Process Monitoring for Quality – A Step Forward in the Zero Defects Vision 712
Process Monitoring for Quality – A Step Forward in the Zero Defects Vision
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5:30 PM — Arrival, professional networking and meal 
6:00 PM —Presentation  
7:00 PM — Questions and Answers 
7:30 PM — Chapter Committee Meeting  

In this month’s presentation, Carlos will review the traditional quality control techniques based on statistics and explain some of the limitations in analyzing industrial big data. Rare quality event detection is one of the most important modern intellectual challenges posed to the industry. He will emphasize how technology is leveraged by Process Monitoring for Quality to make a step forward in the path of developing defect-free processes.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistical techniques are applied to develop an effective predictive system. Manufacturing-derived data sets for binary classification of quality tend to be highly/ultra-unbalanced, making it very difficult for the learning algorithms to learn the minority (defective), class. Here, the vision of how traditional quality philosophies (based on statistics) and PMQ can collaborate, interact and complement each other to enable the development of zero-defect processes is presented. This vision is supported by a real case study in which 100% of the defects are detected.  

Carlos is a Senior Researcher at the GM R&D Center in the Manufacturing Systems Lab at General Motors and has over 10 years’ experience in the process of quality control. He holds a Ph.D. in Engineering Sciences with a concentration in Artificial Intelligence from Tecnológico de Monterrey, a certified black belt in six sigma and design for six sigma at Arizona State University and the University of Michigan respectively. Carlos’ in-depth knowledge in better decision making through data-driven insights helped hundreds of professionals at all levels improve their data analysis capabilities to make better and more informed decisions that lead to actions and impact.  
Date & Time
Wednesday January 15th, 2020 5:30pm EST
End Date & Time
Wednesday January 15th, 2020 7:30pm EST
Oakland Community College
2900 Featherstone Rd
OCC Bldg. T
Auburn Hills, MI 48326
United States
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January 2020 Bulletin.pdfJanuary 2020 Bulletin.pdf748 KB
Category Chapter Meeting

Event Map

Date & Time: 01/15/2020 05:30:00 PM EST