Machining Estimation Using Intermediary Variables for Inline Gaps
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Solution Overview
Problem
Conventional methods for estimating objective variables in machining processes face challenges when physical models are difficult to configure, requiring extensive measured data and often necessitating multiple experimental designs, which can double the number of experiments needed and fail to accurately estimate variables not collected inline.
Innovation Solution
An estimation method that involves acquiring first-type, second-type, third-type, and fourth-type information through machining experiments, deriving expressions to output multiple solutions, and using these expressions to estimate machining results, allowing for appropriate estimation of machining information even when certain parameters are not measured inline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physical model is hard to be configured, then extensive measured data and multiple experimental designs are required, but this doubles the number of experiments needed and fails to accurately estimate variables not collected inline
Solution Approach 1:
The patent introduces an intermediary variable (third-type information) that mediates between the measurable parameters (first-type, second-type information) and the target variable (fourth-type information). By deriving expressions that output this intermediary variable and then using it to estimate the target variable, the system achieves accurate estimation without requiring direct measurement of all variables or complex multiple experimental designs.
2Loss of information
If multiple experimental designs are used to estimate variables not collected inline, then the number of experiments doubles, but this increases time consumption and resource requirements
Solution Approach 1:
The patent performs preliminary derivation of expressions during the experimental design phase that enable subsequent estimation of target variables from routinely collected data. By preparing these mathematical relationships in advance, the system can estimate variables not collected inline without conducting additional experiments, thus preventing information loss while avoiding time consumption.
3Measurement precision
If conventional estimation methods are used for variables not collected inline, then estimation accuracy is compromised, but the patent's method achieves accurate estimation using derived expressions
Solution Approach 1:
The patent segments the estimation problem into two parts: first deriving expressions that output an intermediary variable (third-type information) from measurable parameters, then using these expressions to estimate the target variable (fourth-type information). This segmentation allows accurate estimation while maintaining modeling ease, as each segment can be developed independently using standard regression techniques.
Data Source
AI summary
A processor performs an experiment of machining a device to acquire first-type and second-type information each indicating conditions of the experiment of machining and third-type and fourth-type information each indicating a result of the experiment of machining (S401). The processor derives a first expression and a second expression, where the first expression receives first-type and second-type information as inputs and outputs third-type information as more than one solution, and the second expression receives first-type and second-type information as inputs and outputs fourth-type information. The processor derives more than one third expression from the first expression, where the more than one third expression each receives second-type and third-type information as inputs and outputs first-type information (S402). The processor receives second-type and third-type information each measured in machining as inputs and outputs fourth-type information indicating a result of machining using the second expression and the more than one third expression (S403).