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User-Modified Artificial Intelligence Causal Prediction Model for Process Improvement Using Design Document Interface

Year: 2025

Type: article

Abstract: Machine learning a artificial intelligence (AI) derives correlations between manufacturing variables from the data. These correlations can be classified into causal and noncausal relationships. In the design for manufacturing, only causal relationships are applicable because control based on causality is necessary to produce the desired product. Domain knowledge is required to confirm the causality and develop a predictive model based on causal relationships. However, domain experts often lack the AI-related knowledge necessary to develop such models, including the skills in AI, coding, and data mining. To overcome this challenge, we developed an AI system that leverages a design document interface (DDI), allowing domain experts to easily create AI models tailored to their tasks even without extensive AI expertise.

Source: Journal of the korean society of manufacturing technology engineers

DOI: 10.7735/ksmte.2025.34.1.64

Authors: Chang Sun Lee, Sang Chul Lee

Language: English

Topic: Manufacturing Process and Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Korea Institute for Advancement of Technology

Award: G4512390541