NDLI: Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear

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Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear

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A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Co-Design of Supply Chain Network and Subassembly Planning Considering the Reconfiguration of Supply Chain Structure for Factory-in-a-Box Manufacturing

Toward a New Mathematical Definition of Datums in Standards to Support Advanced Manufacturing

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Surface Roughness Prediction in Additive Manufacturing Using Machine Learning

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Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Wang, Xingtao Robert, E. Williams Michael, P. Sealy Rao, Prahalada Guo, Yuebin
Copyright Year	2018
Abstract	The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of Variance (ANOVA) was also used to determine factors significant to the energy consumption by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies (0.1 rad/sec) highlighted by DDS methodology.
Sponsorship	Manufacturing Engineering Division
File Format	PDF
ISBN	9780791851371
DOI	10.1115/MSEC2018-6511
Volume Number	Volume 3: Manufacturing Equipment and Systems
Conference Proceedings	ASME 2018 13th International Manufacturing Science and Engineering Conference
Language	English
Publisher Date	2018-06-18
Publisher Place	College Station, Texas, USA
Access Restriction	Subscribed
Subject Keyword	Energy consumption Cutting Wear Sustainability Milling Machining Time series Motors Manufacturing Modeling Engines
Content Type	Text
Resource Type	Article

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