Material Characteristic Identification From Working Process Correlation
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Solution Overview
Problem
Existing methods for evaluating material characteristic variations in manufacturing processes are inadequate, leading to difficulties in designing stable processes and ensuring shape accuracy due to unknown material variations, which can result in defects.
Innovation Solution
A method involving the creation of correlation data between known material characteristic values and representative values of workpieces, allowing for the identification of material characteristic values of unknown workpieces through measurement and calculation using the same predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of basic tests is increased to accurately evaluate material characteristic variations, then measurement precision is improved, but productivity deteriorates due to the time-consuming nature of multiple basic tests
Solution Approach 1:
The patent segments the material characteristic value identification process into two parts: (1) a calibration phase where correlation data is created between basic test results and working process data for a limited number of reference workpieces, and (2) an evaluation phase where the established correlation is applied to rapidly identify material characteristics of many other workpieces using only working process data. This segmentation allows accurate calibration while enabling rapid evaluation of large numbers of workpieces.
Solution Approach 2:
The patent performs preliminary action by pre-establishing correlation data between basic test results and working process data before actual material characteristic identification. This correlation model is created in advance through calibration tests, allowing subsequent material characteristic values to be determined rapidly without repeating the full basic test procedure for each workpiece.
2Reliability
If assumption range of material characteristic value variation is made larger to ensure coverage of actual variations, then reliability is improved, but manufacturing precision deteriorates due to excessive safety margins
Solution Approach 1:
The patent implements feedback by using identified material characteristic values from actual workpieces to refine and update the correlation data and evaluation models. As more workpieces are tested and their material characteristics identified through the rapid evaluation method, the system accumulates real data that feeds back into improving the accuracy of variation range estimates, allowing progressively tighter and more accurate specification ranges.
Solution Approach 2:
The patent replaces the mechanical approach of using fixed, conservative assumption ranges with a data-driven computational approach. Instead of relying on predetermined safety margins, the system uses actual measured data from working processes combined with correlation models to dynamically determine material characteristic values, replacing rigid mechanical assumptions with flexible information-based evaluation.
Data Source
AI summary
Provided is an identification method of a material characteristic value of a workpiece or a computer to reduce the number of basic tests for acquiring material characteristic values and furthermore to identify the material characteristic values of a larger number of workpieces. The identification method of the material characteristic value of the workpiece or the computer includes: (1) creating, with respect to a first workpiece whose material characteristic value is known, correlation data between the known material characteristic value and a first representative value of the workpiece during working or after working; (2) working on a second workpiece whose material characteristic value is unknown; (3) acquiring a second representative value of the second workpiece during working or after working; and (4) acquiring a material characteristic value of the second workpiece based on the correlation data and the second representative value. Here, the second representative value is a value measured under the same predetermined measurement condition as the first representative value.