Laser Machining Teaching Path Optimization for Welding Points
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Solution Overview
Problem
Conventional laser machining systems lack an efficient method to automatically determine the optimal motion path and timing for welding points, leading to increased machining time and variability in weld quality.
Innovation Solution
A teaching device that groups machining points into groups to minimize non-machining time and determines a machining path for a robot to move a machining head at a constant speed, optimizing the order of machining points to reduce total movement time and ensure the shortest distances to each point within the scanning range of the machining head.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If an operator determines motion path and welding timing instinctively, then the system is easy to operate, but the machining time increases and weld quality becomes variable
Solution Approach 1:
The system performs automatic path optimization and machining time calculation without requiring operator intervention. The computer automatically determines the optimal motion path, grouping machining points and calculating timings based on pre-set conditions, enabling the system to serve itself rather than relying on operator instinct and experience
Solution Approach 2:
The system changes the parameter of motion path determination from instinctive operator judgment to calculated optimization. By automatically computing the optimal path that minimizes machining time while satisfying scanning range constraints, the system transforms the operation from manual to automated parameter optimization
2Ease of operation
If an operator determines motion path and welding timing instinctively, then the system is easy to operate, but weld quality becomes variable
Solution Approach 1:
The system automatically determines optimal machining parameters and timing without operator intervention, ensuring consistent weld quality through calculated optimization rather than variable human judgment. The computer systematically processes machining points to generate reliable, repeatable results
Solution Approach 2:
The system uses feedback from machining point positions and scanning range constraints to automatically adjust and optimize the motion path. By continuously referencing the predetermined scanning range and calculating optimal paths based on actual machining point distributions, the system ensures consistent weld quality across different operations
3Device complexity
If the robot moves the machining head to machine each point sequentially without optimization, then the path determination is simple, but the total movement time increases
Solution Approach 1:
The system segments the machining points into groups that can be processed efficiently together. By dividing the total set of machining points into multiple groups and determining optimal paths for each group, the system reduces total movement time while keeping the complexity of path determination for each group manageable
Solution Approach 2:
The system transitions from simple sequential point-by-point path determination to multi-dimensional optimization that considers grouping, inter-group transitions, and intra-group paths simultaneously. This dimensional expansion enables comprehensive time optimization while managing complexity through structured segmentation
4Stability of the object's composition
If the machining head scans at constant speed, then the machining process is stable, but the in-group non-machining time increases
Solution Approach 1:
The system performs preliminary grouping of machining points before execution, organizing them into groups that minimize non-machining time while maintaining constant scanning speed. By pre-calculating optimal groupings and paths, the system prepares the most efficient sequence in advance, reducing idle time during actual machining operations
Solution Approach 2:
The system optimizes the parameter of machining point grouping to minimize non-machining time while maintaining constant scanning speed. By changing the grouping parameters and path sequences based on machining point distributions, the system reduces idle time without compromising machining stability
Data Source
AI summary
Provided is a teaching device including a grouping unit which divides machining points into machining point groups so that a machining head can sequentially machine each machining point for a machining time and so that a non-machining time can be minimized, a machining path determination unit which determines a machining path on which an in-group movement time of a robot is shortest for each machining point group, a teaching process adjustment unit which adjusts a machining order of the machining points and an operation order of the machining point groups so as to minimize a distance between groups and which optimizes the grouping so as to minimize a total movement time for completing machining, and a teaching data output unit which outputs, as teaching data, machining execution positions on the machining path obtained as a result of processing of the teaching process adjustment.