کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5128842 1489602 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions
چکیده انگلیسی

Cyber-physical systems (CPS) have opened up a wide range of opportunities in terms of performance analysis that can be applied directly to the machine tool industry and are useful for maintenance systems and machine designers. High-speed communication capabilities enable the data to be gathered, pre-processed and processed for the purpose of machine diagnosis. This paper describes a complete real-world CPS implementation cycle, ranging from machine data acquisition to processing and interpretation. In fact, the aim of this paper is to propose a CPS for machine component knowledge discovery based on clustering algorithms using real data from a machining process. Therefore, it compares three clustering algorithms -k-means, hierarchical agglomerative and Gaussian mixture models- in terms of their contribution to spindle performance knowledge during high throughput machining operation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Manufacturing - Volume 10, 2017, Pages 997-1008
نویسندگان
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