Optimum Sampling Search System with Risk Assessment
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Solution Overview
Problem
Conventional optimization search methods often confuse objective and constraint functions, leading to inefficient sampling parameter searches due to excessive sampling points that do not meet constraints in industries like thin film and chemical processes, where objective and constraint function outputs are unknown black-boxes.
Innovation Solution
An optimum sampling search system with risk estimation, incorporating a data acquisition unit, objective satisfaction score calculation, constraint satisfaction probability calculation, sampling risk evaluation, and an adjusting unit, uses models like Gaussian Process and Support Vector Machine to recommend and adjust sampling parameters based on objective and constraint satisfaction probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization search methods are used to obtain sampling parameters, then the search can proceed without advanced risk assessment, but the number of sampling points that do not meet constraints increases, reducing search efficiency
Solution Approach 1:
The system performs preliminary risk assessment before executing sampling points by calculating constraint satisfaction probabilities in advance. The risk assessment unit evaluates each candidate sampling point against constraint functions before it is executed, filtering out high-risk points that are likely to violate constraints. This preliminary action prevents wasteful execution of ineffective sampling points, thereby improving search efficiency without unnecessarily reducing the total number of valid sampling points obtained.
2Adaptability or versatility
If the number of sampling points is increased to improve search coverage, then more potential optimal solutions can be found, but the proportion of sampling points that do not meet constraints increases, wasting resources
Solution Approach 1:
The system implements a feedback mechanism where the risk assessment unit continuously evaluates candidate sampling points based on constraint satisfaction probabilities. As sampling points are executed and results are obtained, the system updates its understanding of the constraint boundaries and adjusts the risk assessment for subsequent candidate points. This feedback loop enables the system to maintain high search coverage by exploring diverse parameter spaces while simultaneously improving constraint satisfaction rates through learned patterns about which regions are likely to violate constraints.
3Ease of operation
If sampling parameters are selected without risk assessment, then the process is simpler and faster to execute, but the quality of sampling points in terms of meeting constraints deteriorates
Solution Approach 1:
The system extracts the risk assessment function as a separate, dedicated component that operates independently from the main sampling execution process. The risk assessment unit takes candidate sampling points as input and outputs a risk score or constraint satisfaction probability, allowing the main execution process to remain simple while incorporating quality filtering. This separation enables the system to maintain execution simplicity by not fundamentally changing the sampling workflow, while simultaneously improving sampling parameter quality through the extracted risk assessment mechanism that filters out low-quality points before execution.
Data Source
AI summary
An optimum sampling search system and method with risk assessment, and a graphical user interface are provided. The optimum sampling search system includes a data acquisition unit, an objective satisfaction score calculation unit, a constraint satisfaction probability calculation unit, a sampling risk evaluation unit, and an adjusting unit. If the constraint satisfaction probability of a recommended sampling parameter is between a first predetermined value and a second predetermined value, the recommended sampling parameter is adjusted, by the adjusting unit, to optimize a constraint satisfaction probability model.