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Books > Professional & Technical > Mechanical engineering & materials > Production engineering
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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning (Paperback, Softcover reprint of the original 1st ed. 2015)
Loot Price: R3,660
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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning (Paperback, Softcover reprint of the original 1st ed. 2015)
Series: Springer Theses
Expected to ship within 10 - 15 working days
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The book reports on a novel approach for holistically identifying
the relevant state drivers of complex, multi-stage manufacturing
systems. This approach is able to utilize complex, diverse and
high-dimensional data sets, which often occur in manufacturing
applications, and to integrate the important process intra- and
interrelations. The approach has been evaluated using three
scenarios from different manufacturing domains (aviation, chemical
and semiconductor). The results, which are reported in detail in
this book, confirmed that it is possible to incorporate implicit
process intra- and interrelations on both a process and programme
level by applying SVM-based feature ranking. In practice, this
method can be used to identify the most important process
parameters and state characteristics, the so-called state drivers,
of a manufacturing system. Given the increasing availability of
data and information, this selection support can be directly
utilized in, e.g., quality monitoring and advanced process control.
Importantly, the method is neither limited to specific products,
manufacturing processes or systems, nor by specific quality
concepts.
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