Workpiece Shape Setting Using Past Processing Performance
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Solution Overview
Problem
Designers face challenges in determining optimal dimensions and machine selection for workpieces, as existing systems lack evaluation of shape and machine suitability, leading to increased processing time and cost due to inadequate consideration of curvature radii and surface roughness.
Innovation Solution
A design support apparatus that stores performance information for past processing, allowing extraction and evaluation of similar shapes and materials, and selects appropriate processing techniques based on evaluation results to set optimal workpiece shapes and machine selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the curvature radius of the corner is set to a small value, then the shape precision is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary evaluation of the workpiece shape using past performance information before actual processing. By extracting similar workpiece data and evaluating processing techniques in advance, the system identifies optimal curvature radii that balance precision and processing time, preventing unnecessary time loss while maintaining required shape precision.
Solution Approach 2:
The system uses feedback from past performance information to continuously improve shape setting. By storing and retrieving processing results, processing times, and shape data from previous operations, the system learns from experience to optimize future curvature radius selections, achieving better precision-time trade-offs.
2Manufacturing precision
If the surface roughness is set finer than necessary, then the manufacturing precision is improved, but the processing cost increases
Solution Approach 1:
The system applies partial action by setting surface roughness to the minimum necessary level rather than excessively fine. By evaluating past performance information and determining the actual required precision for each workpiece, the system avoids unnecessary over-processing, reducing processing cost while maintaining adequate surface quality.
Solution Approach 2:
The system dynamically adjusts the surface roughness parameter based on evaluated past performance data. By changing the surface roughness setting to match the actual requirements rather than using fixed fine settings, the system optimizes the balance between manufacturing precision and processing cost.
3Ease of operation
If the operator determines the design shape based on experience, then the ease of operation is improved, but the manufacturing precision deteriorates
Solution Approach 1:
The system introduces an intermediary evaluation mechanism between the operator's experience and the final design shape. By automatically extracting past performance information and evaluating optimal processing techniques, the system mediates between operator input and precision requirements, providing data-driven shape optimization recommendations that improve manufacturing precision while maintaining operational ease.
Solution Approach 2:
The system enables self-service by automatically evaluating and optimizing workpiece shapes based on stored performance information. The system serves itself by learning from past data and automatically adjusting shape parameters, reducing reliance on operator experience while improving precision.
4Adaptability or versatility
If multiple types of machines are available, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The system uses feedback from past performance information to simplify machine selection among multiple available machines. By evaluating which machines produced the best results for similar workpieces in the past, the system provides data-driven recommendations that reduce selection complexity while maintaining adaptability to different workpiece requirements.
Solution Approach 2:
The system enables self-service in machine selection by automatically evaluating and recommending suitable machines based on stored performance data. The system serves itself by learning from past machine performance and automatically identifying optimal machine-workpiece matchings, reducing the complexity of machine selection while maintaining versatility.
Data Source
AI summary
A design support apparatus stores performance information of past processing of a workpiece, design information of a current workpiece, and allowance information including allowable shape information by which a change in dimension is permitted to a design shape. The respective information are stored for each element of the workpiece. The design support apparatus includes an extraction unit extracting the performance information including a shape of an element similar to a design shape of an element of the current workpiece. The design support apparatus includes an evaluation unit evaluating a processing technique of the elements included in the performance information. The design support apparatus includes a shape setting unit setting a shape of the element based on the processing technique selected by the evaluation unit and an allowable shape.