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 …
most mature organizations operate their processes at very low defects per million …
Quality 4.0–an evolution of Six Sigma DMAIC
Purpose Manufacturing companies can competitively be recognized among the most
advanced and influential companies in the world by successfully implementing Quality 4.0 …
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 …
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 …
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
Product technologies are changing rapidly in advanced automotive propulsion systems.
These products are driving the need for new manufacturing processes and new inspection …
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
In big data-based analyses, because of hyper-dimensional feature spaces, there has been
no previous distinction between machine learning algorithms (MLAs). Therefore, multiple …
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 …
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 …
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 …
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 …
(PMQ), proposes Big Data—Big Models, a new modeling paradigm that includes a big data …