23 results about "Robot machining" patented technology

The application discloses a biological implant robot machining posture planning and error compensation method based on a global stiffness model, belongs to the technical field of biological manufacturing and surgical robot auxiliary machining, and comprises the following steps: a biomechanical stiffness atlas covering the whole posture space of the implant is constructed by sparse modal sampling combined with an inverse distance weighting algorithm. The application effectively solves the problem that the cell responses are inconsistent due to the machining quality differences of different regions of the complex curved surface implant through the global surface biological activity consistency, and significantly improves the long-term success rate of implant surgery. The application is easy to deploy on the existing medical robot through hardware pollution. Through micron-level functional molding, the micron-level biological texture depth is accurately controlled, and the manufacturing level of the personalized implant is improved from simple geometric shape customization to a new height of surface biological function customization.

The application discloses a kind of infinite die workpiece robot machining program automatic generation method in the field of intelligent manufacturing, comprising the following steps: step 1, obtain blank point cloud;Step 2, point cloud normal vector and Gaussian curvature calculation;Step 3, all point clouds are classified, generate construction point 1, obtain sub-region boundary line and sub-region clustering center point, then second clustering is carried out, and the boundary line clustering center point is obtained again;Step 4, generate construction point 3, generate surface construction key point using key point generation algorithm;Step 5, establish auxiliary plane point cloud, generate auxiliary plane based on sub-region clustering center point, boundary line clustering center point and surface construction key point;Step 6, generate initial path point, find the intersection of original point cloud and auxiliary plane point cloud, generate initial path point;Step 7, post-processing;Realize the generation of polishing and cleaning program not dependent on numerical model.

The application discloses a kind of sand belt abrasion prediction method and system of fusion acoustic signal and acoustic emission signal.Method includes: installing acoustic signal sensor and acoustic emission sensor and collecting corresponding signal, time domain analysis and frequency domain analysis are carried out to two kinds of signals, and the time domain characteristics and frequency domain characteristics of signal are respectively extracted;Using LSTM neural network fusion the time-frequency domain characteristics of the two kinds of signals, establish the nonlinear model of sand belt abrasion in the process of complex blade robot grinding;Using state estimation technique in probability statistics method, predict the probability of sand belt stable abrasion and severe abrasion occurring at next time;According to the abrasion probability, feedback adjustment and control the process parameters in grinding process, prevent tool excessive abrasion from causing blade to appear burn and other quality decline phenomenon.The abrasion state of sand belt in the process of processing is monitored in real time and accurately, timely feedback regulation, prevent complex blade from being damaged due to severe abrasion of sand belt in the process of robot processing.

The utility model relates to a floor sweeping robot injection mould processing technical field especially, a kind of floor sweeping robot processing injection mould, including lower fixed plate and upper fixed plate, the upper end of lower fixed plate is equipped with two die feet, the upper end of die foot is provided with lower mould, the lower end of upper fixed plate is connected with upper mould, the end corresponding to the upper mould and lower mould is embedded, the middle part of lower mould is provided with die groove, the lower end of lower mould is equidistantly provided with bottom groove, the inner chamber top of bottom groove is provided with top groove, the inner chamber of bottom groove is provided with stripping mechanism, the top of stripping mechanism is penetrated top groove and is inserted with die groove, compared with prior art, the structure design of the utility model is reasonable, and effectively improve the effect of stripping, avoid the traditional needle ejecting product to cause because needle and product's contact area is small and lead to damage to product when ejecting.

This invention belongs to the technical field of robot machining accuracy control. It discloses a robot machining error control method and device for machining large-size components. The steps are as follows: (1) After decomposing the machining system, calculate the pose error of each part to obtain the mapping relationship between force and deformation error, and construct an augmented stiffness model of the machining system based on the mapping relationship; (2) Embed the augmented stiffness model into the Transformer time series prediction module of the multi-head self-attention mechanism to obtain the error look-ahead prediction model; (3) Input the state vector corresponding to the machining system to be controlled into the error look-ahead prediction model to obtain the predicted value of the deformation error of the machining system. Based on the PPO reinforcement learning strategy, the predicted value of the deformation error is allocated to the redundant end effector and the robot body to perform collaborative control of the machining system. This invention improves the control accuracy.

This application belongs to the field of robot machining and error compensation, and more specifically, relates to a method and system for path error compensation in robot milling. This application achieves data augmentation of historical path sequences through K-means clustering, sliding window generation of subsequences, and sequence discreteness filtering operations. This results in a large number of training path sequences that retain the physical laws of path errors, thereby improving the path prediction accuracy of the path error prediction model. A spatial feature extraction layer composed of multiple two-dimensional convolutional networks with dynamically adjustable kernel sizes accurately extracts the path spatial features of the current path sequence. The path error prediction model accurately obtains the initial positioning error of each current path point in the current path sequence, which, combined with an iterative compensation strategy, corrects the pose parameters of each current path point, achieving high-precision error compensation for the robot's current path.

This invention discloses a method for simultaneously predicting the microstructure and processing stability of robot-milled surfaces for bio-implants, belonging to the fields of high-end medical device manufacturing and biomedical engineering technology. It includes establishing a non-homogeneous dynamic model that considers regeneration effects and retains static cutting force terms as non-homogeneous excitations, tailored to the characteristics of robot processing of biomaterials; using a semi-discretization method to discretize the cutting cycle and derive the state-space affine transformation relationship within a cutting cycle; and improving the clinical success rate of implants from the manufacturing stage by accurately selecting process parameters that are both chatter-free and generate optimal osseointegration textures before code execution. Furthermore, by directly solving for steady-state fixed points using analytical matrix operations, it replaces traditional long-cycle time-domain simulation, increasing computation speed by tens of times, making it highly suitable for large-scale, rapid process verification of personalized implants.

This invention discloses a method, device, and medium for compensating for the machining of large thin-walled parts using a robot. The method includes: acquiring industrial robot parameters, large thin-walled part parameters, and machining process parameters; establishing a kinematic model based on the industrial robot parameters, and identifying the robot's joint stiffness matrix based on the kinematic model; establishing an end-effector stiffness performance evaluation index based on the joint stiffness matrix; constructing a deformation prediction model based on the machining process parameters and the large thin-walled part parameters; predicting the robot's tool tip deformation and the large thin-walled part machining deformation based on the end-effector stiffness performance evaluation index and the deformation prediction model, respectively; and adjusting the robot's pre-determined theoretical machining pose according to the robot's tool tip deformation and the large thin-walled part machining deformation to determine the compensated actual machining pose. This method achieves a significant improvement in the dimensional and positional accuracy of large thin-walled part machining.

This application belongs to the field of robot machining technology, specifically disclosing a robot milling error prediction method based on a physical information Gaussian process. The method includes: constructing a prediction model, which includes a pre-constructed robot stiffness error model, an error prediction framework based on a multi-output Gaussian process, and a physical information kernel function. The multi-output Gaussian process-based error prediction framework is used to capture the coupling correlation of the robot end effector's deformation in the x, y, and z directions through a linear common-region model; training the prediction model to obtain a trained prediction model; and inputting the robot's posture and milling force into the trained prediction model to obtain the predicted deformation of the robot end effector output by the prediction model. This method can improve the prediction accuracy of robot stiffness deformation.

The utility model relates to the technical field of wing manufacturing technique provides a kind of composite robot processing equipment, AGV dolly, manipulator being arranged on AGV dolly, main shaft and the abutting component of main shaft connection being arranged at the end of manipulator and processing tool, main shaft is used to drive processing tool rotation, and processing tool can be moved along the axis direction of processing tool relative to abutting component.It can be understood that the continuous abutment of abutting component can provide stable supporting force for processing tool, so as to offset the vibration or deviation force generated in the processing process, i.e. the close contact between abutting component and processing surface forms a temporary fixed fulcrum, which limits the shaking space of processing tool to some extent. Therefore, the processing tool can always run along the preset trajectory, avoiding the deviation of the trajectory caused by external interference, and finally ensuring the stability and processing accuracy of the processing operation. In this way, the punching effect is guaranteed, and the practicability of the equipment is better.

This invention discloses a method and system for processing injection molds based on industrial robots, relating to the field of robot processing technology. This application uses a deep learning model to identify and segment point cloud data of a 3D injection mold. The outline is calculated from the obtained annular structural region, and inflection points are identified and connected to construct a grinding curvature tree. The slicing density is adjusted according to curvature changes, enabling more refined grinding path planning even in complex areas. An annular tangent plane is obtained from the virtual curves in the grinding curvature tree. Discrete points are extracted from the annular tangent plane using a hybrid discrete point extraction method to obtain discrete trajectory points, ensuring the continuity and integrity of the injection mold grinding trajectory points. The discrete trajectory points are then sorted by polar angle to generate an ordered annular discrete trajectory point set. A non-uniform rational B-spline curve is used to fit the ordered annular discrete trajectory point set to generate an annular processing trajectory, planning the injection mold grinding path for the industrial robot.

The application provides a magnetic anti-collision assembly, and relates to the technical field of industrial robot processing. The anti-collision assembly comprises a first part, a second part and a reset part for extruding the first part and the second part when they are close to or in contact with each other; the reset part comprises at least a set of magnetic parts, and the set of magnetic parts comprises first magnetic parts and second magnetic parts arranged on the first part and the second part, respectively; the magnetic poles on the sides of the first magnetic parts and the second magnetic parts close to each other are opposite; wherein the first magnetic parts and the second magnetic parts are profiled magnetic parts comprising a main body and a limiting structure arranged at an angle of 90°±80° with the main body; and the first magnetic parts and the second magnetic parts are fixed in the first part and the second part, respectively, through the limiting structures. The application improves the problems in the prior art that the replacement of magnetic parts is difficult, and the structure or material for fixing the magnetic parts is prone to residual or deformation.

A method for repairing cracks in turbine runner blades includes the following steps: S1, designing a quick-change tooling at the end effector of a robot according to the runner crack repair process; S2, installing a vision measurement device at the end effector of the robot, and using a hand-eye calibration method to unify the vision measurement coordinate system with the robot end effector flange coordinate system; simultaneously, calibrating the robot and various end effector tools; S3, using an ultrasonic phased array to conduct depth testing on the runner crack area to determine the depth of each location of the runner crack, and constructing the runner crack envelope in trajectory planning software; simultaneously, extracting features from a standard sphere to obtain the coordinates of the sphere's center; S4, constructing a workpiece coordinate system on the center of the extracted standard sphere, generating robot machining point data in the trajectory planning software by combining the runner crack envelope model, generating a robot machining trajectory program using robot inverse kinematics and trajectory optimization strategies, and performing trajectory simulation in trajectory processing software to generate the robot machining trajectory program. This invention effectively improves the efficiency, quality, and accuracy of turbine runner crack repair, and reduces the risks of manual repair.

This invention discloses a method for determining machining allowance and automatically guiding grinding of curved workpieces based on point clouds. The method includes: performing hand-eye calibration on the robot's end-effector camera to obtain a first transformation matrix; converting the acquired workpiece point cloud to the robot tool coordinate system to form an actual workpiece point cloud; triangulating and sampling the workpiece's 3D model to generate a dense simulation point cloud; extracting the same region of interest (ROI) point cloud from two point clouds, obtaining the initial pose through principal component analysis, and then obtaining the final transformation matrix through ICP fine registration; performing registration transformation on the simulation point cloud; using the registered simulation point cloud as a template to calculate the normal deviation of each point in the actual point cloud relative to the template, and judging the workpiece status as qualified, with allowance, or overcut and scrapped; if it is determined to have allowance, performing surface normal estimation and principal component analysis on the allowance region to generate a grinding trajectory containing discrete path points and corresponding tool directions to guide robot machining; this invention can achieve closed-loop intelligent control of the machining process.

This invention provides a robotic grinding and polishing method and system for cabin coating surfaces, comprising: Step S1, a robot measurement system performs full-domain measurement of the cabin coating surface, generating an actual coating surface model from point cloud data; Step S2, a robot offline programming system optimizes the wall thickness measurement points and automatically generates a measurement path; Step S3, the robot measurement system measures the wall thickness at feature points to obtain the actual wall thickness, and generates a cabin coating wall thickness distribution model based on mean interpolation; Step S4, the robot offline programming system automatically generates a grinding and polishing path and automatically optimizes the grinding and polishing parameters according to the wall thickness allowance distribution; Step S5, a robot processing system performs adaptive grinding and polishing on the cabin coating. This invention can complete cabin coating grinding and polishing in an automated manner, and has significant advantages such as high processing accuracy, no occupational hazards, high degree of automation, and high production efficiency, making it suitable for efficient processing of cabin coatings.

The utility model relates to the technical field of aircraft wing manufacturing technique provides a kind of processing device, processing robot and composite robot processing equipment, wherein, a kind of processing device, comprising: processing assembly and chip removal assembly;Processing assembly includes processing tool, chip removal assembly is installed on processing assembly, chip removal assembly includes at least one chip removal unit, chip removal unit is movably close or far from processing tool.In this way, by the movable setting of chip removal unit, so that chip removal unit can be closer to processing tool when working, so that chip removal unit compared with prior art can be flexibly moved to the position closer to processing tool, and further make the range of vacuum suction and the core area distance of the processing chip generated closer, thereby effectively enhance the adsorption effect of chip removal assembly to chip.

The machining surface roughness prediction method and system based on deep learning solve the problem of how to improve the machining surface roughness prediction while using sensors as much as possible, and belong to the technical field of robot machining.The present application constructs a mutual cross fusion prediction model based on a deep learning network, uses joint torque inside a robot and external vibration signals to predict machining surface roughness; the prediction model comprises a feature dimension reduction module, a multi-channel feature enhancement module and a mutual fusion module connected in sequence; the feature dimension reduction module reduces the dimension of the input to obtain low-dimensional features of each channel, the multi-channel feature enhancement module realizes dynamic interaction between different channel features for the low-dimensional features of each channel to obtain multi-channel torque enhancement features and multi-channel vibration enhancement features, the mutual fusion module bidirectionally fuses the multi-channel torque enhancement features and the multi-channel vibration enhancement features to obtain fusion features, and the machining surface roughness is predicted according to the fusion features.

This invention relates to a pipe bending method using a mobile pipe bending robot, comprising a coordinate system establishment step, a machining trajectory planning step, and a pipe bending execution step. The coordinate system establishment step includes: S11, establishing a base coordinate system; S12, establishing a robot base coordinate system; S13, establishing a robot machining coordinate system; and S14, establishing a pipe fitting coordinate system. The machining trajectory planning step includes: S21, establishing a 3D model of the pipe fitting; S22, calibrating the robot machining coordinate system based on the 3D model of the pipe fitting; and S23, calculating the pipe fitting machining parameters. The pipe bending execution step involves the machining trajectory Traj having a discontinuity point N for each bend. The robot first moves to the discontinuity point to perform the pipe bending operation, and after the bend is completed, it continues to the next trajectory segment. This invention, based on a mobile pipe bending robot, proposes a pipe bending method that improves bending accuracy, shortens overall processing time, and increases bending efficiency.

The application discloses a welding robot machining device and method suitable for steel support components, and belongs to the technical field of equipment machining, and comprises side-by-side workbench modules, including a frame support frame, end support columns; a spot welding machining module, including a mounting plate, a first machining clamp and a first positioning structure; a full welding machining module, including a mounting plate, a second machining clamp and a second positioning structure; the spot welding machining module and the full welding machining module are fixedly arranged on the frame support frame; and a welding robot is arranged. The spot welding machining module is arranged to fix the structural plate of the steel support component for spot welding, so that a spot-welded steel support component is formed; the full welding machining module is arranged to place the spot-welded steel support component for full welding; and through the side-by-side workbench modules, alternating work is performed to improve welding efficiency and reduce operation cost.

This invention belongs to the field of mechanical manufacturing technology and discloses a method and system for gravity compensation of a six-dimensional force sensor in robot processing. It uses Euler angles and robot inverse kinematics to obtain the sensor's pose during deburring, performs preliminary gravity compensation through static methods, and divides the robot's deburring workspace using the constraint that the robot's flange joint does not reach the extreme value of the joint angle during continuous movement. A Latin hypercube sampling algorithm is used to obtain a dataset of systematic errors in gravity compensation, and a long short-term memory network is used to train a predictive model for systematic errors in gravity compensation. This solves the problems of anisotropy of the six-dimensional force sensor reference value and poor accuracy of traditional gravity compensation methods during robot deburring.

The application provides a robot teaching polishing trajectory compensation method based on three-dimensional vision and digital twinning, relates to the field of robot processing, and comprises the following steps: obtaining the pre-processing point cloud and post-processing point cloud of a teaching part by using three-dimensional vision, combining the pre-processing point cloud and post-processing point cloud, performing robust registration and truncation, and extracting a processing area; combining teaching G code, robot structure parameters and a grinding wheel geometric model, replaying the teaching processing process in a digital twinning environment, and inversely obtaining the real contact point trajectory of the grinding wheel in the teaching processing process; obtaining a new part point cloud, registering the new part point cloud with the pre-processing point cloud of the teaching part to obtain a new part processing area, constructing an error field based on the new part processing area and the real contact point trajectory, segmenting and compressing the error field, and generating trajectory compensation to obtain compensation G code for new part processing. The application can significantly improve the polishing consistency and processing quality of complex curved surface parts.

This invention relates to the field of robot machining performance analysis technology, and particularly to a multi-channel online monitoring device and method for chatter in robot milling. The invention employs successive multiple variational mode decomposition to decompose the acquired three-channel acceleration signal into multiple sets of intrinsic mode functions with mode alignment characteristics, thereby extracting the common frequency components between channels. This method not only adaptively decomposes the signal but also effectively captures the physical correlations between different channels. The invention proposes a dimensionless chatter discrimination index—frequency ratio—for automatically extracting chatter components from the decomposed signal. Subsequently, the energy ratio sequence between the chatter signal and the milling signal is used as input to train a deep convolutional neural network model to achieve chatter detection. This method avoids manually setting chatter index thresholds and improves the generalization ability of chatter detection.

The application discloses a body intelligent robot sensing and control system equipped with a parallel processing device, and relates to the technical field of robot control. The system comprises a multi-modal sensing system, an intelligent control system and a general operation mode for different processing scenes. The multi-modal sensing system integrates binocular vision cameras, temperature and humidity sensors, multi-dimensional force sensors and various sensing devices to realize three-dimensional processing scene modeling, self-state monitoring and force-position hybrid compliant control. The intelligent control system operates based on multi-modal sensing information and has sensing fusion modules, decision planning modules, execution control modules and various functional modules to realize high-intelligent processing of "hand-eye-brain cooperation". Thus, the technical problem of poor device adaptability, low overall processing efficiency, difficulty in balancing large-scale flexible movement and local high-precision processing, and fixed working mode of the robot processing system in the prior art is solved.