Description
hardware flow control. It is an ideal choice in the field of industrial automation.
(5) Perform predictive maintenance, analyze machine operating conditions, determine the main
causes of failures, and predict component failures to avoid unplanned downtime.
Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s
Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts
of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in
large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of
machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing
analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate
nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.
Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is
important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through
controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key
factors that may affect quality and then run
DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively
impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However,
there are some unique data science challenges in manufacturing.
(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms
and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to
be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that
the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives
at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when
translating business goals into technical goals and candidate evaluation methods.
Display operation panel INPCI01
Display operation panel INPBS01
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Display operation panel INNPM22
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Display operation panel INNPM12
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Display operation panel INNIS11
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Display operation panel INLIM03
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Display operation panel INIT03
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Display operation panel INIIT12
Display operation panel INIIT03
Display operation panel INIIT03
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Display operation panel INIIT01
Display operation panel INIET800
Display operation panel INICT13A
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Display operation panel INFBA01
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Display operation panel INDDM01
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Display operation panel IMUSM01
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Display operation panel IMSET01
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Display operation panel IMRIO02
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Display operation panel IMRI002
Display operation panel IMRDO01
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Display operation panel IMRDI13
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Display operation panel IMRCC02
Display operation panel IMRCC02
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