Kazuto Kojitani

SoS aspects of health management technology in substrate manufacturing process

Complex manufacturing processes such as substrate manufacturing could be alive because they change continuously and cyclically in order to improve their health. The health management technology has been proposed as general solution framework for complex systems like manufacturing process. In this article, the System of Systems aspects of the technology and its application are discussed. Firstly, the health management technology is briefly introduced by centering the notion of causality. As its application, causality-based quality control in substrate manufacturing process is developed by applying the technology. Among the quality control, process anomaly diagnosis is investigated by using the real manufacturing production line for experimental use. Experiments were conducted to prove the effectiveness and efficiency of the health management technology.

Problem Formalization and Problem Solving Approach based on Fusion Model

The quadrant for problem formalization is introduced based on problem definition and structure. Problem formalization has significant influence on not just for satisfying reasonable cost and performance but also conforming problem solving approach. Based on the quadrant, problem solving approach is discussed with considering the fusion model of human experts’ domain knowledge and field data. The fusion model plays an important role in problem solving stages of Learning and Execution. Auditory-based product inspection systems as real world problems are studied with considering problem formalization concepts and the fusion model.

A Study of Progressive Solution to Data Distribution Maturity Problem at Product Inspection Stage of Manufacturing

Production lines are alive because the important factors of production such as man, machine, material, method, and environment (4M+E for short) change suddenly and continuously. The change causes serious obstacles to achieving the requirements of quality, cost, and delivery (QCD for short). In this article, a progressive solution to the production change problem is proposed and discussed. The target is product inspection of production process. The problem to be solved here is to acquire the inspection model and criteria from multivariate data set extracted from sensing data. The change of 4M + E and QCD in production is reflected in the data distribution; i.e., sophistication of production process is strongly connected with data distribution maturity. The proposed method is to fuse one class SVM as a non-parametric method and MTS as a parametric method according to the data distribution maturity. Experiments were conducted to evaluate performance of the proposed method.

Fusion Model and Human-Machine Collaborative Solution for Automated Sensory Inspection Systems

Manufacturing processes are actually alive because the factors of the processes changes continuously and suddenly. In response to the change problems, sophisticated knowledge of skilled human experts has been applied to solve them flexibly. However, this kind of solution is subjective, inconsistent, and dependent on certain experts. Deskilling is a key to realize stable manufacturing with reasonable quality, cost, and delivery. Many types of automated systems have been deployed at the manufacturing process for realizing the deskilling. However, there still remain problems in flexibility against the changes. In this article, an automated sensory inspection system is employed to discuss the problems and the flexible solution against the changes. Based on the discussion, two types of solutions are proposed. The first one is fusion model and the second, human-machine collaborative decision making. Both solutions are considering the data distribution maturity in the manufacturing line.