In-Process Quality Improvement - Quality Science for Smart Manufacturing - Introduction

In-process quality improvement: Concepts, methodologies, and applications

Jianjun Shi

To cite this article: Jianjun Shi (2022): In-process quality improvement: Concepts, methodologies,

and applications, IISE Transactions, DOI: 10.1080/24725854.2022.2059725 

Keynote Jianjun Shi - Quality Science for Smart Manufacturing in the Era of Data-Driven Automation

16 Sept 2021

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https://www.youtube.com/watch?v=DmVqO3jN9tA

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In-process quality improvement: Concepts, methodologies, and applications

Jianjun Shi

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

One fundamental component is missing in the existing quality control frameworks: how to make use of multiple in-situ sensing signals, integrated with other process and product data and engineering knowledge, to achieve in-process quality improvements.

What is the innovation of the IPQI, and what are its unique characteristics?

IPQI refers to a set of methodologies of engineering-driven data fusion for process monitoring, root cause diagnosis, and feedback and feed-forward control. 

IPQI embodies those methodologies throughout the life cycle of a process and product, ranging from product design, process design, in-situ sensors, product quality measurement, and maintenance information, among others.

What is the evolution of the IPQI and its applications?

Since the introduction of the IPQI (Shi, 1996), a tremendous amount of effort has been made by the author himself and others interested in IPQI to develop and enrich IPQI methodologies and applications.

Jianjun Shi  -  Jianjun.shi (at the rate) isye.gatech.edu