| 书目名称 | Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Lear | | 编辑 | Thorsten Wuest | | 视频video | | | 概述 | Nominated as an outstanding thesis by Universität Bremen, Germany.Reports on a simple and efficient supervised machine learning approach for the analysis and control of complex, multi-stage manufactur | | 丛书名称 | Springer Theses | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2015 | | 关键词 | Holistic information management; Holonic manufacturing systems; Intelligent manufacturing systems; Mach | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-17611-6 | | isbn_softcover | 978-3-319-38698-0 | | isbn_ebook | 978-3-319-17611-6Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | Springer International Publishing Switzerland 2015 |
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发表于 2025-3-21 16:54:57
