Roughing Toolpath Sequencing for Cutting Tool Combination Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing subtractive manufacturing processes face challenges in determining optimal roughing toolpath sequences for complex parts, as experienced users often struggle to select the most efficient cutting tool combinations and operational parameters, leading to suboptimal machining times and tool wear.
Innovation Solution
A computer-aided design and manufacturing system that generates and optimizes roughing toolpath sequences by estimating machining times and volumes for different cutting tool combinations, using a cost function and simulation to select the best tool selections and operational parameters, including tool replacements and parameter variations, to minimize machining time and tool wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If experienced users manually program roughing operations, then they can utilize their expertise, but they cannot readily determine optimal roughing procedures for new complex parts
Solution Approach 1:
The system enables automatic generation of roughing toolpath sequences without requiring user expertise. The computer program autonomously determines optimal cutting tool combinations, operational parameters, and toolpath sequences by evaluating multiple candidate combinations against cost functions, allowing the system to serve itself rather than relying on user programming skills.
Solution Approach 2:
The patent replaces manual user expertise and experience-based decision making with an automated computational system. The mechanical process of user analysis and programming is substituted with computer-based cost function evaluation, simulation, and automatic optimization algorithms that systematically assess multiple tool combination scenarios.
2Productivity
If multiple cutting tool combinations are tested to find optimal roughing procedure, then machining efficiency is improved, but computational complexity increases
Solution Approach 1:
The evaluation process is segmented into distinct computational stages: generating candidate tool combinations, estimating machining results for each candidate, evaluating cost functions, and selecting optimal sequences. This segmentation allows the complex problem to be broken down into manageable computational tasks that can be processed systematically.
Solution Approach 2:
The system performs preliminary estimation of machining results for multiple candidate tool combinations before final optimization. By pre-evaluating candidate combinations using cost functions and simulation data, the system narrows down the search space and identifies promising sequences ahead of time, reducing the overall computational burden.
3Manufacturing precision
If comprehensive simulation and cost function optimization are performed, then near-optimal roughing operations are determined, but processing time increases
Solution Approach 1:
The system evaluates multiple candidate tool combinations beyond what a single optimal sequence would require, using cost function estimation to quickly filter out inferior options. By performing partial evaluations on many candidates and focusing detailed simulation only on promising sequences, the system achieves near-optimal results without exhaustive computation of all possible combinations.
Solution Approach 2:
The system uses estimated machining results and cost function evaluations as proxies for full simulation. Rather than performing complete detailed simulations for every candidate combination, the system uses simplified cost models to estimate performance, reserving comprehensive simulation only for final optimization of selected candidates, thereby reducing overall computation time.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using roughing toolpath sequences generated for subtractive manufacturing include, in one aspect, a method including: obtaining 3D models of a part and a workpiece and information regarding different cutting tools and cutting data therefor; determining a set of candidate combinations of the different cutting tools to effect the roughing operations by estimating a target machining result for each of multiple, tool-size-ordered lists of the different cutting tools; generating an expanded set of combinations of the different cutting tools to effect the roughing operations by adding variations of the candidate combinations; populating a multidimensional roughing operations representation vector using the expanded set of combinations; optimizing values of the multidimensional roughing operations representation vector using simulation of the roughing operations; and providing specified tool selections and operational parameters for use in roughing the part.