Robotic Coating Program Generation for Collision-Free Thickness Control
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Solution Overview
Problem
Programming robots for coating operations in industrial processes is an exhaustive, iterative, and error-prone task, requiring improved techniques to achieve accurate and efficient coating thickness coverage.
Innovation Solution
A data processing system that automatically generates a collision-free robotic program for coating by using a coating dispersion object with machine learning techniques to simulate and calculate the coating thickness, ensuring desired coverage and reducing complexity and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative programming methods are used for robot coating operations, then the robot can achieve coating coverage, but the programming process becomes exhaustive, error-prone, and time-consuming
Solution Approach 1:
The system performs preliminary simulation and calculation of coating thickness before actual robot execution. By using a coating dispersion object model and ray-tracing algorithms, the system predicts coating outcomes in advance, allowing programmers to verify coating coverage accuracy before deploying the robot, thus eliminating exhaustive iterative programming and reducing programming time significantly
2Manufacturing precision
If traditional programming approaches are used, then robot coating operations can be performed, but the process becomes complex and costly
Solution Approach 1:
The system creates a virtual copy of the coating process through simulation. A coating dispersion object is modeled in virtual space with ray-tracing capabilities that replicate the physical coating process. This virtual model allows complex coating thickness calculations and uniformity verification to be performed computationally rather than through complex physical programming, significantly reducing programming complexity while maintaining manufacturing precision
Solution Approach 2:
The system replaces mechanical trial-and-error programming with computational algorithms. Instead of physically programming the robot through iterative testing, the system uses ray-tracing algorithms and coating dispersion models to calculate optimal paths and parameters computationally, substituting mechanical complexity with mathematical precision
Data Source
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AI summary
Systems and a method for predicting generating a robotic program for industrial coating. Inputs are received including a virtual representation of a robot (202), of a coating gun (203), of elements of the object surface (204) to be coated and a set of desired coating thickness ranges. Inputs on a coating dispersion object are received. Training data of a plurality of robotic programs for industrial coating and of their corresponding coating thickness coverage on a plurality of surfaces are received. The training data are processed in x, y tuples so as to learn a mapping function to generate a coating prediction module. Starting with a given selected valid thickness coverage as input parameters, it is proceeded in an iterative manner to: predict a robotic program via the coating prediction module; simulate the predicted robotic program with a collision detection engine within the industrial sub-cell; calculate the thickness values of the coating material on the given surface element by detected collisions between elements of the coating dispersion object mounted on the used robotic coating gun and sub-elements of the given surface element; where the input parameters are iteratively tuned until the calculated thickness values correspond to the set of desired value range and there are no unallowed robotic collisions within the industrial sub-cell. A coating robotic program is generated for each surface element based on the resulting predicted coating programs.