Collaborative Robot Parameter Control for Variable Workpiece Positions
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Solution Overview
Problem
Existing collaborative robots face challenges in analyzing their working state due to varying workpiece placement positions, leading to difficulties in optimizing production parameters and increasing costs.
Innovation Solution
An industrial Internet of Things system comprising a user platform, service platform, management platform, and sensing network platform, utilizing machine vision and profilometry to determine benchmark and dynamic working parameters for collaborative robots, enabling real-time monitoring and adjustment of machining processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine vision technology is used to locate and identify workpieces without adjusting workpiece position, then production costs are reduced, but working state analysis becomes difficult due to varying placement positions
Solution Approach 1:
The system implements feedback by using machine vision to detect workpiece placement positions and feeding this information back to the control system. The control system then adjusts machining parameters based on the detected position, creating a closed-loop system that maintains working state analysis capability while accommodating variable workpiece positions.
Solution Approach 2:
The system dynamically adjusts machining parameters based on real-time workpiece position detection. Instead of using fixed parameters, the control system modifies machining parameters adaptively according to the detected workpiece placement position, enabling the system to handle variable positions while maintaining optimal machining conditions.
2Productivity
If workpiece position adjustment is eliminated to reduce costs, then production efficiency improves, but machining precision may deteriorate due to varying placement positions
Solution Approach 1:
The system changes machining parameters based on detected workpiece positions. The control system receives position information from the machine vision device and adjusts parameters such as cutting speed, feed rate, and tool path to maintain machining precision across different workpiece placement positions, eliminating the need for manual position adjustment.
Solution Approach 2:
The system applies local quality by tailoring machining parameters to specific workpiece positions. Different regions or positions of the workpiece receive customized machining parameters optimized for their specific characteristics, ensuring high precision machining regardless of where the workpiece is placed on the fixture.
3Device complexity
If benchmark working parameters are used for all workpiece positions, then system complexity is reduced, but machining optimization is limited
Solution Approach 1:
The system segments the workpiece workspace into multiple detection regions and assigns different benchmark working parameters to each region. The machine vision device determines which region the workpiece occupies and selects the corresponding parameter set, providing optimized machining parameters for different positions without requiring a complex adaptive control system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient monitoring of collaborative robots at different workpiece positions, reducing the need for position adjustments and lowering production costs by optimizing machining parameters.
Implementation Method 1
obtain position information and a morphological feature of the precision part through a machine vision recognition device of a target collaborative robot
Implementation Method 2
obtain, by measuring the precision part with a profilometer, a specificity feature of the precision part, wherein the specificity feature includes precision parameter distribution information after a previous process
Data Source
AI summary
The present disclosure provides an industrial Internet of things for determining working parameters of a collaborative robot and a control method, a storage medium thereof, wherein the control method comprises: obtaining position information, a morphological feature, and a specificity feature of the precision part; determining, based on the position information and the morphological feature, a benchmark working parameter for the target collaborative robot to perform the finishing on the precision part; determining a specificity machining point position based on the precision parameter distribution information after the previous process of the precision part and a reference precision of the previous process; determining, based on a precision parameter of the specificity machining point position, a dynamic working parameter through a precision prediction model and sending the benchmark working parameter and the dynamic working parameter to a service platform and to the user platform for display.