanical positioning; the plurality of clamping grooves 901 of the blank placement area 10 and the workpiece placement area 9 are arranged in an array for mechanical positioning of the blanks and workpieces in the plane; the blank heating furnaces 8, the secondary heating furnaces 7 and the die heating furnace 6 are provided with sensors 11 for controlling the opening and closing of the furnace doors, and when the sensors 11 for controlling the opening and closing of the furnace doors of the blank heating furnaces 8, the secondary heating furnaces 7 and the die heating furnace 6 detect that the forging transfer robot 3 is approaching, the furnace doors are controlled to open, and when the sensors 11 detect that the forging transfer robot 3 is moving away, the furnace doors are controlled to close.

[0061] The die heating furnace 6 is used to slowly preheat the cold die to the optimal working temperature before forging, and to make the internal temperature uniform through heat preservation, so as to eliminate the thermal stress impact caused by temperature difference, thereby ensuring the size accuracy of the forged parts, improving the service life of the die and preventing the die from cracking.

[0062] By planning the whole-line trajectory of the intelligent forging production line as described above, the motion of each joint and the transfer trajectory are optimized, the automation, unmannedness 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.

[0063] As shown in Figure 3 and Figure 4 The forging handling robot 3 includes a vehicle mechanism 303, a connecting rod mechanism 302 and a tongs mechanism 301, the tongs mechanism 301 is connected with the vehicle mechanism 303 through the connecting rod mechanism 302, the vehicle mechanism 303 controls the overall travel and rotation of the forging handling robot 3, the connecting rod mechanism 302 controls the tongs extension, lifting and pitching of the forging handling robot 3, and the tongs mechanism 301 controls the tongs clamping and rotation of the forging handling robot 3.

[0064] The connecting rod mechanism 302 of the forging handling robot 3 is a plane three-degree-of-freedom, which is convenient for trajectory planning.

[0065] The application also provides a whole-line trajectory planning method of an intelligent forging production line, which comprises the following steps:

[0066] S1, according to the forging product, determine the forging process information, such as the number of workpieces, the category, whether secondary heating is needed, determine the forging die, etc., and complete the preheating of the forging die in advance, and preliminarily determine the time distribution of each process of the forging production line and the total time limit;

[0067] S2, execute the whole-line trajectory planning program, and the forging handling robot 3 is reset to zero, and the product forging production process is started;

[0068] S3, the forging handling robot 3 first rotates counterclockwise by 90° and translates to the No. 1 card slot position of the billet placing area 10, and grabs the billet in the No. 1 card slot, then rotates 180° and translates to the corresponding No. 1 card slot position of the billet heating furnace 8, the sensor on the billet heating furnace 8 senses the approach of the forging handling robot 3, controls the furnace door to open, and the forging handling robot 3 places the billet in the No. 1 card slot of the billet heating furnace 8;

[0069] S4, repeat step S3 until all billets are transferred from the card slot of the billet placing area 10 to the corresponding card slot of the billet heating furnace 8, then the furnace door is closed, and the billet heating furnace 8 heats the billet to the specified temperature;

[0070] S5, the forging handling robot 3 translates to the No. 1 card slot position of the billet heating furnace 8, the sensor on the billet heating furnace 8 senses the approach of the forging handling robot 3, controls the furnace door to open, the forging handling robot 3 grabs the billet in the No. 1 card slot of the billet heating furnace 8, then the furnace door is closed, the forging handling robot 3 rotates clockwise by 90° and translates to the billet making press 4, and places the billet in the billet making press 4, and the billet making press 4 works with the forging manipulator 5 to perform billet making forging on the billet;

[0071] S6, according to the workpiece forging process information, determine whether the workpiece needs secondary heating;

[0072] If the forging needs to be heated twice, the forging handling robot 3 takes out the forging in the blank making press 4, rotates counterclockwise by 90° and translates to the secondary heating furnace 7, the sensor on the secondary heating furnace 7 senses that the forging handling robot 3 is close, controls the furnace door to open, the forging handling robot 3 places the blank in the secondary heating furnace, performs secondary heating, after heating is completed, the forging handling robot 3 takes out the forging, rotates counterclockwise by 90° and translates to the die forging press 1;

[0073] If the forging does not need to be heated twice, the forging handling robot 3 takes out the forging in the blank making press 4, rotates 180° and directly translates to the die forging press 1;

[0074] S7, the forging handling robot 3 places the forging in the die forging press 1 for die forging, after the forging in the die forging press 1 is completed, the forging handling robot 3 takes out the completed workpiece in the die forging press 1, rotates counterclockwise by 90° and translates to the first slot position of the workpiece placement area 9, and places the workpiece in the first slot of the workpiece placement area 9;

[0075] S8, repeat steps S5-S7 until all workpieces are completed and placed in the corresponding slots of the workpiece placement area 9 for subsequent work.

[0076] In step S2, the whole line trajectory planning program is executed, which first needs to execute the whole line time distribution optimization main program to plan the time distribution of each sub-stage of the intelligent forging production line;

[0077] 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, among which taking and heating and placing and cooling have no time limit; therefore, the whole line time distribution optimization takes sending blank making and sending forging as the target objects;

[0078] Among them, sending blank making and sending 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 sending blank making and sending forging have ;

[0079] Therefore, the input conditions of the whole line planning time distribution optimization algorithm of the intelligent forging production line are:

[0080] The input variables are: The time distribution of each sub-stage is ;

[0081] 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, is the first Sub-stage;

[0082] The objective function is: the time taken in each sub-stage. Minimum.

[0083] Therefore, the overall planning and optimization procedure for the intelligent forging production line is executed as follows:

[0084] 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. ;

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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:

[0089] The operational space is planned as follows:

[0090] 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.

[0091] 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.

[0092] The above steps are taken as a whole to optimize the impact of the operating space tongs mechanism 301, with the time increment of each key point and the position coordinates of each to-be-optimized as input variables, the operating space tongs mechanism 301 speed, acceleration, impact and total time as constraint conditions, and the particle swarm optimization algorithm is adopted to optimize a set of optimal input parameters as the planning path of the operating space tongs mechanism 301 of the forging handling robot 3.

[0093] The joint space planning is as follows:

[0094] According to the operating space planning path, the optimized key points are discretely encrypted to key points (N is a natural number, and is a natural number, and ), and the preliminary time allocation is performed on the N encrypted key points;

[0095] Kinematics inverse solution, the closed loop vector method is adopted to determine the analytical relationship between the motion amount of each driving joint and the key point coordinates;

[0096] Joint space fitting, by the discrete time of each driving joint at each key point, the motion law of each driving joint is fitted by five B-splines, that is, the displacement, speed, acceleration and jerk (impact) of each driving joint are obtained;

[0097] Dynamics modeling, the Lagrange method is adopted to model the dynamics of the forging handling robot 3, that is, the driving force variation law of each driving joint in the motion process is obtained;

[0098] The above steps are taken as a whole to optimize the impact of the operating space tongs mechanism 301, with the time increment of each key point and the position coordinates of each to-be-optimized as input variables, the operating space tongs mechanism 301 speed, acceleration, impact and total time as constraint conditions, and the particle swarm optimization algorithm is adopted to optimize a set of optimal input parameters as the planning path of the operating space tongs mechanism 301 of the forging handling robot 3;

[0099] Further, when the rotation and translation sub-stage programs are executed, the forging handling robot 3 involves point-to-point trajectory planning, a quintic polynomial interpolation is used to fit the trajectory curve connecting two points, and the planning steps are as follows:

[0100] Let the quintic polynomial be:

[0101]

[0102] In the formula, is a function of the rotation angle or the translation position with respect to time, is the initial time, t is an arbitrary time,​ is the constant term of the quintic polynomial, is the coefficient of the linear term of the quintic polynomial, is the coefficient of the quadratic term of the quintic polynomial, is the coefficient of the cubic term of the quintic polynomial, is the coefficient of the quartic term of the quintic polynomial, is the coefficient of the quintic term of the quintic polynomial;

[0103] The first derivative is:

[0104]

[0105] The second derivative is:

[0106]

[0107] The third derivative is:

[0108]

[0109] For this segment of the quintic polynomial, use two-point (initial time and the final time ) constraints:

[0110]

[0111] where, is the displacement as a function of time, is the velocity as a function of time, is the acceleration as a function of time, i.e. is the displacement as a function of the initial time, is the velocity as a function of the initial time, is the acceleration as a function of the initial time; is the displacement as a function of the final time, is the velocity as a function of the final time, is the acceleration as a function of the final time; is the final time, and are the angles or positions of the start and end points, respectively, and are the velocities of the start and end points, respectively, and are the accelerations of the start and end points, respectively;

[0112] The polynomial coefficients are solved as:

[0113]

[0114] where, , ;

[0115] Finally, the motion curves of trajectory displacement, velocity, acceleration and jerk between two points are obtained.

[0116] Further, the quintic B-spline interpolation is as follows:

[0117] For control points , a node vector , the quintic B-spline curve polynomial expression is constructed: :

[0118]

[0119] In the formula, is the control vertex; is the total number of control vertices; is a variable in the node interval ; is the segment quintic normal B-spline basis function, expressed by the Cox-De Boor recursive formula is:

[0120]

[0121] In the formula, is the degree of the B-spline basis function, and are the and the node, is the segment 0th B-spline basis function, is the node, is the segment quintic B-spline basis function, is the node, is the segment quintic B-spline basis function, is the node;

[0122] When the control vertices are inversely calculated, the key points and the node vector are known, the repetition degree of the curve start and end points is set to , the cumulative chord length parameterization method is used to normalize the time nodes, and the value of the node vector is obtained :

[0123]

[0124] 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;

[0125] Differentiation yields the B-spline curve. r expression of the first derivative for:

[0126]

[0127] 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;

[0128]

[0129] 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;

[0130] 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 , No. 5th-order B-spline curve It can be represented as:

[0131]

[0132] In the formula: For the first Control Vertex ordinal number, For the first One control vertex, For the first 5th-order B-spline basis functions, For the first The first control vertex and the second part The product of B-spline basis functions;

[0133] Given the endpoints of each curve segment, and based on the continuity requirement, the connection point of two consecutive B-spline curve segments must satisfy the following conditions to form a continuous curve. One equation;

[0134]

[0135] In the formula, For the first part End value of the second-order B-spline curve; For the first part The first value of the second-order B-spline curve.

[0136] Since the motion at the initial and final positions is known, the specified start and stop positions are respectively... The start and stop speeds are respectively The start-stop accelerations are respectively This can further increase the number of equations by 6, thus totaling The equations can be solved to obtain Control Vertex ;

[0137]

[0138] Given the node vectors, basis functions, and control vertices, the final B-spline curve can be obtained. Taking the derivative of the B-spline curve yields the velocity of the fifth-order B-spline curve. acceleration And accelerometer (impact) for:

[0139]

[0140] wherein, is a function of velocity and time; is a function of acceleration and time; is a function of jerk and time; is the first derivative of a quintic B-spline curve; is the second derivative of a quintic B-spline curve; is the third derivative of a quintic B-spline curve;

[0141] is the first derivative of the nth control point; is the second derivative of the nth control point; is the third derivative of the nth control point; is the fourth derivative of the nth control point; is the fifth derivative of the nth control point; is the nth B-spline basis function of the mth segment; is the nth B-spline basis function of the mth segment; is the nth B-spline basis function of the mth segment; is the specific summation formula for the first derivative of a quintic B-spline curve; is the specific summation formula for the second derivative of a quintic B-spline curve; is the specific summation formula for the third derivative of a quintic B-spline curve. Further, as shown in FIG. 6, in the planning step, the inverse kinematics solution is as follows: According to the closed-loop vector method, the inverse kinematics solution of the forging transfer robot 3 is expressed as the known end effector mechanism 301 passing through the key point coordinates , the motion amount of each driving joint , can be listed as follows:

[0142] Figure 6

[0143] According to the closed-loop vector method, the inverse kinematics solution of the forging transfer robot 3 is expressed as the known end effector mechanism 301 passing through the key point coordinates , the motion amount of each driving joint , can be listed as follows:

[0144]

[0145] wherein, is the coordinate vector of the link , the direction from to , the size is the length of ; is the coordinate vector of the link , the direction from to , the size is the length of ;​​​​ M coordinate vector of link M , direction from to , length of M ; coordinate vector of link , length of

[0146] Solving the vector equation system, the motion of each driving joint (inverse kinematics) is as follows:

[0147]

[0148] wherein, is the driving amount of the first joint, is the driving amount of the second joint, is the component of in the direction of , is the component of in the direction of , is the component of in the direction of , is the component of in the direction of ;

[0149] Further, the dynamics modeling is as follows:

[0150] Compared with the driving force of each driving joint, the joint friction of the forging transfer robot 3 is negligible in value, so the joint friction is not considered when using the Lagrange method to model the dynamics;

[0151] The Lagrange equation is as follows:

[0152]

[0153] wherein, 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 first​​​ a joint, a first drive force or drive torque;

[0154] may be further represented as:

[0155]

[0156] The premise of establishing a dynamic model based on Lagrange equation: there is a set of generalized coordinates representing the kinetic energy and potential energy of the system;

[0157] The kinetic energy of a rigid body is represented as:

[0158]

[0159] In the formula, is the total mass of the rigid body, is the translational velocity of the rigid body, is the angular velocity of the rigid body, is represented as the translational kinetic energy of the rigid body, is represented as the rotational kinetic energy of the rigid body;

[0160] Where the rigid body inertia tensor , only depends on the shape and mass distribution of the object, is represented as:

[0161]

[0162] In the formula, the main diagonal elements of the matrix , , are the moments of inertia of the rigid body around , , The non-diagonal elements of the matrix , , are the products of inertia of the rigid body; , , are equal to , , respectively;

[0163] The linear velocity and angular velocity of any point on the rigid body can be represented by the Jacobian matrix and joint velocity:

[0164]

[0165]

[0166] In the formula, Add rigid bodies to the robot f The linear velocity of Jacobi, Add rigid bodies to the robot f angular velocity Jacobi, This is the joint drive quantity. Joint velocity;

[0167] Therefore, the total kinetic energy of the system Represented as:

[0168]

[0169] In the formula, Represented as a rigid body The inertial tensor at the initial position, m f Let the mass of the rigid body be... Represented as a rigid body The rotation matrix, n Total is the total number of rigid bodies in the robot;

[0170]

[0171] In the formula, Represented as the system's inertia matrix;

[0172]

[0173] potential energy of a rigid body Represented as:

[0174]

[0175] In the formula, Let the coordinates be those of the center of mass of the rigid body. This refers to the local gravitational acceleration.

[0176] Therefore, the total potential energy of the system Represented as:

[0177]

[0178] In the formula, For the first The coordinates of the center of mass of a rigid body;

[0179] By rearranging the above formulas, we can establish the robot's dynamic equations, ultimately obtaining the Lagrange equations as follows:

[0180]

[0181] In the formula, 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.

[0182] Further, the input conditions of the particle swarm algorithm are:

[0183] Operation space:

[0184] The input variable is: a time increment , and position coordinates;

[0185] The constraint condition is that the speed, acceleration, impact of the tongs mechanism 301 and the total time are set to extreme values;

[0186] The objective function is that the impact of the tongs mechanism 301 is minimum;

[0187] Joint space:

[0188] The input variable is: a time increment ;

[0189] The constraint condition is that the driving joint speed, acceleration, impact and joint driving force and the total time are set to extreme values;

[0190] The objective function is that the driving joint impact is minimum;

[0191] The extreme value setting depends on engineering experience or experimental analysis.

[0192] The above embodiments of the present application are described in detail, but the content described is only the preferred embodiments of the present application, and cannot be considered to limit the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application should still be included in the scope of the present 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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