Description
hardware flow control. It is an ideal choice in the field of industrial automation.
(2) Data collection and traceability issues. Data collection issues often occur, and many assembly lines lack “end-to-end traceability.”
In other words, there are often no unique identifiers associated with the parts and processing steps being produced.
One workaround is to use a timestamp instead of an identifier. Another situation involves an incomplete data set. In this case, omit
incomplete information parts or instances from the forecast and analysis, or use some estimation method (after consulting with manufacturing experts).
(3) A large number of features. Different from the data sets in traditional data mining, the features observed in manufacturing analysis
may be thousands. Care must therefore be taken to avoid that machine learning algorithms can only work with reduced datasets (i.e.
datasets with a small number of features).
(4) Multicollinearity, when products pass through the assembly line, different measurement methods are taken at different stations
in the production process. Some of these measurements can be highly correlated, however many machine learning and data mining
algorithm properties are independent of each other, and multicollinearity issues should be carefully studied for the proposed analysis method.
(5) Classification imbalance problem, where there is a huge imbalance between good and bad parts (or scrap, that is, parts that do not
pass quality control testing). Ratios may range from 9:1 to even lower than 99,000,000:1. It is difficult to distinguish good parts from scrap
using standard classification techniques, so several methods for handling class imbalance have been proposed and applied to manufacturing analysis [8].
(6) Non-stationary data, the underlying manufacturing process may change due to various factors such as changes in suppliers
or operators and calibration deviations in machines. There is therefore a need to apply more robust methods to the non-stationary
nature of the data. (7) Models can be difficult to interpret, and production and quality control engineers need to understand the analytical
solutions that inform process or design changes. Otherwise the generated recommendations and decisions may be ignored.
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ABB PNI800
ABB MB805
ABB PNI800SWLIC-01
GE DS200SDCCG1ACA
A-B 1394-SJT22-T-RL
GE DS200TCDAH1BHE
GE DS200PCCAG10ACB
GE VMIVME-7614-132
ENTERASYS A2H124-24
ENTERASYS P0973BJ
ENTERASYS A2H124-24FX
ENTERASYS A2H124-24FX P0973BJ
ENTERASYS P0973BK
ENTERASYS A2H254-16
ENTERASYS A2H254-16 P0973BK
ENTERASYS P0973JN
ENTERASYS A4H124-24FX
ENTERASYS A4H124-24FX P0973JN
ENTERASYS P0973JM
ENTERASYS A4H124-24TX
ENTERASYS A4H124-24TX P0973JM
ENTERASYS P0973JP
ENTERASYS A4H254-8F8T
ENTERASYS A4H254-8F8T P0973JP
ENTERASYS C2RPS-CHAS2
ENTERASYS P0973BP
ENTERASYS C2RPS-PSM P0973BP
ENTERASYS C2RPS-PSM
ENTERASYS SSA-G1018-0652
ENTERASYS SSA-T8028-0652
ENTERASYS SSA-T8028-0652 P0973LN
IS200EXHSG3AEC GE
A-B 1394C-SJT22-A
A-B 1394-SJT22T
A-B 1394-SJT22-C-RL
A-B 2198-D012-ERS4
ZYGO 7702
ZYGO 7701 P/N 6191-0460-01
ZYGO 7702 P/N- 8070-0102-01
ZYGO 7701A/E 6191-0460-02
ZYGO LS-8
ZYGO 7034
ZYGO ZMI7705 8070-0902-01X
ZYGO ZMI501
ZYGO 8020-1513-01 REV.C 2 ZMI 501
ZYGO 260-00106-01
ZYGO 270-00047-00
ZYGO ZMI-2001 MEAS BD 8020-0210 8020-0210-01
ZYGO 8020-0101-04
ZYGO 8020-0450 REV.C
ZYGO 8020-0700-01 PCB
ZYGO 8020-0101-04
ZYGO 8070-0122-01-J
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