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 SA801F
Display operation panel SA801F
Display operation panel SA610
Display operation panel SA168 3BSE003389R1
Display operation panel S900-BI100
Display operation panel S503X
Display operation panel S503X
Display operation panel S200-TB3T
Display operation panel S200-TB3
Display operation panel S200-PS13
Display operation panel S200-IR8
Display operation panel S-113N 3BHB018008R0101
Display operation panel S-113N 3BHB018008R0001
Display operation panel s-113N 3BHB018008R0001
Display operation panel S-093H 3BHB030478R0309
Display operation panel S-093H 3BHB030478R0309
Display operation panel S-076N 3BHB009884R0021
Display operation panel S-073N 3BHB009884R0021
Display operation panel S-073N 3BHB009884R0021
Display operation panel RXTUG 22H 1MRK000592-A
Display operation panel RXPPK 2H 1MRK001615-AA
Display operation panel RXIIK 4 1MRK001643-AA
Display operation panel RXIDK 2H 1MRK000838-HA
Display operation panel RXEDK 2H 1MRK000841-RA
Display operation panel RXDS 4 1MRK000344-A
Display operation panel RX865
Display operation panel RX845
Display operation panel RX835
Display operation panel RX835
Display operation panel RW857F
Display operation panel RW856F
Display operation panel RVC6-5A
Display operation panel RVC6-5A
Display operation panel RTAC-01
Display operation panel RMU811
Display operation panel RMIO-02C
Display operation panel RLY-100
Display operation panel RLM01 3BDZ000398R1
Display operation panel RLM01
Display operation panel RLM01
Display operation panel RLM01
Display operation panel RLM01
Display operation panel RINT-5521C
Display operation panel RINT-5521C
Display operation panel RID-04 3HNA020882-001
Display operation panel RFO810
Display operation panel RF620
Display operation panel RF615
Display operation panel RF615
Display operation panel RF533 3BSE014227R1
Display operation panel RF522 3BSE000743R1
Display operation panel REX521GHHGSH51G
Display operation panel REX521GBHPLB01G
Display operation panel REX010
Display operation panel REU615E_D
Display operation panel REU610CVVHCNR
Display operation panel RET670
Display operation panel RET670
Display operation panel RET670
Display operation panel RET650
Display operation panel RET650
Display operation panel RET630
Display operation panel RER133
Display operation panel REM615
Display operation panel REM545BM223AAAA
Display operation panel REM545BM222BAAA
Display operation panel REM543CM216AAAA
Display operation panel REM543CM214AAAB
Display operation panel REG670
Display operation panel REG216
Display operation panel REF620C
Display operation panel REF615R
Display operation panel REF615K
Display operation panel REF615F
Display operation panel REF615E
Display operation panel REF615C-E
Display operation panel REF615C-D
Display operation panel REF615A_E HAFAABAAABE1BCA1XE
Display operation panel REF615
Display operation panel REF611C-E
Display operation panel REF610C11LCLR
Display operation panel REF610
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