Manufacturing Process Analysis for Lot Variation Hindering Factors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional manufacturing process analysis methods face challenges in real-time abnormality diagnosis and do not effectively stabilize product performance by failing to identify factors contributing to variation between lots, especially in non-defective products, leading to inconsistent quality supply.
Innovation Solution
A manufacturing process analysis method that involves collecting product and process data, standardizing it, performing principal component analysis, applying cluster analysis to classify lots, determining relative merit, and specifying hindering factors contributing to these merits, thereby improving process efficiency and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multivariable analysis method is used for abnormality diagnosis, then product performance variation can be suppressed, but real-time diagnosis becomes difficult and product performance must be acquired beforehand
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the relationship between process data and product performance through offline multivariable analysis. The analysis results are stored as reference data that can be quickly queried during real-time production without performing complex calculations at that moment, thus achieving both real-time responsiveness and accurate performance prediction
Solution Approach 2:
The patent introduces an intermediary mechanism by using pre-computed analysis results as a bridge between process data and product performance. Instead of directly performing multivariable analysis in real-time, the system uses stored analysis outcomes as intermediate data that facilitates rapid diagnosis while maintaining the benefits of comprehensive multivariable analysis
2Manufacturing precision
If abnormality diagnosis excludes only defective products, then defective products can be identified, but high and low quality products are not distinguished and stable high quality supply cannot be achieved
Solution Approach 1:
The patent applies local quality by differentiating the quality assessment across different product categories. Instead of treating all non-defective products uniformly, the system identifies and distinguishes high-quality products from low-quality products within the non-defective category, applying different quality standards and improvement strategies to different local quality levels
Solution Approach 2:
The patent segments the product quality spectrum into multiple levels (high quality, medium quality, low quality, defective) rather than a simple binary classification. This segmentation allows for targeted improvement strategies for each quality level, enabling the system to not only identify defective products but also to promote overall quality enhancement across all product categories
Data Source
AI summary
To provide a manufacturing process analysis method for specifying a hindering factor that causes a variation in product performance and for stabilizing product performance. A manufacturing process analysis method comprises: a step for collecting product data indicating the quality of a product and process data indicating manufacturing conditions of a product; a step for standardizing the process data so that the data are converted into an intermediate function; a step for performing principal component analysis on the intermediate function to derive a principal component load amount and a principal component score of the process data; a step for applying cluster analysis to the principal component score to classify manufacturing process lots into a plurality of groups; a step for determining relative merit of each group on the basis of product data soundness corresponding to the principal component score belonging to the group; and a step for specifying a hindering factor that contributes to the relative merit of the group.