Machine learning techniques for quality control in high conformance manufacturing environment

CA Escobar… - Advances in Mechanical …, 2018 - journals.sagepub.com
In today's highly competitive global market, winning requires near-perfect quality. Although
most mature organizations operate their processes at very low defects per million …

Quality 4.0–an evolution of Six Sigma DMAIC

CA Escobar, D Macias, M McGovern… - International journal of …, 2022 - emerald.com
Purpose Manufacturing companies can competitively be recognized among the most
advanced and influential companies in the world by successfully implementing Quality 4.0 …

Big data-driven manufacturing—Process-monitoring-for-quality philosophy

JA Abell, D Chakraborty… - Journal of …, 2017 - asmedigitalcollection.asme.org
Discussion of big data (BD) has been about data, software, and methods with an emphasis
on retail and personalization of services and products. Big data also has impacted …

Machine learning and pattern recognition techniques for information extraction to improve production control and design decisions

CA Escobar, R Morales-Menendez - Industrial Conference on Data Mining, 2017 - Springer
In today's highly competitive global market, winning requires near-perfect quality. Although
most mature organizations operate their processes at very low defects per million …

[HTML][HTML] Implementation strategy for launch and performance improvement of high throughput manufacturing inspection systems

JP Spicer, D Chakraborty, M Wincek, J Abell - Manufacturing Letters, 2024 - Elsevier
Product technologies are changing rapidly in advanced automotive propulsion systems.
These products are driving the need for new manufacturing processes and new inspection …

Process monitoring for quality—A multiple classifier system for highly unbalanced data

CA Escobar, D Macias, R Morales-Menendez - Heliyon, 2021 - cell.com
In big data-based analyses, because of hyper-dimensional feature spaces, there has been
no previous distinction between machine learning algorithms (MLAs). Therefore, multiple …

Process-monitoring-for-quality—big models

CA Escobar, JA Abell, M Hernández-de-Menéndez… - Procedia …, 2018 - Elsevier
Abstract Process Monitoring for Quality (PMQ) is a big data-driven quality philosophy aimed
at defect detection (through binary classification) and empirical knowledge discovery. It was …

Process-monitoring-for-quality—a model selection criterion for l1-regularized logistic regression

CA Escobar, R Morales-Menendez - Procedia Manufacturing, 2019 - Elsevier
Process monitoring for quality is a big data-driven quality philosophy aimed at defect
detection through binary classification. The l 1-regularized logistic regression learning …

Process-monitoring-for-quality—a model selection criterion for support vector machine

CA Escobar, R Morales-Menendez - Procedia manufacturing, 2019 - Elsevier
In today's manufacturing environment, most mature organizations generate only a few
Defects Per Million of Opportunities (DPMO). Detecting these defects is one of the main …

Process-monitoring-for-quality—a model selection criterion

CA Escobar, R Morales-Menendez - Manufacturing Letters, 2018 - Elsevier
The new big data driven manufacturing quality philosophy, Process Monitoring for Quality
(PMQ), proposes Big Data—Big Models, a new modeling paradigm that includes a big data …