Production Line Control Condition Determination via Historical Data Segmentation
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Solution Overview
Problem
Manufacturers face challenges in determining suitable control conditions for production lines due to complex control combinations and reliance on experiential methods, which often result in inefficient yield improvements and excessive adjustment costs.
Innovation Solution
An apparatus and method that divide historical control condition sets into groups, calculate central tendency, degree of variation, and weight scores to select a suitable control condition set, reducing the need for experiential adjustments and minimizing excessive factor overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturers rely on experiential methods to set control conditions, then they can make decisions based on practitioner knowledge, but the yield related values can only be improved after many times of adjustment and the process is extremely dependent on individual experiences
Solution Approach 1:
The system performs preliminary analysis by dividing historical control condition sets into groups and calculating measurements of central tendency and weight scores before actual production. This pre-computation of optimal conditions based on historical data eliminates the need for multiple trial adjustments during production, directly resolving the contradiction between reliability improvement and time consumption.
Solution Approach 2:
The system creates a virtual model of historical control conditions and their outcomes, analyzing patterns in the data to determine optimal control condition sets. This copying and analysis of historical patterns replaces reliance on individual practitioner experiences and enables systematic optimization without repeated trial adjustments.
2Manufacturing precision
If manufacturers design an experimental method to obtain an optimal control condition set, then they can find optimal control conditions after many experiments, but applying the optimal control condition set to an on-site production line is usually infeasible due to restrictions in range, variation amount, and adjustability
Solution Approach 1:
The system calculates weight scores for different groups of control conditions, allowing each control factor to have locally optimized conditions based on historical performance. This enables the selection of optimal control condition sets that are specifically tailored to the actual constraints and characteristics of the on-site production line, rather than applying a single global optimal solution.
Solution Approach 2:
The system analyzes historical control condition sets to identify patterns and determines optimal control conditions by changing parameters based on measured central tendency and weight scores. This data-driven parameter optimization ensures that the determined control condition sets are both optimal and feasible for on-site production line constraints.
3Reliability
If the control condition set includes many control factors with different conditions, then the yield related values can be improved, but the production line may become overloaded and the complexity of control combinations increases
Solution Approach 1:
The system segments the complex control condition sets into multiple groups based on control factors and their conditions. By dividing the historical control condition sets into groups and calculating weight scores for each group, the system manages complexity systematically while identifying the most effective control combinations for improving yield related values.
Data Source
AI summary
An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.