Robotic Tooling Selection for Energy Optimization
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Solution Overview
Problem
Current Product Data Management systems fail to efficiently optimize robot tooling in manufacturing processes, leading to high energy consumption and increased production costs due to inefficient tooling selection, which is exacerbated by the complexity of robot powertrains and frequent operation in production lines.
Innovation Solution
A method and system that receives inputs such as robot information, operation information, and position information to generate a list of tooling candidates, determine energy consumption values, and rank them based on energy efficiency, ultimately selecting the optimal tooling for each robot to minimize energy consumption by simulating robot performance and eliminating candidates that cause collisions or cannot reach task locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional tooling selection methods are used in PDM systems, then tooling selection can be completed, but energy consumption is high and production costs increase
Solution Approach 1:
The system changes the selection criteria parameter from traditional factors (speed, precision) to include energy consumption as a key parameter. By simulating and calculating energy consumption for each tooling candidate, the system identifies optimal tooling that minimizes energy usage while maintaining manufacturing requirements, thus resolving the contradiction between energy efficiency and production cost.
Solution Approach 2:
The system implements feedback by simulating robot operations with different tooling candidates and measuring their energy consumption. This feedback loop allows the system to evaluate and compare energy efficiency of various tooling options, selecting the optimal one that reduces both energy consumption and production costs compared to traditional selection methods.
2Reliability
If comprehensive tooling evaluation is performed, then optimal tooling can be selected, but system complexity increases
Solution Approach 1:
The system introduces a simulation module as an intermediary between tooling selection and evaluation. This intermediary component handles the complex calculations of energy consumption and robot performance, allowing the main PDM system to make accurate tooling selections without directly managing the computational complexity. The simulation acts as a mediator that translates complex physical parameters into comparable energy consumption values.
3Productivity
If energy consumption optimization is implemented, then production costs are reduced, but calculation time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing energy consumption data for different tooling candidates during the simulation phase. This preliminary calculation allows for quick comparison and selection of optimal tooling during actual production planning, reducing the time required for final decision-making while maintaining energy optimization benefits.
Data Source
AI summary
Systems and a method for robotic energy saving tool search. The systems and method include receiving inputs including one or more of robot information, tooling information, operation information, and position information. Using the information received, a list of tooling candidates of a robot required to complete one or more tasks for a complex operation and a task location for each of the one or more tasks in the complex operation is generated. Tooling candidate are then removed from the list of tooling candidates when the robot cannot reach every task location on a path required by the complex task. The path is adjusted to remove one or more collision events. The total energy consumption value for each remaining tooling candidate is calculated, and returning the tooling candidate with the lowest energy consumed.