Pickup truck rear bumper frame assembly and manufacturing device and method thereof

Abstract

The present invention discloses a pickup truck rear bumper frame assembly and a manufacturing device and method thereof, comprising a bumper body, both ends of the bumper body are fixedly installed with extension plates, the bottom end of the bumper body is fixedly installed with an anti-collision mechanism, the anti-collision mechanism comprises a limit block and an anti-collision shell, the present invention provides an anti-collision mechanism, the anti-collision shell and a rubber plate buffer the impact force from the outside of the bumper, the anti-collision shell moves toward the limit block under the impact force, and a first shock absorber and a first shock absorbing spring perform secondary buffering on the impact force, thereby avoiding deformation or damage of the pickup truck rear bumper frame after the bumper is subjected to a strong collision, thereby extending the service life of the pickup truck rear bumper frame; by providing a first jacking mechanism and a second jacking mechanism, the first jacking mechanism assists the top mold in demoulding the product from both sides of the top mold, and the second jacking mechanism assists in demoulding from the bottom of the bottom mold, thereby improving the demoulding efficiency of the product.

Description

Technical Field

[0001] The invention relates to the technical field of automobile parts, and in particular to a pickup truck rear bumper frame assembly and a manufacturing device and method thereof. Background Art

[0002] The function of the bumper frame is to absorb and mitigate external impact force when the vehicle or driver is subjected to force in a collision. The bumper is a device that produces a buffer, which reduces the damage to people in the car and protects the safety of people and trucks. The front bumper frame is an anti-collision beam: an anti-collision beam is a device used to reduce the absorption of collision energy when the vehicle is hit. It is composed of a main beam, an energy absorption box, and a mounting plate connected to the car. The main beam and the energy absorption box can effectively absorb the collision energy when the vehicle collides at a low speed, and minimize the damage to the longitudinal beam of the vehicle body caused by the impact force. In this way, it plays a protective role for the vehicle. The anti-collision beam is generally hidden inside the bumper and the inside of the door. Under the action of a large impact force, when the elastic material can no longer buffer the energy, it truly plays a role in protecting the occupants in the car. Not every car has an anti-collision beam. It is mostly made of metal, such as aluminum alloy, steel pipe, etc. Anti-collision beams are generally made of iron. High-end cars are generally made of aluminum alloy, and some cars are made of very hard plastic.

[0003] However, the traditional pickup truck rear bumper frame has the following disadvantages:

[0004] The traditional pickup truck rear bumper frame has a general anti-collision effect. If it is subjected to a strong collision, the pickup truck rear bumper frame may be deformed or damaged, affecting the service life of the pickup truck rear bumper frame. Summary of the invention

[0005] The purpose of the present invention is to provide a pickup truck rear bumper frame assembly and a manufacturing device and method thereof, so as to solve the problem that the traditional pickup truck rear bumper frame proposed in the above background technology has a general anti-collision effect, and if it is subjected to a strong collision, the pickup truck rear bumper frame may be deformed or damaged, which affects the service life of the pickup truck rear bumper frame.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a pickup truck rear bumper frame assembly, comprising a bumper body, both ends of the bumper body are fixedly installed with extension plates, the bottom end of the bumper body is fixedly installed with an anti-collision mechanism, the anti-collision mechanism comprises a limit block and an anti-collision shell, both sides of the bottom end of the limit block are fixedly installed with first shock-absorbing springs, the bottom ends of the two first shock-absorbing springs are respectively fixedly connected to the two sides of the inner wall of the anti-collision shell, an angle seat is fixedly installed in the middle of the bottom end of the inner wall of the anti-collision shell, a first shock absorber is installed inside the angle seat, one end of the first shock absorber away from the angle seat is connected to the side directly opposite to the limit block, a rubber plate is laid on the outside of the anti-collision shell, a reinforcement groove is opened in the middle of the top end of the bumper body, a plurality of reinforcement members are fixedly installed inside the reinforcement groove, and the plurality of reinforcement members each comprise a fixed block and a first stiffening rib, one end of the fixed block is fixedly connected to one end of the first stiffening rib, and the other end of the first stiffening rib is fixedly installed with a second stiffening rib.

[0007] As a preferred technical solution of the present invention, assembly blocks are fixedly installed on both sides of the top of the bumper body, mounting plates are fixedly installed on the tops of the two assembly blocks, first mounting pieces are fixedly installed on both sides of the tops of the two mounting plates, second mounting pieces are fixedly installed on both ends of one side of the bumper body, and the bumper body is fixed on the pickup truck by the first mounting piece and the second mounting piece.

[0008] As a preferred technical solution of the present invention, the side of the limit block away from the first shock-absorbing spring is fixedly connected to the side opposite to the bumper body, one end of the fixed block away from the first stiffening rib and one end of the second stiffening rib away from the first stiffening rib are both fixedly connected to the side opposite to the reinforcement groove, and the anti-collision mechanism is installed on the bumper body through the limit block.

[0009] The present invention provides a manufacturing device for a pickup truck rear bumper frame assembly, comprising a fixed base, a top plate being provided at the top of the fixed base, a lifting cylinder being fixedly installed at the middle of the top of the top plate, a lifting platform being fixedly installed at the movable end of the lifting cylinder, a first lifting mechanism being slidably connected to both sides of the top of the lifting platform, a top mold being fixedly installed at the middle of the bottom end of the lifting platform, a second lifting mechanism being fixedly installed at the middle of the top of the fixed base, the second lifting mechanism comprising a connecting platform and a lifting platform, second shock absorbers being fixedly installed at both sides of the top of the connecting platform, the tops of the two second shock absorbers being fixedly connected to both sides of the bottom end of the lifting platform respectively, and the top of the lifting platform being fixedly connected to the bottom end of the bottom mold.

[0010] As a preferred technical solution of the present invention, the two first jacking mechanisms both include a jacking plate and a length rod, the top end of the jacking plate is fixedly connected to the bottom end of the length rod, a baffle is fixedly installed on the top end of the length rod, a connecting spring is fixedly installed on the top end of the jacking plate, the two length rods are slidably connected to the lifting platform, the ends of the two connecting springs away from the jacking plate are fixedly connected to the side opposite to the lifting platform, the jacking plate moves synchronously with the lifting platform, the jacking plate pushes the length rod to slide along the lifting platform, and the jacking plate squeezes the connecting spring from one side, the connecting spring is elastic, and the connecting spring undergoes elastic deformation to buffer the extrusion force, and when the lifting platform rises, the top mold is pushed in the reverse direction to be demolded under the action of the first jacking mechanism.

[0011] As a preferred technical solution of the present invention, vertical plates are fixedly installed on both sides of the top of the fixed base, and the tops of the two vertical plates are fixedly connected to the two sides of the bottom of the top plate respectively, and height grooves are opened on the opposite sides of the two vertical plates, and height blocks are slidably connected inside the two height grooves, and the opposite sides of the two height blocks are fixedly connected to the two ends of the lifting platform respectively, and the lifting cylinder performs telescopic movement, and the lifting cylinder pushes the lifting platform from the top, and the lifting platform drives the height block to slide along the height groove, thereby improving the lifting stability of the lifting platform.

[0012] As a preferred technical solution of the present invention, a first assembly shell is fixedly installed in the middle of the bottom end of the lifting platform, the bottom end of the first assembly shell is slidably connected to the second assembly shell, the bottom end of the second assembly shell is fixedly connected to the top of the connecting platform, and the connecting platform is provided with two movable grooves respectively located on one side of the second shock absorber, and movable rods are fixedly installed inside the two movable grooves, and movable blocks are slidably connected to the middle parts of the two movable rods, and second shock-absorbing springs are fixedly installed on the opposite sides of the two movable blocks, and the ends of the two second shock-absorbing springs away from the movable blocks are opposite to the side of the movable groove. The jacking platform is pressed from the top, and the second shock absorber buffers the squeezing force. The jacking platform drives the first assembly shell to slide relative to the second assembly shell, and the first assembly shell drives the positioning seat to move synchronously. The positioning seat drives the synchronous rod to push the movable block. The movable block squeezes the second shock-absorbing spring from one side. The second shock-absorbing spring is elastic, and the second shock-absorbing spring undergoes elastic deformation to buffer the squeezing force.

[0013] As a preferred technical solution of the present invention, the bottom end of the connecting platform is fixedly connected to the fixed base, and the second lifting mechanism is installed on the fixed base through the connecting platform.

[0014] The present invention provides a method for manufacturing a pickup truck rear bumper frame assembly, comprising the following steps:

[0015] Step 1: Injecting raw materials: injecting raw materials for preparing the pickup truck rear bumper frame assembly between the top mold and the bottom mold;

[0016] Step 2: Buckle and extrude: The lifting cylinder pushes the lifting platform from the top, and the lifting platform drives the top mold into the bottom mold to buckle the raw material of the pickup truck rear bumper frame assembly;

[0017] Step 3: Natural cooling: The formed pickup truck rear bumper frame assembly between the top mold and the bottom mold is naturally cooled;

[0018] Step 4: Product demoulding: The first lifting mechanism and the second lifting mechanism cooperate with each other to demould the product.

[0019] A pickup truck rear bumper frame assembly, characterized in that a lifting cylinder 14 pushes a lifting platform 15 from the top, the lifting platform 15 drives a top mold 18 to enter a bottom mold 20 to buckle the pickup truck rear bumper frame assembly raw material, and an intelligent control system and image recognition technology are used to automatically correct the alignment of the top mold and the bottom mold, specifically including the following steps:

[0020] Step 1: System hardware deployment

[0021] To further improve the accuracy and reliability of the alignment system, multiple sensors and cameras can be added to collect more data. These sensors may include:

[0022] High-resolution camera: Install cameras at fixed positions on the top mold and bottom mold respectively. The installation position should ensure that the camera can clearly capture the mold image during the entire alignment process. Adjust the angle and height of the camera to ensure that the camera can capture the key feature areas of the top mold and bottom mold;

[0023] Displacement sensor: used to accurately measure the relative displacement of the top mold and the bottom mold, installed at the relative position of the top mold and the bottom mold;

[0024] Acceleration sensor: used to monitor the acceleration of the top mold and the bottom mold during movement, to help analyze dynamic changes, and installed on the moving path of the top mold and the bottom mold;

[0025] Temperature sensor: used to monitor the ambient temperature to ensure that temperature changes do not affect the alignment accuracy, installed in the surrounding environment of the mold;

[0026] Pressure sensor: used to monitor the pressure between the top mold and the bottom mold to ensure that the mold or material will not be damaged during the fastening process. It is installed on the contact surface of the top mold and the bottom mold.

[0027] Step 2: Data acquisition and processing

[0028] Image data acquisition:

[0029] Start the camera to collect image data of the top mold and bottom mold in real time. The image data is transmitted to the image processing unit, and grayscale processing, denoising and edge detection are performed through the OpenCV library:

[0030] Image acquisition module: obtain image data through the GigE interface;

[0031] Preprocessing module: grayscale processing, denoising and edge detection;

[0032] Image data processing:

[0033] The image data is fused with the sensor data and input into the image processing unit. The key feature points in the image are extracted using the SIFT algorithm. The feature points are matched using the FLANN matcher to calculate the position deviation. , combined with displacement, acceleration, temperature and pressure data, and input into the RNN model for prediction:

[0034] Feature extraction module: Feature extraction is used to extract key feature points and descriptors from images. These feature points and descriptors can be used for image matching and recognition. The SIFT algorithm is used. The SIFT algorithm detects feature points at different scales by calculating the Gaussian pyramid of the image.

[0035] Feature matching module: Feature matching is used to pair feature points in two images to determine the correspondence between them. The FLANN matcher is used. The FLANN matcher finds the nearest neighbors between descriptors through fast approximate nearest neighbor search.

[0036] Data fusion: image data is fused with sensor data as input data for subsequent processing;

[0037] Position deviation calculation module: calculates the relative position deviation of the top mold and the bottom mold based on the image feature points and sensor data;

[0038] ;

[0039] in, represents the matching point, is the total number of matching points, is the average position deviation of the top mold and the bottom mold in the horizontal direction, is the average position deviation of the top mold and the bottom mold in the vertical direction, is the average rotation angle deviation between the top and bottom molds, It is The position deviation of the matching points in the horizontal direction, It is The position deviation of the matching points in the vertical direction, It is The rotation angle deviation of the matching points;

[0040] We use the Bat Optimization Algorithm (BA) to optimize the hyperparameters of the recurrent neural network (RNN), including the learning rate and the number of hidden layer nodes, to improve the prediction accuracy of the model;

[0041] initialization:

[0042] Bat colony size , maximum number of iterations , frequency range , initial loudness , initial pulse emission rate , initialize the position of each bat and speed ;

[0043] Frequency, speed and position updates:

[0044] Update frequency:

[0045] ;

[0046] in, It is The frequency of bats, is the minimum value of the frequency range, is the maximum value of the frequency range, is a A random number between

[0047] Update speed and location:

[0048] ;

[0049] ;

[0050] in, It is Bats at time step speed, It is Bats at time step speed, It is Bats at time step location, It is Bats at time step location, is the global optimal position in the current iteration (i.e. the best position among all the bats at present), It is The frequency of bats;

[0051] Loudness and pulse rate updates:

[0052] Update the loudness and pulse emission frequency:

[0053] ;

[0054] ;

[0055] in, It is Bats at time step The loudness, is the loudness attenuation factor, It is Bats at time step The loudness, It is Bats at time step The pulse emission rate, It is The pulse firing rate of a bat at the initial time step, is an exponential function, is the pulse emission rate increasing factor, is the current iteration number;

[0056] The bat optimization algorithm finds the optimal hyperparameter combination that minimizes the loss function. These optimized hyperparameters will be used to train the RNN model to improve the prediction accuracy of the model.

[0057] Step 3: RNN model training and optimization

[0058] Set acquisition data, image feature data , sensor data , actual position deviation label ;

[0059] RNN model structure:

[0060] Input layer: input image feature data and sensor data, recorded as vector ,in, is the image feature data, is sensor data;

[0061] Initialization parameters: weight matrix , the bias vector ;

[0062] Hidden layer: The hidden layer state of the recurrent neural network is , at time step Updated to:

[0063] ;

[0064] in, is the time step The hidden layer state vector is is the weight matrix input to the hidden layer, is the time step The input vector is is the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state vector is is the bias vector of the hidden layer, Represents the activation function ;

[0065] Output layer: The output layer of the recurrent neural network is the predicted relative position deviation , at time step Calculated as:

[0066] ;

[0067] in, is the time step The predicted position deviation in the horizontal direction, is the time step The predicted position deviation in the vertical direction, is the time step The predicted rotation angle deviation is is the time step The output vector of is the weight matrix from the hidden layer to the output layer, is the time step The hidden layer state vector is is the bias vector of the output layer;

[0068] Loss function:

[0069] The mean square error (MSE) is used as the loss function, which is defined as:

[0070] ;

[0071] in, is the sample size, It is The actual position deviation of samples in the horizontal direction, It is The reagent position deviation of each sample in the vertical direction, It is The reagent rotation angle deviation of each sample, It is The predicted position deviation of samples in the horizontal direction, It is The predicted position deviation of samples in the vertical direction, It is The predicted rotation angle deviation of samples;

[0072] Backward Propagation:

[0073] Calculate the gradient of the loss function with respect to weights and biases and update the parameters. The backpropagation algorithm includes the following steps:

[0074] Output layer gradient:

[0075] ;

[0076] ;

[0077] in, is the loss function, is the weight matrix from the hidden layer to the output layer, is the sample size, It is The predicted output vector of samples is It is The actual output vector of samples, It is The transpose of the hidden layer state vector of samples, is the bias vector of the output layer;

[0078] Hidden layer gradient:

[0079] ;

[0080] in, is the time step The hidden layer state error vector is is the transpose of the weight matrix from the hidden layer to the output layer, is the time step The predicted output vector is is the time step The actual output vector, is the transpose of the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state error vector is is the hidden layer state vector, represents element-wise multiplication (Hadamard product), Represents the derivative of the activation function;

[0081] Update weights and biases:

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] in, is the learning rate, is the loss function, is the weight matrix input to the hidden layer, is the weight matrix from hidden layer to hidden layer, is the weight matrix from the hidden layer to the output layer, is the bias vector of the hidden layer, is the bias vector of the output layer;

[0088] Step 4: Real-time correction

[0089] According to the relative position deviation predicted by the RNN model, the predicted relative position deviation Input to the control system, the position of the top mold and the bottom mold is automatically adjusted through the PID control system. The PID controller is used to generate control signals to minimize position deviation;

[0090] Control signal formula:

[0091] ;

[0092] in, Respectively represent proportional gain, integral gain, and differential gain, among which, is the output of the controller, is the current time step error, is the error;

[0093] Actuator adjustment:

[0094] Electric motor: used to accurately adjust the position of the top mold and the bottom mold;

[0095] Hydraulic cylinder: used to provide greater adjustment force;

[0096] Real-time feedback and error correction:

[0097] The alignment accuracy is continuously adjusted and optimized through the feedback mechanism, and the position deviation is monitored in real time through sensors and cameras. Correction is made immediately based on the new position deviation to ensure that the position error is within an acceptable range.

[0098] Compared with the prior art, the present invention has the following beneficial effects:

[0099] 1. By setting up the anti-collision mechanism, the anti-collision shell and the rubber plate buffer the impact force from the outside of the bumper. The anti-collision shell moves towards the limit block under the impact force, and the first shock absorber and the first shock absorbing spring buffer the impact force for the second time, so as to avoid deformation or damage of the pickup truck rear bumper frame after the bumper is hit by a strong collision, thereby extending the service life of the pickup truck rear bumper frame;

[0100] 2. By setting the first jacking mechanism and the second jacking mechanism, the first jacking mechanism assists the top mold to demould the product from both sides of the top mold, and the second jacking mechanism assists demoulding from the bottom of the bottom mold, thereby improving the demoulding efficiency of the product;

[0101] 3. This patent combines the bat optimization algorithm (BA) and the recursive neural network (RNN) model to achieve precise alignment of the top mold and the bottom mold during the manufacturing process of the pickup truck rear bumper frame. First, data is collected in real time through high-resolution cameras and multiple sensors to ensure the accuracy and real-time nature of the data. Secondly, the RNN model is used to process and predict the image feature data and sensor data to generate the relative position deviation of the top mold and the bottom mold. Subsequently, the hyperparameters of the RNN model, such as the learning rate and the number of hidden layer nodes, are optimized by the BA algorithm to improve the model prediction accuracy and response speed. The BA algorithm achieves efficient search of the bat colony by adjusting the frequency, speed and position, and enhances the global search capability and local search accuracy of the algorithm by dynamically updating the loudness and pulse emission rate. Finally, combined with the PID control system, the predicted relative position deviation is corrected in real time, and the position of the top mold and the bottom mold is automatically adjusted to achieve high-precision alignment. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 A three-dimensional diagram of the bumper frame assembly of the present invention;

[0103] Figure 2 It is a side view of the bumper frame assembly of the present invention;

[0104] Figure 3 is a side view of the anti-collision mechanism of the present invention;

[0105] Figure 4 A side view of the manufacturing device of the present invention;

[0106] Figure 5 It is a side view of the first lifting mechanism of the present invention;

[0107] Figure 6 is a side view of the second lifting mechanism of the present invention;

[0108] Figure 7 is a flow chart of the present invention;

[0109] Figure 8 The figure is a flow chart of the intelligent control system of the bumper frame assembly of the present invention.

[0110] In the figure: 1. bumper body; 2. assembly block; 3. mounting plate; 4. first mounting plate; 5. second mounting plate; 6. reinforcement groove; 7. reinforcement member; 71. fixing block; 72. first stiffening rib; 73. second stiffening rib; 8. anti-collision mechanism; 81. anti-collision shell; 82. rubber plate; 83. angle seat; 84. first shock-absorbing spring; 85. limit block; 86. first shock absorber; 9. extension plate; 10. fixed base; 11. vertical plate; 12. height groove; 13. top plate; 14. lifting cylinder; 15. lifting platform; 16. Height block; 17. First jacking mechanism; 171. Jacking plate; 172. Length rod; 173. Connecting spring; 174. Baffle; 18. Top mold; 19. Second jacking mechanism; 1901. Connecting platform; 1902. Movable groove; 1903. Movable rod; 1904. Second shock absorber; 1905. Jacking platform; 1906. First assembly shell; 1907. Positioning seat; 1908. Synchronous rod; 1909. Movable block; 1910. Second shock-absorbing spring; 1911. Second assembly shell; 20. Bottom mold. DETAILED DESCRIPTION

[0111] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0112] See also Figure 1-8 The present invention provides a pickup truck rear bumper frame assembly, including a bumper body 1, both ends of the bumper body 1 are fixedly installed with extension plates 9, the bottom end of the bumper body 1 is fixedly installed with an anti-collision mechanism 8, the anti-collision mechanism 8 includes a limit block 85 and an anti-collision shell 81, both sides of the bottom end of the limit block 85 are fixedly installed with first shock absorbing springs 84, the bottom ends of the two first shock absorbing springs 84 are respectively fixedly connected to the two sides of the inner wall of the anti-collision shell 81, and the middle part of the bottom end of the inner wall of the anti-collision shell 81 is fixedly installed with an angle seat 83, and the angle seat 83 A first shock absorber 86 is installed inside, and one end of the first shock absorber 86 away from the angle seat 83 is connected to the side opposite to the limit block 85. A rubber plate 82 is laid on the outer side of the anti-collision shell 81. A reinforcement groove 6 is opened in the middle of the top of the bumper body 1. A plurality of reinforcing members 7 are fixedly installed inside the reinforcement groove 6. The plurality of reinforcing members 7 include a fixing block 71 and a first stiffening rib 72. One end of the fixing block 71 is fixedly connected to one end of the first stiffening rib 72, and the other end of the first stiffening rib 72 is fixedly installed with a second stiffening rib 73.

[0113] Assembly blocks 2 are fixedly installed on both sides of the top of the bumper body 1, mounting plates 3 are fixedly installed on the tops of the two assembly blocks 2, first mounting pieces 4 are fixedly installed on both sides of the tops of the two mounting plates 3, and second mounting pieces 5 are fixedly installed on both ends of one side of the bumper body 1. The bumper body 1 is fixed on the pickup truck through the first mounting piece 4 and the second mounting piece 5.

[0114] The side of the limit block 85 away from the first shock-absorbing spring 84 is fixedly connected to the side of the bumper body 1 facing the same, and the end of the fixed block 71 away from the first stiffening rib 72 and the end of the second stiffening rib 73 away from the first stiffening rib 72 are both fixedly connected to the side facing the reinforcement groove 6. The anti-collision mechanism 8 is installed on the bumper body 1 through the limit block 85.

[0115] The present invention provides a manufacturing device for a pickup truck rear bumper frame assembly, comprising a fixed base 10, a top plate 13 is provided at the top of the fixed base 10, a lifting cylinder 14 is fixedly installed in the middle of the top of the top plate 13, a lifting platform 15 is fixedly installed at the movable end of the lifting cylinder 14, a first lifting mechanism 17 is slidably connected to both sides of the top of the lifting platform 15, a top mold 18 is fixedly installed in the middle of the bottom of the lifting platform 15, a second lifting mechanism 19 is fixedly installed in the middle of the top of the fixed base 10, the second lifting mechanism 19 comprises a connecting platform 1901 and a lifting platform 1905, second shock absorbers 1904 are fixedly installed on both sides of the top of the connecting platform 1901, the tops of the two second shock absorbers 1904 are respectively fixedly connected to the two sides of the bottom of the lifting platform 1905, and the top of the lifting platform 1905 is fixedly connected to the bottom of the bottom mold 20.

[0116] The two first lifting mechanisms 17 each include a lifting plate 171 and a length rod 172, the top end of the lifting plate 171 is fixedly connected to the bottom end of the length rod 172, a baffle 174 is fixedly installed on the top end of the length rod 172, a connecting spring 173 is fixedly installed on the top end of the lifting plate 171, the two length rods 172 are slidably connected to the lifting platform 15, the ends of the two connecting springs 173 away from the lifting plate 171 are fixedly connected to the side directly opposite to the lifting platform 15, the lifting plate 171 moves synchronously with the lifting platform 15, the lifting plate 171 pushes the length rod 172 to slide along the lifting platform 15, and the lifting plate 171 squeezes the connecting spring 173 from one side, the connecting spring 173 is elastic, and the connecting spring 173 undergoes elastic deformation to buffer the squeezing force, and when the lifting platform 15 rises, the top mold 18 is pushed in the reverse direction for demoulding under the action of the first lifting mechanism 17.

[0117] Vertical plates 11 are fixedly installed on both sides of the top of the fixed base 10, and the tops of the two vertical plates 11 are fixedly connected to the two sides of the bottom of the top plate 13 respectively. Height grooves 12 are opened on the opposite sides of the two vertical plates 11, and height blocks 16 are slidably connected inside the two height grooves 12. The opposite sides of the two height blocks 16 are fixedly connected to the two ends of the lifting platform 15 respectively. The lifting cylinder 14 performs telescopic movement, and the lifting cylinder 14 pushes the lifting platform 15 from the top. The lifting platform 15 drives the height block 16 to slide along the height groove 12, thereby improving the stability of the lifting of the lifting platform 15.

[0118] A first assembly shell 1906 is fixedly installed in the middle of the bottom end of the lifting platform 1905, and a second assembly shell 1911 is slidably connected to the bottom end of the first assembly shell 1906. The bottom end of the second assembly shell 1911 is fixedly connected to the top of the connecting platform 1901. The connecting platform 1901 is provided with two movable grooves 1902, which are respectively located on one side of the second shock absorber 1904. The insides of the two movable grooves 1902 are fixedly installed with movable rods 1903. The middle parts of the two movable rods 1903 are slidably connected with movable blocks 1909. Second shock-absorbing springs 1910 are fixedly installed on the opposite sides of the two movable blocks 1909. The ends of the two second shock-absorbing springs 1910 away from the movable blocks 1909 are fixedly connected to the side opposite to the movable groove 1902. A positioning seat 1907 is fixedly installed, and both sides of the positioning seat 1907 are rotatably connected with synchronization rods 1908. The ends of the two synchronization rods 1908 away from the positioning seat 1907 are respectively connected to the sides directly opposite to the two movable blocks 1909. The lifting platform 1905 squeezes from the top, and the second shock absorber 1904 buffers the squeezing force. The lifting platform 1905 drives the first assembly shell 1906 to slide relative to the second assembly shell 1911, and the first assembly shell 1906 drives the positioning seat 1907 to move synchronously. The positioning seat 1907 drives the synchronization rod 1908 to push the movable block 1909. The movable block 1909 squeezes the second shock-absorbing spring 1910 from one side. The second shock-absorbing spring 1910 is elastic, and the second shock-absorbing spring 1910 undergoes elastic deformation to buffer the squeezing force.

[0119] The bottom end of the connecting platform 1901 is fixedly connected to the fixed base 10 , and the second lifting mechanism 19 is installed on the fixed base 10 through the connecting platform 1901 .

[0120] The present invention provides a method for manufacturing a pickup truck rear bumper frame assembly, comprising the following steps:

[0121] Step 1: Injecting raw materials: Injecting raw materials for preparing the pickup truck rear bumper frame assembly between the top mold 18 and the bottom mold 20;

[0122] Step 2: buckle and extrude: the lifting cylinder 14 pushes the lifting platform 15 from the top, and the lifting platform 15 drives the top mold 18 into the bottom mold 20 to buckle the pickup truck rear bumper frame assembly raw material;

[0123] Step 3: Natural cooling: The formed pickup truck rear bumper frame assembly between the top mold 18 and the bottom mold 20 is naturally cooled;

[0124] Step 4: Product demoulding: The first lifting mechanism 17 and the second lifting mechanism 19 cooperate with each other to demould the product.

[0125] In the present invention, the raw materials for preparing the pickup truck rear bumper frame assembly are injected between the top mold 18 and the bottom mold 20; the lifting cylinder 14 pushes the lifting platform 15 from the top, the lifting cylinder 14 performs telescopic movement, the lifting cylinder 14 pushes the lifting platform 15 from the top, the lifting platform 15 drives the height block 16 to slide along the height groove 12, and improves the lifting stability of the lifting platform 15, the lifting platform 15 drives the top mold 18 into the bottom mold 20 to buckle the pickup truck rear bumper frame assembly raw materials; the molded pickup truck rear bumper frame assembly between the top mold 18 and the bottom mold 20 is naturally cooled; the lifting platform 1905 is extruded from the top, the second shock absorber 1904 buffers the extrusion force, and the lifting platform 1905 drives the first assembly shell 1906 to slide relative to the second assembly shell 1911, and the first assembly shell 1906 drives the fixed The positioning seat 1907 moves synchronously, and the positioning seat 1907 drives the synchronization rod 1908 to push the movable block 1909. The movable block 1909 squeezes the second shock-absorbing spring 1910 from one side. The second shock-absorbing spring 1910 is elastic, and the second shock-absorbing spring 1910 undergoes elastic deformation to buffer the squeezing force. The lifting plate 171 moves synchronously with the lifting platform 15. The lifting plate 171 pushes the length rod 172 to slide along the lifting platform 15, and the lifting plate 171 squeezes the connecting spring 173 from one side. The connecting spring 173 is elastic, and the connecting spring 173 undergoes elastic deformation to buffer the squeezing force. When the lifting platform 15 rises, the top mold 18 is pushed in the reverse direction to demold under the action of the first lifting mechanism 17, and the first lifting mechanism 17 and the second lifting mechanism 19 cooperate with each other to demold the product.

[0126] The technical solution is a pickup truck rear bumper frame assembly, characterized in that a lifting cylinder 14 pushes a lifting platform 15 from the top, and the lifting platform 15 drives a top mold 18 to enter a bottom mold 20 to buckle the pickup truck rear bumper frame assembly raw material, and an intelligent control system and image recognition technology are used to automatically correct the alignment of the top mold and the bottom mold, specifically including the following steps:

[0127] Step 1: System hardware deployment

[0128] To further improve the accuracy and reliability of the alignment system, multiple sensors and cameras can be added to collect more data. These sensors may include:

[0129] High-resolution camera: Select a high-definition camera with a resolution of 1920x1080, a camera with a frame rate of 30fps, a zoom camera with a focal length range of 4mm to 12mm, and a wide-angle lens with a viewing angle range of 90 degrees to 120 degrees. Install the cameras at fixed positions on the top mold and the bottom mold respectively. The installation position should ensure that the camera can clearly capture the mold image during the entire alignment process. Adjust the angle and height of the camera to ensure that the camera can capture the key feature areas of the top mold and the bottom mold.

[0130] Displacement sensor: used to accurately measure the relative displacement of the top mold and the bottom mold, installed at the relative position of the top mold and the bottom mold;

[0131] Acceleration sensor: used to monitor the acceleration of the top mold and the bottom mold during movement, to help analyze dynamic changes, and installed on the moving path of the top mold and the bottom mold;

[0132] Temperature sensor: used to monitor the ambient temperature to ensure that temperature changes do not affect the alignment accuracy, installed in the surrounding environment of the mold;

[0133] Pressure sensor: used to monitor the pressure between the top mold and the bottom mold to ensure that the mold or material will not be damaged during the fastening process. It is installed on the contact surface of the top mold and the bottom mold.

[0134] Step 2: Data acquisition and processing

[0135] Image data acquisition:

[0136] Start the camera to collect image data of the top mold and bottom mold in real time. The image data is transmitted to the image processing unit, and grayscale processing, denoising and edge detection are performed through the OpenCV library:

[0137] Image acquisition module: obtain image data through the GigE interface;

[0138] Preprocessing module: grayscale processing, denoising and edge detection;

[0139] Image data processing:

[0140] The image data is fused with the sensor data and input into the image processing unit. The key feature points in the image are extracted using the SIFT algorithm. The feature points are matched using the FLANN matcher to calculate the position deviation. , combined with displacement, acceleration, temperature and pressure data, and input into the RNN model for prediction:

[0141] Feature extraction module: Feature extraction is used to extract key feature points and descriptors from images. These feature points and descriptors can be used for image matching and recognition. The SIFT algorithm is used. The SIFT algorithm detects feature points at different scales by calculating the Gaussian pyramid of the image.

[0142] Feature matching module: Feature matching is used to pair feature points in two images to determine the correspondence between them. The FLANN matcher is used. The FLANN matcher finds the nearest neighbors between descriptors through fast approximate nearest neighbor search.

[0143] Data fusion: image data is fused with sensor data as input data for subsequent processing;

[0144] Position deviation calculation module: calculates the relative position deviation of the top mold and the bottom mold based on the image feature points and sensor data;

[0145] ;

[0146] in, represents the matching point, is the total number of matching points, is the average position deviation of the top mold and the bottom mold in the horizontal direction, is the average position deviation of the top mold and the bottom mold in the vertical direction, is the average rotation angle deviation between the top and bottom molds, It is The position deviation of the matching points in the horizontal direction, It is The position deviation of the matching points in the vertical direction, It is The rotation angle deviation of the matching points;

[0147] We use the Bat Optimization Algorithm (BA) to optimize the hyperparameters of the recurrent neural network (RNN), including the learning rate and the number of hidden layer nodes, to improve the prediction accuracy of the model;

[0148] initialization:

[0149] Bat colony size , maximum number of iterations , frequency range = , initial loudness , initial pulse emission rate , initialize the position of each bat and speed ;

[0150] When the iteration is the 10th time, the current bat position and speed are as follows: The current bat position , the current bat speed , the global optimal position ;

[0151] Frequency, speed and position updates:

[0152] Update frequency:

[0153] ;

[0154] in, It is The frequency of bats, is the minimum value of the frequency range, is the maximum value of the frequency range, is a The random number between is set to 0.3;

[0155] ;

[0156] Update speed and location:

[0157] ;

[0158] ;

[0159] in, It is Bats at time step speed, It is Bats at time step speed, It is Bats at time step location, It is Bats at time step location, is the global optimal position in the current iteration (i.e. the best position among all the bats at present), It is The frequency of bats;

[0160] ;

[0161] ;

[0162] Loudness and pulse rate updates:

[0163] Update the loudness and pulse emission frequency:

[0164] ;

[0165] ;

[0166] in, It is Bats at time step The loudness, is the loudness attenuation factor, It is Bats at time step The loudness, It is Bats at time step The pulse emission rate, It is The pulse firing rate of a bat at the initial time step, is an exponential function, is the pulse emission rate increasing factor, is the current iteration number;

[0167] ;

[0168] ;

[0169] The bat optimization algorithm finds the optimal hyperparameter combination that minimizes the loss function. These optimized hyperparameters will be used to train the RNN model to improve the prediction accuracy of the model.

[0170] Step 3: RNN model training and optimization

[0171] Set acquisition data, image feature data , sensor data , actual position deviation label ;

[0172] RNN model structure:

[0173] Input layer: input image feature data and sensor data, recorded as vector ,in, is the image feature data, For sensor data, set ;

[0174] Initialization parameters: weight matrix , the bias vector ;

[0175] Hidden layer: The hidden layer state of the recurrent neural network is , at time step Updated to:

[0176] ;

[0177] in, is the time step The hidden layer state vector is is the weight matrix input to the hidden layer, is the time step The input vector is is the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state vector is is the bias vector of the hidden layer, Represents the activation function ;

[0178] Output layer: The output layer of the recurrent neural network is the predicted relative position deviation , at time step Calculated as:

[0179] ;

[0180] in, is the time step The predicted position deviation in the horizontal direction, is the time step The predicted position deviation in the vertical direction, is the time step The predicted rotation angle deviation is is the time step The output vector of is the weight matrix from the hidden layer to the output layer, is the time step The hidden layer state vector is is the bias vector of the output layer;

[0181] Loss function:

[0182] The mean square error (MSE) is used as the loss function, which is defined as:

[0183] ;

[0184] Among them, set the sample size is 100, It is The actual position deviation of samples in the horizontal direction, It is The reagent position deviation of each sample in the vertical direction, It is The reagent rotation angle deviation of each sample, It is The predicted position deviation of samples in the horizontal direction, It is The predicted position deviation of samples in the vertical direction, It is The predicted rotation angle deviation of samples;

[0185] Backward Propagation:

[0186] Calculate the gradient of the loss function with respect to weights and biases and update the parameters. The backpropagation algorithm includes the following steps:

[0187] Output layer gradient:

[0188] ;

[0189] ;

[0190] in, is the loss function, is the weight matrix from the hidden layer to the output layer, is the sample size, It is The predicted output vector of samples is It is The actual output vector of samples, It is The transpose of the hidden layer state vector of samples, is the bias vector of the output layer;

[0191] Hidden layer gradient:

[0192] ;

[0193] in, is the time step The hidden layer state error vector is is the transpose of the weight matrix from the hidden layer to the output layer, is the time step The predicted output vector is is the time step The actual output vector, is the transpose of the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state error vector is is the hidden layer state vector, represents element-wise multiplication (Hadamard product), Represents the derivative of the activation function, setting the hidden layer dimension to 64;

[0194] Update weights and biases:

[0195] ;

[0196] ;

[0197] ;

[0198] ;

[0199] ;

[0200] Among them, setting is the learning rate of 0.001, is the loss function, is the weight matrix input to the hidden layer, is the weight matrix from hidden layer to hidden layer, is the weight matrix from the hidden layer to the output layer, is the bias vector of the hidden layer, is the bias vector of the output layer;

[0201] Step 4: Real-time correction

[0202] According to the relative position deviation predicted by the RNN model, the predicted relative position deviation Input to the control system, the position of the top mold and the bottom mold is automatically adjusted through the PID control system. The PID controller is used to generate control signals to minimize position deviation;

[0203] Control signal formula:

[0204] ;

[0205] in, Divided into 0.1, 0.01, 0.001, among which, is the output of the controller, is the current time step error, is the error;

[0206] Actuator adjustment:

[0207] Electric motor: used to accurately adjust the position of the top mold and the bottom mold;

[0208] Hydraulic cylinder: used to provide greater adjustment force;

[0209] Real-time feedback and error correction:

[0210] The alignment accuracy is continuously adjusted and optimized through the feedback mechanism. The position deviation is monitored in real time through sensors and cameras. Correction is made immediately according to the new position deviation to ensure that the position error is within an acceptable range.

[0211] Numerical case analysis:

[0212] Use the trained RNN model to predict the relative position deviation: get the predicted output (Unit: mm and degrees), set the target position to :

[0213] ;

[0214] Actual position deviation calculate:

[0215] ;

[0216] ;

[0217] ;

[0218] for deviation:

[0219] set up Divided into 0.1, 0.01, 0.001, current deviation , the previous integral error is , the previous deviation is , then the rate of change is , ;

[0220] for deviation:

[0221] set up Divided into 0.1, 0.01, 0.001, current deviation , the previous integral error is , the previous deviation is , then the rate of change is , ;

[0222] for deviation:

[0223] set up Divided into 0.1, 0.01, 0.001, current deviation , the previous integral error is , the previous deviation is , then the rate of change is , ;

[0224] Actuator adjustment:

[0225] According to the new position deviation, correction is made immediately. Deviation adjustment, the motor needs to move ,for To adjust the deviation, the hydraulic cylinder needs to rotate .

[0226] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for manufacturing a pickup truck rear bumper frame assembly, the rear bumper frame assembly comprising a bumper body (1), characterized in that: Both ends of the bumper body (1) are fixedly mounted with extension plates (9); the bottom end of the bumper body (1) is fixedly mounted with an anti-collision mechanism (8); the anti-collision mechanism (8) comprises a limit block (85) and an anti-collision shell (81); first shock absorbing springs (84) are fixedly mounted on both sides of the bottom end of the limit block (85); the bottom ends of the two first shock absorbing springs (84) are respectively fixedly connected to the two sides of the inner wall of the anti-collision shell (81); an angle seat (83) is fixedly mounted in the middle of the bottom end of the inner wall of the anti-collision shell (81); a first shock absorber (86) is mounted inside the angle seat (83); One end of the first shock absorber (86) away from the angle seat (83) is connected to a side directly opposite to the limit block (85); a rubber plate (82) is laid on the outer side of the anti-collision shell (81); a reinforcement groove (6) is opened in the middle of the top of the bumper body (1); a plurality of reinforcement members (7) are fixedly installed inside the reinforcement groove (6); the plurality of reinforcement members (7) each include a fixing block (71) and a first stiffening rib (72); one end of the fixing block (71) is fixedly connected to one end of the first stiffening rib (72); and a second stiffening rib (73) is fixedly installed on the other end of the first stiffening rib (72); The manufacturing method comprises the following steps: Step 1: Injecting raw materials: injecting raw materials for preparing the pickup truck rear bumper frame assembly between the top mold (18) and the bottom mold (20); Step 2: snap-fitting and extruding: the lifting cylinder (14) pushes the lifting platform (15) from the top, and the lifting platform (15) drives the top mold (18) to enter the bottom mold (20) to snap-fit ​​the raw material of the pickup truck rear bumper frame assembly; Step 3: Natural cooling: The formed pickup truck rear bumper frame assembly between the top mold (18) and the bottom mold (20) is naturally cooled; Step 4: demoulding the product: the first lifting mechanism (17) and the second lifting mechanism (19) cooperate with each other to demould the product; In step 2, the alignment of the top mold and the bottom mold is automatically corrected using intelligent control and image recognition technology, which specifically includes the following steps: Step 1: System hardware deployment High-resolution camera: Install cameras at fixed positions on the top mold and bottom mold respectively. The installation position should ensure that the camera can clearly capture the mold image during the entire alignment process. Adjust the angle and height of the camera to ensure that the camera can capture the key feature areas of the top mold and bottom mold; Displacement sensor: used to accurately measure the relative displacement of the top mold and the bottom mold, installed at the relative position of the top mold and the bottom mold; Acceleration sensor: used to monitor the acceleration of the top mold and the bottom mold during movement, to help analyze dynamic changes, and installed on the moving path of the top mold and the bottom mold; Temperature sensor: used to monitor the ambient temperature to ensure that temperature changes do not affect the alignment accuracy, installed in the surrounding environment of the mold; Pressure sensor: used to monitor the pressure between the top mold and the bottom mold to ensure that the mold or material will not be damaged during the fastening process. It is installed on the contact surface of the top mold and the bottom mold. Step 2: Data acquisition and processing Image data acquisition: Start the camera to collect image data of the top mold and bottom mold in real time. The image data is transmitted to the image processing unit, and grayscale processing, denoising and edge detection are performed through the OpenCV library: Image acquisition module: obtain image data through the GigE interface; Preprocessing module: grayscale processing, denoising and edge detection; Image data processing: The image data is fused with the sensor data and input into the image processing unit. The key feature points in the image are extracted using the SIFT algorithm. The feature points are matched using the FLANN matcher to calculate the position deviation. , combined with displacement, acceleration, temperature and pressure data, and input into the RNN model for prediction: Feature extraction module: Feature extraction is used to extract key feature points and descriptors from images. These feature points and descriptors can be used for image matching and recognition. The SIFT algorithm is used. The SIFT algorithm detects feature points at different scales by calculating the Gaussian pyramid of the image. Feature matching module: Feature matching is used to pair feature points in two images to determine the correspondence between them. The FLANN matcher is used. The FLANN matcher finds the nearest neighbors between descriptors through fast approximate nearest neighbor search. Data fusion: image data is fused with sensor data as input data for subsequent processing; Position deviation calculation module: calculates the relative position deviation of the top mold and the bottom mold based on the image feature points and sensor data; in, represents the matching point, is the total number of matching points, is the average position deviation of the top mold and the bottom mold in the horizontal direction, is the average position deviation of the top mold and the bottom mold in the vertical direction, is the average rotation angle deviation between the top and bottom molds, It is The position deviation of the matching points in the horizontal direction, It is The position deviation of the matching points in the vertical direction, It is The rotation angle deviation of the matching points; Use the bat optimization algorithm BA to optimize the hyperparameters of the recurrent neural network RNN, including the learning rate and the number of hidden layer nodes, to improve the prediction accuracy of the model; initialization: Bat colony size , maximum number of iterations , frequency range , initial loudness , initial pulse emission rate , initialize the position of each bat and speed ; Frequency, speed and position updates: Update frequency: in, It is The frequency of bats, is the minimum value of the frequency range, is the maximum value of the frequency range, is a A random number between Update speed and location: in, It is Bats at time step speed, It is Bats at time step speed, It is Bats at time step +1 position, It is Bats at time step location, is the global optimal position in the current iteration, that is, the best position among all the bats at present, It is The frequency of bats; Loudness and pulse rate updates: Update the loudness and pulse emission frequency: in, It is Bats at time step The loudness, is the loudness attenuation factor, It is Bats at time step The loudness, Yes, yes Bats at time step The pulse emission rate, Yes, yes The pulse firing rate of a bat at the initial time step, is an exponential function, is the pulse emission rate increasing factor, is the current iteration number; The bat optimization algorithm finds the optimal hyperparameter combination that minimizes the loss function. These optimized hyperparameters will be used to train the RNN model to improve the prediction accuracy of the model. Step 3: RNN model training and optimization Set acquisition data, image feature data , sensor data , actual position deviation label ; RNN model structure: Input layer: input image feature data and sensor data, recorded as vector ,in, is the image feature data, is sensor data; Initialization parameters: weight matrix , the bias vector ; Hidden layer: The hidden layer state of the recurrent neural network is , at time step Updated to: in, is the time step The hidden layer state vector is is the weight matrix input to the hidden layer, is the time step The input vector is is the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state vector is is the bias vector of the hidden layer, Represents the activation function ; Output layer: The output layer of the recurrent neural network is the predicted relative position deviation , at time step Calculated as: in, is the time step The predicted position deviation in the horizontal direction, is the time step The predicted position deviation in the vertical direction, is the time step The predicted rotation angle deviation is is the time step The output vector of is the weight matrix from the hidden layer to the output layer, is the time step The hidden layer state vector is is the bias vector of the output layer; Loss function: The mean square error MSE is used as the loss function, which is defined as: in, is the sample size, It is The actual position deviation of samples in the horizontal direction, It is The reagent position deviation of each sample in the vertical direction, It is The reagent rotation angle deviation of each sample, It is The predicted position deviation of samples in the horizontal direction, It is The predicted position deviation of samples in the vertical direction, It is The predicted rotation angle deviation of samples; Backward Propagation: Calculate the gradient of the loss function with respect to weights and biases and update the parameters. The backpropagation algorithm includes the following steps: Output layer gradient: in, is the loss function, is the weight matrix from the hidden layer to the output layer, is the sample size, It is The predicted output vector of samples, It is The actual output vector of samples, It is The transpose of the hidden layer state vector of samples, is the bias vector of the output layer; Hidden layer gradient: in, is the time step The hidden layer state error vector is is the transpose of the weight matrix from the hidden layer to the output layer, is the time step The predicted output vector is is the time step The actual output vector, is the transpose of the weight matrix from hidden layer to hidden layer, is the time step The hidden layer state error vector is is the hidden layer state vector, represents element-wise multiplication, represents the derivative of the activation function, Update weights and biases: in, is the learning rate, is the loss function, is the weight matrix input to the hidden layer, is the weight matrix from hidden layer to hidden layer, is the weight matrix from the hidden layer to the output layer, is the bias vector of the hidden layer, is the bias vector of the output layer; Step 4: Real-time correction According to the relative position deviation predicted by the RNN model, the predicted relative position deviation Input to the control system, the position of the top mold and the bottom mold is automatically adjusted through the PID control system. The PID controller is used to generate control signals to minimize position deviation; Control signal formula: in, Respectively represent proportional gain, integral gain, and differential gain, among which, is the output of the controller, is the current time step error, is the error; Actuator adjustment: Electric motor: used to accurately adjust the position of the top mold and the bottom mold; Hydraulic cylinder: used to provide greater adjustment force; Real-time feedback and error correction: The alignment accuracy is continuously adjusted and optimized through the feedback mechanism, and the position deviation is monitored in real time through sensors and cameras. Correction is made immediately based on the new position deviation to ensure that the position error is within an acceptable range.

2. The method according to claim 1, characterized in that: Assembly blocks (2) are fixedly mounted on both sides of the top of the bumper body (1), mounting plates (3) are fixedly mounted on the tops of the two assembly blocks (2), first mounting pieces (4) are fixedly mounted on both sides of the tops of the two mounting plates (3), and second mounting pieces (5) are fixedly mounted on both ends of one side of the bumper body (1).

3. The method according to claim 1, characterized in that: The side of the limit block (85) away from the first shock absorbing spring (84) is fixedly connected to the side of the bumper body (1) facing the bumper body, and the end of the fixing block (71) away from the first stiffening rib (72) and the end of the second stiffening rib (73) away from the first stiffening rib (72) are both fixedly connected to the side facing the reinforcement groove (6).

4. A manufacturing device for a pickup truck rear bumper frame assembly according to any one of claims 1 to 3, comprising a fixing base (10), characterized in that: A top plate (13) is provided at the top of the fixed base (10), a lifting cylinder (14) is fixedly installed in the middle of the top of the top plate (13), a lifting platform (15) is fixedly installed at the movable end of the lifting cylinder (14), both sides of the top of the lifting platform (15) are slidably connected with a first lifting mechanism (17), a top mold (18) is fixedly installed in the middle of the bottom of the lifting platform (15), a second lifting mechanism (19) is fixedly installed in the middle of the top of the fixed base (10), the second lifting mechanism (19) comprises a connecting platform (1901) and a lifting platform (1905), both sides of the top of the connecting platform (1901) are fixedly installed with second shock absorbers (1904), the tops of the two second shock absorbers (1904) are respectively fixedly connected to the two sides of the bottom of the lifting platform (1905), and the top of the lifting platform (1905) is fixedly connected to the bottom of the bottom mold (20).

5. The manufacturing device of the pickup truck rear bumper frame assembly according to claim 4, characterized in that: The two first lifting mechanisms (17) each comprise a lifting plate (171) and a length rod (172); the top end of the lifting plate (171) is fixedly connected to the bottom end of the length rod (172); a baffle (174) is fixedly installed on the top end of the length rod (172); a connecting spring (173) is fixedly installed on the top end of the lifting plate (171); the two length rods (172) are slidably connected to the lifting platform (15); and the ends of the two connecting springs (173) away from the lifting plate (171) are fixedly connected to a side directly opposite to the lifting platform (15).

6. The manufacturing device of a pickup truck rear bumper frame assembly according to claim 4, characterized in that: Vertical plates (11) are fixedly mounted on both sides of the top of the fixed base (10); the tops of the two vertical plates (11) are respectively fixedly connected to the two sides of the bottom of the top plate (13); height grooves (12) are provided on opposite sides of the two vertical plates (11); height blocks (16) are slidably connected inside the two height grooves (12); and opposite sides of the two height blocks (16) are respectively fixedly connected to the two ends of the lifting platform (15).

7. The manufacturing device of a pickup truck rear bumper frame assembly according to claim 4, characterized in that: A first assembly shell (1906) is fixedly installed in the middle of the bottom end of the lifting platform (1905), and the bottom end of the first assembly shell (1906) is slidably connected to a second assembly shell (1911), and the bottom end of the second assembly shell (1911) is fixedly connected to the top of the connecting platform (1901). The connecting platform (1901) is provided with two movable grooves (1902) respectively located on one side of the second shock absorber (1904), and movable rods (1903) are fixedly installed inside the two movable grooves (1902), and movable blocks (1903) are slidably connected in the middle of the two movable rods (1903). 1909), a second shock-absorbing spring (1910) is fixedly installed on one side opposite to the two movable blocks (1909), and one end of the two second shock-absorbing springs (1910) away from the movable block (1909) is fixedly connected to the side opposite to the movable groove (1902), and a positioning seat (1907) is fixedly installed in the middle of the first assembly shell (1906), and both sides of the positioning seat (1907) are rotatably connected to synchronization rods (1908), and one end of the two synchronization rods (1908) away from the positioning seat (1907) is respectively connected to the side opposite to the two movable blocks (1909).

8. The manufacturing device of a pickup truck rear bumper frame assembly according to claim 4, characterized in that: The bottom end of the connecting platform (1901) is fixedly connected to the fixed base (10).

Images