Chip mounter feeder system with waste storage and high-foot material beating functions

Abstract

The invention relates to the technical field of chip mounter feeders, and discloses a chip mounter feeder system with waste storage and high-foot material beating, which comprises a shell, a control assembly, a first transmission assembly, a second transmission assembly and a waste bin, and is characterized in that the control assembly, the first transmission assembly, the second transmission assembly and the waste bin are mounted in the shell; the control assembly is used for controlling the first transmission assembly and the second transmission rod assembly, the control assembly is used for adjusting the speed, starting and pausing of the equipment, and the waste bin is used for collecting adhesive films on material strips. The device has the advantages of orderly waste collection, accurate and efficient transmission, adaptability to high-foot material mounting, convenience in operation and control, optimized adhesive film waste treatment and the like, can improve the production efficiency and the product quality, and is suitable for surface mounting production operation in the electronic manufacturing industry.

Description

Technical Field

[0001] The invention relates to the technical field of feeders for chip placement machines, in particular to a feeder system for chip placement machines with waste storage and high-leg material functions. Background Art

[0002] The feeder is an important component of the placement machine, also known as the feeder, which is used to feed materials accurately and quickly and deliver electronic components to the placement position.

[0003] Traditional SMT feeders have numerous shortcomings. First, they lack effective collection measures for film scrap from strips, causing it to fly or fall to the ground. This not only pollutes the production environment but also easily mixes with products or equipment components, affecting product quality and increasing the risk of equipment failure. Second, they are poorly adapted to the placement of tall pins, making it difficult to meet the diverse placement requirements of modern electronic components. As electronic devices continue to miniaturize and increase their performance, the height and shape of electronic component pins have become increasingly diverse. Traditional feeders are unable to accurately and reliably handle tall pins, resulting in decreased placement accuracy and increased scrap rates, severely hindering both efficiency and cost control. Therefore, the development of a SMT feeder that can effectively store scrap and accommodate tall pins is urgent.

[0004] For this purpose, we propose a feeder system for placement machines with waste storage and high-foot material handling. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In view of the deficiencies in the prior art, the present invention provides a feeder system for a placement machine with waste storage and high-leg material handling, which solves the problems existing in the background technology.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a feeder for a placement machine with waste storage and high-leg materials, comprising a shell, a control component, a first transmission component, a second transmission component and a waste bin, the control component, the first transmission component, the second transmission component and the waste bin are installed in the shell, the control component is used to control the first transmission component and the second transmission rod component, the control component is used to adjust the speed, start and pause of the equipment, the waste bin is used to collect the film on the material strip, the waste bin is used to prevent the film waste from flying around and falling directly to the ground, the first transmission component is used for the transmission of the material strip, the second transmission component is used to transmit the film waste to the waste bin and assist the first transmission component in transmission, and the shell is provided with a first cavity and a second cavity for storing the first transmission component and the second transmission component.

[0009] Preferably, the shell includes a shell body, a first cavity, a second cavity and a feeding pipe. The shell body is provided with the first cavity, the second cavity, a waste bin and a feeding pipe. The feeding pipe is provided between the first cavity and the waste bin. The feeding pipe is used to place material strips. The second cavity is provided above the waste bin.

[0010] Preferably, the first transmission assembly includes a first motor, a second motor, a first gear body, a second gear body, a third gear and a fourth gear, the first motor is installed inside the first cavity, the motor shaft of the first motor is provided with the first gear body, the motor shaft of the second motor is provided with the second gear body, the first gear body is meshed with the second gear body through the third gear, and the fourth gear is installed at the bottom of the first gear body.

[0011] Preferably, the second transmission member includes a third motor, a fifth gear, a sixth gear and a first gear member. The third motor is installed inside the second cavity, the motor shaft of the third motor is installed with the fifth gear, the fifth gear is engaged with the sixth gear on the housing, and the first gear member is provided on the outside of the third motor.

[0012] Preferably, the control component includes a circuit board and a control device, the circuit board is installed in the first cavity, the circuit board is electrically connected to the first motor, the third motor, the second motor and the control device, and the control device controls the first motor, the second motor and the third motor through the circuit board to adjust the speed, start and pause of the device.

[0013] Preferably, a clamping assembly is also provided on the shell, and the clamping assembly includes a first spring member and a clamping member. The clamping member is installed on the shell body, and the first spring member is provided between the shell body and the clamping member. The clamping assembly is used to tighten the waste film and fully peel off the waste film from the material strip.

[0014] Preferably, a movable component is also provided on the shell, and the movable component includes a second gear part, a second spring and a movable part. The movable part is installed on the shell, and the second gear part is provided on the movable part. The second gear part is engaged with the first gear part. The second spring part is provided between the movable part and the shell. When the second spring part is compressed and deformed, the second gear part is separated from the first gear part for placing waste film. The second spring part is not deformed, and the second gear part is engaged with the first gear part to prevent the waste film from falling out.

[0015] A chip placement machine feeder system with waste storage and high-foot material processing, including an intelligent instruction processing module, a multi-axis collaborative control module, an adaptive adjustment module and a predictive maintenance module;

[0016] Intelligent command processing module: This module implements fusion analysis and risk assessment of multimodal commands, verifies the rationality of commands through knowledge graphs, dynamically allocates operation permissions, and improves the efficiency and safety of human-computer interaction. Multimodal commands include voice, images, and gestures.

[0017] Multi-axis collaborative control module: Based on quantum genetic algorithms, it optimizes the synchronous control parameters of multiple motors, mimics biological neural networks to achieve phase coordination, generates flexible motion trajectories, and ensures submicron positioning accuracy and motion smoothness.

[0018] Adaptive Adjustment Module: Utilizes the principle of quantum entanglement to quickly identify system parameters, adapts to new scenarios through meta-learning algorithms, balances energy, accuracy, and quality constraints, and achieves dynamic anti-interference and energy efficiency optimization.

[0019] Predictive maintenance module: This module uses a spatiotemporal graph neural network to capture the spatiotemporal correlation of equipment status, combines causal reasoning to locate the root cause of faults, and uses digital twins to predict system life to enable proactive maintenance decisions.

[0020] Preferably, the intelligent instruction processing module includes a multimodal instruction fusion algorithm and an instruction risk assessment algorithm, and the multi-axis collaborative control module includes a quantum genetic PID optimization algorithm and a biological heuristic synchronization algorithm;

[0021] Multimodal instruction fusion algorithm: Dynamically weight the confidence of different modal instructions through the attention mechanism. The formula is: in s j =W·tanh(U·I i +b);I fusion is the confidence of the fused instruction, I i is the instruction confidence of the i-th mode, α i is the adaptive weight coefficient of the i-th mode, s i is the output value of the feature scoring function of the i-th modality, which is used to calculate the weight. W and U are learnable weight matrices, and b is the bias term.

[0022] Instruction risk assessment algorithm: Combines the current system status with historical data, dynamically adjusts weights through reinforcement learning, and assesses instruction execution risk. The formula is: Where R is the instruction execution risk score, w i is the weight of the i-th risk assessment, S current is the system’s current operating status parameter, f i is the risk assessment function of the i-th item;

[0023] Quantum genetic PID optimization algorithm: PID parameters are encoded using quantum bit phases and iteratively optimized through quantum rotating gates. The formula is:

[0024]

[0025] θ i (t+1)=θ i (t)+Δθ i ·sgn(f(θ i (t))-f(θ best ));

[0026] Among them, K P , K i is the proportional coefficient and integral coefficient of the PID controller, α i is the probability coefficient of the quantum bit, θ i is the phase angle of the i-th quantum bit, f(θ i ) is the objective function value, Δθ i is the phase angle update step, θ best is the optimal phase angle in the current iteration.

[0027] Bio-inspired synchronization algorithm: Multi-motor dynamic synchronization is achieved through a coupled phase difference model, the formula is:

[0028] Where T i is the control torque of the i-th motor, k is the coupling strength coefficient, is the current phase of the i-th motor, ψ ij is the inherent phase difference between motors i and j.

[0029] Preferably, the adaptive adjustment module includes a quantum entangled state parameter identification algorithm and a meta-learning adaptive algorithm, and the predictive maintenance module includes a spatiotemporal graph neural network prediction algorithm and a causal effect evaluation algorithm;

[0030] Quantum entangled state parameter identification algorithm: Map system parameters to quantum state probability amplitudes and update parameters in real time through quantum state evolution. The formula is: |ψ(t)>=α(t)|0)+β(t)|1>

[0031] θ1=|α(t)| 2

[0032] θ2=|β(t)| 2

[0033] Where |ψ(t)> is the quantum state wave function, α(t) and β(t) are the probability amplitudes of the quantum state on the basis vectors |0> and |1>, and θ1 and θ2 are the system parameters to be identified;

[0034] Meta-learning adaptive algorithm: Based on model-independent meta-learning (MAML), the control parameters are quickly optimized. The formula is:

[0035] where θ * To adapt to the optimization parameters after the new task, θ is the basic parameter, α is the meta-learning step size, For task T i The loss function on ;

[0036] Empty graph neural network prediction algorithm: Modeling device state association through graph convolutional network, the formula is:

[0037] Among them H (ι) is the ι-layer graph neural network, σ is the activation function, is the adjacency matrix The degree matrix of is an adjacency matrix with self-loops, A is the original adjacency matrix, I is the identity matrix, W (ι) is the learning weight matrix of layer ι;

[0038] Causal effect evaluation algorithm: Calculate the fault causal effect through the backdoor adjustment formula to locate the root cause. The formula is:

[0039] ACE=∑ z P(Y=1|X=1,Z=z)P(Z=z)-∑ z P(Y=1|X=0,Z=z)P(Z=z), where ACE is the average causal effect, Z is the set of confounding variables, ∑ z P(Y|X,Z)P(Z) is the conditional probability of state Y after the intervention variable X.

[0040] In summary, the technical effects and advantages of the present invention are as follows:

[0041] 1. In the present invention, a special waste bin is provided to effectively collect the film on the material strips, prevent the film waste from flying around and falling directly to the ground, keep the production environment clean, reduce the adverse effects of waste on products and equipment, reduce equipment maintenance costs, and improve the overall stability of production.

[0042] 2. In the present invention, the first transmission assembly is responsible for the stable transmission of the material strips, and the second transmission assembly can not only accurately transmit the film waste to the waste bin, but also assist the first transmission assembly in its operation. The two cooperate with each other to ensure smooth material strip transportation and waste processing, thereby improving the operating efficiency and reliability of the placement machine feeder.

[0043] 3. The present invention, through unique structural design and component coordination, enables the feeder of the placement machine to perform stable placement operations on high-leg materials, meet the placement needs of diversified electronic components in modern electronic manufacturing, improve placement accuracy, reduce scrap rate, and improve product quality and production efficiency.

[0044] 4. In the present invention, the control component can conveniently adjust the equipment speed, control the start and pause of the equipment, and can quickly and flexibly adjust parameters according to actual production needs, thereby improving production flexibility and adaptability.

[0045] 5. In the present invention, the clamping component can tighten the film waste, promote the full separation of the film waste and the material strip, and ensure the integrity of the waste collection; the movable component facilitates the placement and fixation of the film waste, prevents it from falling out, and further improves the convenience and stability of waste treatment.

[0046] 6. In the present invention, the control device is composed of an intelligent instruction processing module, a multi-axis collaborative control module, an adaptive adjustment module and a predictive maintenance module. The intelligent instruction processing module supports multimodal interaction, can assess risks, dynamically allocate permissions, and improve efficiency and safety; the multi-axis collaborative control module realizes submicron positioning, generates flexible trajectories, ensures mounting accuracy, and makes transmission more reliable; the adaptive adjustment module can quickly identify parameters, optimize autonomously, balance energy and accuracy, and adapt to different scenarios; the predictive maintenance module can warn of faults in advance, accurately locate the root cause, and combine digital twins to optimize maintenance, reduce downtime losses, and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the overall structure of a feeder for a placement machine with waste storage and high-leg materials according to the present invention;

[0048] Figure 2 This is a schematic diagram of the overall structure of a feeder for a placement machine with waste storage and high-leg materials according to the present invention;

[0049] Figure 3 This is a schematic diagram of the partial structure of a feeder for a placement machine with waste storage and high-leg materials according to the present invention;

[0050] Figure 4 This is a schematic diagram of another partial structural part of a feeder for a placement machine with waste storage and high-leg materials according to the present invention;

[0051] Figure 5 This is a structural schematic diagram from another perspective of a feeder for a placement machine with waste storage and high-leg materials according to the present invention.

[0052] In the figure: 1. shell; 11. shell body; 12. first cavity; 13. second cavity; 14. feeding pipe; 2. control component; 21. circuit board; 22. control device; 3. first transmission component; 31. first motor; 32. second motor; 33. first gear body; 34. second gear body; 35. third gear; 36. fourth gear; 4. second transmission component; 41. third motor; 42. fifth gear; 43. sixth gear; 44. first gear member; 5. waste bin; 6. clamping component; 61. first spring member; 62. clamping member; 7. movable component; 71. second gear member; 72. second spring member; 73. movable member. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 efforts are within the scope of protection of the present invention.

[0054] refer to Figure 1 - Figure 5 The shown embodiment is a feeder for a placement machine with waste storage and high-legged materials, comprising a shell 1, a control component 2, a first transmission component 3, a second transmission component 4 and a waste bin 5. The control component 2, the first transmission component 3, the second transmission component 4 and the waste bin 5 are installed in the shell 1. The control component 2 is used to control the first transmission component 3 and the second transmission rod component. The control component 2 is used to adjust the speed, start and pause of the equipment. The waste bin 5 is used to collect the film on the material strip. The waste bin 5 is used to prevent the film waste from flying around and falling directly to the ground. The first transmission component 3 is used to transmit the material strip. The second transmission component 4 is used to transmit the film waste to the waste bin 5 and assist the first transmission component 3 in transmission. The shell 1 is provided with a first cavity 12 and a second cavity 13 for storing the first transmission component 3 and the second transmission component 4.

[0055] Among them, the shell 1 includes a shell body 11, a first cavity 12, a second cavity 13 and a feeding pipe 14. The shell body 11 is provided with a first cavity 12, a second cavity 13, a waste bin 5 and a feeding pipe 14. A feeding pipe 14 is provided between the first cavity 12 and the waste bin 5. The feeding pipe 14 is used to place material strips. A second cavity 13 is provided above the waste bin 5.

[0056] Among them, the first transmission assembly 3 includes a first motor 31, a second motor 32, a first gear body 33, a second gear body 34, a third gear 35 and a fourth gear 36. The first motor 31 is installed inside the first cavity 12. The motor shaft of the first motor 31 is provided with a first gear body 33, and the motor shaft of the second motor 32 is provided with a second gear body 34. The first gear body 33 is meshed with the second gear body 34 through the third gear 35, and the fourth gear 36 is installed at the bottom of the first gear body 33.

[0057] Among them, the second transmission component includes a third motor 41, a fifth gear 42, a sixth gear 43 and a first gear component 44. The third motor 41 is installed inside the second cavity 13, and the motor shaft of the third motor 41 is installed with a fifth gear 42. The fifth gear 42 is engaged with the sixth gear 43 on the shell 1, and the first gear component 44 is provided on the outside of the third motor 41.

[0058] Among them, the control component 2 includes a circuit board 21 and a control device 22. The circuit board 21 is installed in the first cavity 12. The circuit board 21 is electrically connected to the first motor 31, the third motor 41, the second motor 32 and the control device 22. The control device 22 controls the first motor 31, the second motor 32 and the third motor 41 through the circuit board 21 to adjust the speed, start and pause of the equipment.

[0059] Among them, a clamping assembly 6 is also provided on the shell 1, and the clamping assembly 6 includes a first spring member 61 and a clamping member 62. The clamping member 62 is installed on the shell body 11. A first spring member 61 is provided between the shell body 11 and the clamping member 62. The clamping assembly 6 is used to tighten the waste film and fully peel off the waste film from the material strip.

[0060] Among them, a movable component 7 is also provided on the shell 1, and the movable component 7 includes a second gear component 71, a second spring and a movable component 73. The movable component 73 is installed on the shell 1, and the second gear component 71 is provided on the movable component 73. The second gear component 71 is engaged with the first gear component 44. A second spring component 72 is provided between the movable component 73 and the shell 1. When the second spring component 72 is compressed and deformed, the second gear component 71 is separated from the first gear component 44 for placing the waste film. The second spring component 72 is not deformed, and the second gear component 71 is engaged with the first gear component 44 to prevent the waste film from falling out.

[0061] Working principle: Preliminary preparation and equipment inspection: Turn on the power supply of the placement machine feeder, and the control device 22 in the control component 2 initializes the first motor 31, the second motor 32 and the third motor 41 for self-inspection to ensure that the motors can operate normally, check the feeding pipe 14 and the waste bin 5, remove any foreign matter that may exist, and ensure that the material and waste transmission paths are unobstructed.

[0062] Preparation of material strips and adhesive film: Take the material strip containing the material to be mounted, carefully tear off the adhesive film covering the material, and align one end of the torn adhesive film with the gap between the first gear member 44 and the second gear member 71. At this time, press the movable member 73 of the movable assembly 7 to compress and deform the second spring member 72. The second gear member 71 is separated from the first gear member 44 by a certain distance, and the adhesive film is smoothly placed between the two. Release the movable member 73, the second spring member 72 returns to its original state, and the second gear member 71 is tightly meshed with the first gear member 44 again to stabilize the adhesive film.

[0063] Equipment startup and material strip transmission: Set the equipment operating parameters on the control device 22, such as equipment speed, placement mode, etc., and then issue a startup command. The control device 22 sends a drive signal to the first motor 31 and the second motor 32 through the circuit board 21. The first motor 31 drives the first gear body 33 on the motor shaft to rotate. The first gear body 33 engages with the second gear body 34 through the third gear 35, thereby driving the second gear body 34 to rotate. The fourth gear 36 installed at the bottom of the first gear body 33 rotates accordingly, pushing the material strip to move forward at a constant speed in the feeding pipe 14.

[0064] High-leg material placement: As the material strips continue to move, when the high-leg material reaches the placement station, the placement head of the placement machine accurately grabs the material and places it at the designated position of the target circuit board 21 according to the preset program. During the entire placement process, the first transmission component 3 continuously and stably transports the material strips to ensure the continuity of material supply, ensure the placement accuracy of the high-leg material, and meet the production process requirements.

[0065] Transmission and collection of waste film: During the material strip conveying process, the waste film is continuously transmitted toward the waste bin 5 driven by the first gear member 44 and the second gear member 71. The third motor 41 drives the fifth gear 42 to rotate, and the fifth gear 42 engages with the sixth gear 43, further providing power for the transmission of the waste film, so that the waste film can smoothly enter the waste bin 5. The waste bin 5 collects and stores the waste film to prevent the waste from being scattered in the working area, keep the production environment clean, and avoid waste from contaminating or damaging production equipment and products.

[0066] Equipment pause and stop operation: During the production process, if it is necessary to pause the operation of the equipment, the operation can be performed on the control device 22. The control device 22 sends a stop signal to each motor through the circuit board 21, the motor stops running, and the equipment pauses. After all the placement tasks are completed, a stop command is issued and the equipment stops running. Before turning off the power, clean the waste in the waste bin 5 for next use.

[0067] A chip placement machine feeder system with waste storage and high-foot material processing, including an intelligent instruction processing module, a multi-axis collaborative control module, an adaptive adjustment module and a predictive maintenance module;

[0068] Intelligent command processing module: This module implements fusion analysis and risk assessment of multimodal commands, verifies the rationality of commands through knowledge graphs, dynamically allocates operation permissions, and improves the efficiency and safety of human-computer interaction. Multimodal commands include voice, images, and gestures.

[0069] Multi-axis collaborative control module: Based on quantum genetic algorithms, it optimizes the synchronous control parameters of multiple motors, mimics biological neural networks to achieve phase coordination, generates flexible motion trajectories, and ensures submicron positioning accuracy and motion smoothness.

[0070] Adaptive Adjustment Module: Utilizes the principle of quantum entanglement to quickly identify system parameters, adapts to new scenarios through meta-learning algorithms, balances energy, accuracy, and quality constraints, and achieves dynamic anti-interference and energy efficiency optimization.

[0071] Predictive maintenance module: This module uses a spatiotemporal graph neural network to capture the spatiotemporal correlation of equipment status, combines causal reasoning to locate the root cause of faults, and uses digital twins to predict system life to enable proactive maintenance decisions.

[0072] Among them, the intelligent instruction processing module includes a multi-modal instruction fusion algorithm and an instruction risk assessment algorithm, and the multi-axis collaborative control module includes a quantum genetic PID optimization algorithm and a biological heuristic synchronization algorithm;

[0073] Multimodal instruction fusion algorithm: Dynamically weight the confidence of different modal instructions through the attention mechanism. The formula is: in s j =W·tanh(U·I i +b);I fusion is the confidence of the fused instruction, I i is the instruction confidence of the i-th mode, α i is the adaptive weight coefficient of the i-th mode, s i is the output value of the feature scoring function of the i-th modality, which is used to calculate the weight. W and U are learnable weight matrices, and b is the bias term.

[0074] Instruction risk assessment algorithm: Combines the current system status with historical data, dynamically adjusts weights through reinforcement learning, and assesses instruction execution risk. The formula is: Where R is the instruction execution risk score, w i is the weight of the i-th risk assessment, S current is the system’s current operating status parameter, f i is the risk assessment function of the i-th item;

[0075] Quantum genetic PID optimization algorithm: PID parameters are encoded using quantum bit phases and iteratively optimized through quantum rotating gates. The formula is:

[0076]

[0077] θ i (t+1)=θ i (t)+Δθ i ·sgn(f(θ i (t))-f(θ best ));

[0078] Among them, K P , K i is the proportional coefficient and integral coefficient of the PID controller, α i is the probability coefficient of the quantum bit, θ i is the phase angle of the i-th quantum bit, f(θ i ) is the objective function value, Δθ i is the phase angle update step, θ best is the optimal phase angle in the current iteration.

[0079] Bio-inspired synchronization algorithm: Multi-motor dynamic synchronization is achieved through a coupled phase difference model, the formula is:

[0080] Where T i is the control torque of the i-th motor, k is the coupling strength coefficient, is the current phase of the i-th motor, ψ ij is the inherent phase difference between motors i and j.

[0081] The adaptive adjustment module includes a quantum entangled state parameter identification algorithm and a meta-learning adaptive algorithm, and the predictive maintenance module includes a spatiotemporal graph neural network prediction algorithm and a causal effect evaluation algorithm.

[0082] Quantum entangled state parameter identification algorithm: Map system parameters to quantum state probability amplitudes and update parameters in real time through quantum state evolution. The formula is: |ψ(t)>=α(t)|0)+β(t)|1>

[0083] θ1=|α(t)| 2

[0084] θ2=|β(t)| 2

[0085] Where |ψ(t)> is the quantum state wave function, α(t) and β(t) are the probability amplitudes of the quantum state on the basis vectors |0> and |1>, and θ1 and θ2 are the system parameters to be identified;

[0086] Meta-learning adaptive algorithm: Based on model-independent meta-learning (MAML), the control parameters are quickly optimized. The formula is:

[0087] where θ * To adapt to the optimization parameters after the new task, θ is the basic parameter, α is the meta-learning step size, For task T i The loss function on ;

[0088] Empty graph neural network prediction algorithm: Modeling device state association through graph convolutional network, the formula is:

[0089] Among them H (ι) is the ι-layer graph neural network, σ is the activation function, is the adjacency matrix The degree matrix of is an adjacency matrix with self-loops, A is the original adjacency matrix, I is the identity matrix, W (ι) is the learning weight matrix of layer ι;

[0090] Causal effect evaluation algorithm: Calculate the fault causal effect through the backdoor adjustment formula to locate the root cause. The formula is:

[0091] ACE=Σ z P(Y=1|X=1,Z=z)P(Z=z)-Σ z P(Y=1|X=0,Z=z)P(Z=z), where ACE is the average causal effect, Z is the set of confounding variables, ∑ z P(Y|X,Z)P(Z) is the conditional probability of state Y after the intervention variable X.

[0092] The electrical components mentioned in this article are all connected to an external main controller and 220V mains electricity, and the main controller can be a conventional known device that performs control such as a computer.

[0093] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or 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 scope of protection of the present invention.

Claims

1. A feeder for a placement machine with waste storage and high-leg material, characterized by: The invention comprises a housing (1), a control component (2), a first transmission component (3), a second transmission component (4) and a waste bin (5), wherein the control component (2), the first transmission component (3), the second transmission component (4) and the waste bin (5) are installed in the housing (1), the control component (2) is used to control the first transmission component (3) and the second transmission rod component, the control component (2) is used to adjust the speed, start and pause of the device, the waste bin (5) is used to collect the film on the material strip, and the waste bin (5) is used to prevent the waste film from flying around and falling directly to the ground, the first transmission component (3) is used to transmit the material strip, the second transmission component (4) is used to transmit the waste film into the waste bin (5) and assist the first transmission component (3) in transmission, and the housing (1) is provided with a first cavity (12) and a second cavity (13) for storing the first transmission component (3) and the second transmission component (4).

2. A feeder for a chip mounter with waste storage and high-leg material according to claim 1, characterized in that: The shell (1) comprises a shell body (11), a first cavity (12), a second cavity (13) and a feeding pipe (14); the shell body (11) is provided with the first cavity (12), the second cavity (13), a waste bin (5) and the feeding pipe (14); the feeding pipe (14) is provided between the first cavity (12) and the waste bin (5); the feeding pipe (14) is used for placing material strips; the second cavity (13) is provided above the waste bin (5).

3. The feeder for a chip mounter with waste storage and high-leg material according to claim 1, characterized in that: The first transmission assembly (3) comprises a first motor (31), a second motor (32), a first gear body (33), a second gear body (34), a third gear (35) and a fourth gear (36); the first motor (31) is mounted inside the first cavity (12); the motor shaft of the first motor (31) is provided with the first gear body (33); the motor shaft of the second motor (32) is provided with the second gear body (34); the first gear body (33) is meshed with the second gear body (34) through the third gear (35); and the bottom of the first gear body (33) is provided with the fourth gear (36).

4. A feeder for a chip mounter with waste storage and high-leg material according to claim 3, characterized in that: The second transmission member comprises a third motor (41), a fifth gear (42), a sixth gear (43) and a first gear member (44); the third motor (41) is installed inside the second cavity (13); the motor shaft of the third motor (41) is installed with the fifth gear (42); the fifth gear (42) is meshed with the sixth gear (43) on the housing (1); and the first gear member (44) is arranged on the outside of the third motor (41).

5. A feeder for chip placement machines with waste storage and high-leg materials according to claim 4, characterized in that: The control component (2) comprises a circuit board (21) and a control device (22); the circuit board (21) is installed in the first cavity (12); the circuit board (21) is electrically connected to the first motor (31), the third motor (41), the second motor (32) and the control device (22); the control device (22) controls the first motor (31), the second motor (32) and the third motor (41) through the circuit board (21) to adjust the speed, start and pause of the device.

6. The feeder for a chip mounter with waste storage and high-leg material according to claim 2, characterized in that: The shell (1) is also provided with a clamping assembly (6), the clamping assembly (6) comprises a first spring member (61) and a clamping member (62), the clamping member (62) is mounted on the shell body (11), the first spring member (61) is provided between the shell body (11) and the clamping member (62), and the clamping assembly (6) is used to tighten the waste film and fully peel the waste film from the material strip.

7. The feeder for a chip mounter with waste storage and high-leg material according to claim 4, characterized in that: The housing (1) is also provided with a movable assembly (7), the movable assembly (7) comprising a second gear member (71), a second spring and a movable member (73), the movable member (73) being mounted on the housing (1), the second gear member (71) being provided on the movable member (73), the second gear member (71) being engaged with the first gear member (44), a second spring member (72) being provided between the movable member (73) and the housing (1), when the second spring member (72) is compressed and deformed, the second gear member (71) is separated from the first gear member (44) for placing the waste film, the second spring member (72) is not deformed, and the second gear member (71) is engaged with the first gear member (44) to prevent the waste film from falling out.

8. A feeder system for a chip mounter with waste storage and high-leg material handling, characterized by: It includes intelligent instruction processing module, multi-axis collaborative control module, adaptive adjustment module and predictive maintenance module; Intelligent command processing module: This module implements fusion analysis and risk assessment of multimodal commands, verifies the rationality of commands through knowledge graphs, dynamically allocates operation permissions, and improves the efficiency and safety of human-computer interaction. Multimodal commands include voice, images, and gestures. Multi-axis collaborative control module: Based on quantum genetic algorithms, it optimizes the synchronous control parameters of multiple motors, mimics biological neural networks to achieve phase coordination, generates flexible motion trajectories, and ensures submicron positioning accuracy and motion smoothness. Adaptive Adjustment Module: Utilizes the principle of quantum entanglement to quickly identify system parameters, adapts to new scenarios through meta-learning algorithms, balances energy, accuracy, and quality constraints, and achieves dynamic anti-interference and energy efficiency optimization. Predictive maintenance module: This module uses a spatiotemporal graph neural network to capture the spatiotemporal correlation of equipment status, combines causal reasoning to locate the root cause of faults, and uses digital twins to predict system life to achieve proactive maintenance decisions.

9. A feeder system for chip placement machines with waste storage and high-leg material handling according to claim 8, characterized in that: The intelligent instruction processing module includes a multi-modal instruction fusion algorithm and an instruction risk assessment algorithm, and the multi-axis collaborative control module includes a quantum genetic PID optimization algorithm and a biological heuristic synchronization algorithm; Multimodal instruction fusion algorithm: Dynamically weight the confidence of different modal instructions through the attention mechanism. The formula is: in s j =W·tanh(U·I i +b); I fusion is the confidence of the fused instruction, I i is the instruction confidence of the i-th mode, α i is the adaptive weight coefficient of the i-th mode, s i is the output value of the feature scoring function of the i-th modality, which is used to calculate the weight. W and U are learnable weight matrices, and b is the bias term. Instruction risk assessment algorithm: Combines the current system status with historical data, dynamically adjusts weights through reinforcement learning, and assesses instruction execution risk; Quantum genetic PID optimization algorithm: uses quantum bit phase encoding PID parameters and iteratively optimizes through quantum rotating gates. Bio-inspired synchronization algorithm: achieving multi-motor dynamic synchronization via coupled phase difference model.

10. The feeder system for chip placement machines with waste storage and high-leg material handling according to claim 8, characterized in that: The adaptive adjustment module includes a quantum entangled state parameter identification algorithm and a meta-learning adaptive algorithm, and the predictive maintenance module includes a spatiotemporal graph neural network prediction algorithm and a causal effect evaluation algorithm; Quantum entangled state parameter identification algorithm: maps system parameters to quantum state probability amplitudes and updates parameters in real time through quantum state evolution; Meta-learning adaptive algorithm: Rapidly optimize control parameters based on model-independent meta-learning (MAML); Empty graph neural network prediction algorithm: Modeling device state associations through graph convolutional networks; Causal effect assessment algorithm: Calculates the causal effect of faults through backdoor adjustment formulas to locate the root cause.

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