Causal Modeling of Manufacturing Conditions With Less Training Data
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Solution Overview
Problem
Existing data processing systems require large volumes of data to accurately model the relationship between manufacturing apparatus conditions and product quality, as they fail to account for various influencing elements such as individual machine differences, material variations, and environmental factors.
Innovation Solution
A data processing apparatus and method that acquires a data set associating objective and conditional variables, generates structured data representing causal structures using directed edges, and models the manufacturing apparatus by calculating explanatory functions to model the manufacturing process, incorporating both operating conditions and influential variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a matrix representing the relationship between operating condition and objective variable is prepared for each of various elements having influence on the objective variable, then the relationship can be specified more accurately, but a large volume of data is required
Solution Approach 1:
The patent segments the complex relationship modeling into two distinct components: (1) a causal structure model that represents the qualitative relationships between elements using a directed acyclic graph, and (2) a parameter adjustment model that quantitatively determines operating condition changes. This segmentation allows accurate relationship specification without requiring exhaustive data for all possible combinations of elements and conditions.
Solution Approach 2:
The patent introduces an intermediate causal structure model (directed acyclic graph) that mediates between the various influential elements and the objective variable. This intermediate representation captures the essential relationships without requiring direct empirical data for all parameter combinations, thereby reducing the volume of data needed while maintaining specification accuracy.
2Measurement precision
If multiple matrices are prepared for different elements (molding machine individual differences, materials, molds, installation environments), then the relationship specification becomes more accurate, but the system complexity increases
Solution Approach 1:
The patent merges the treatment of multiple influential elements (molding machine individual differences, materials, molds, installation environments) into a unified causal structure model. Instead of maintaining separate matrices for each element, the directed acyclic graph integrates all elements and their interactions in a single coherent framework, thereby reducing system complexity while preserving relationship specification accuracy.
Solution Approach 2:
The causal structure model serves as a universal framework that can accommodate multiple types of influential elements simultaneously. The same model structure handles diverse elements (machines, materials, molds, environments) through a common representation and adjustment mechanism, eliminating the need for element-specific matrices and reducing overall system complexity.
Data Source
AI summary
A data processing apparatus includes an acquisition unit that acquires a data set, a structuring unit that generates structured data representing a causal structure between a conditional variable and an objective variable by connecting a leaf node representing the conditional variable, a root node representing the objective variable, and an intermediate node representing an element having an influence on the objective variable using the data set, and a modeling unit that models a manufacturing apparatus by sequentially calculating a function representing a variable of an upper node as an end point of the specific edge by using a variable of a lower node as a start point of the specific edge from the leaf node to the root node using the data set, and generating a model function as a function including the calculated explanatory function.