Study of Motorized Spindle Reliability Monitoring

Chiang, Hsun-Fu; Chang, P; Chen, Jenq-Shyong; Pan, Chung-Yu; He, Yi-Lin

doi:10.23919/ae.2018.8501459

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…Intelligent manufacturing requires certain underpinning technologies in order to enable devices or machines to vary their behaviours in response to different situations and requirements based on past experiences and learning capacities [6]. An intelligent manufacturing system that adopt motorized high speed spindle on machine tools based on reliability engineering was propose by Hsun-Fu et al [7]. This study aimed at conceptualizing a smart-decision making algorithm of an intelligent manufacturing system through integration of recent technologies, such as CPS, IoT, cloud computing, digital twin and real-time communication and information learning for intelligent production system.…”

Section: Introductionmentioning

confidence: 99%

Optimising Production through Intelligent Manufacturing

Olalere

Olanrewaju

2020

E3S Web Conf.

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Intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), digital twin and big data analytics to optimize production through smart manufacturing. This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing. The work considers product monitoring and assessment, machine health and operating parameters monitoring, as an important factor for intelligent decision making on a machining production line through the developed cyber twin of the machine tool for production optimisation. Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics. The ACA enables resources monitoring, production monitoring, machine condition monitoring, cloud feedback notification, product monitoring, and assessment, for intelligent decision-making from a cluster of similar machines using ANN clustering tool for self-aware, self-predict and self-reconfiguration in a smart machining production line to detect a cutting tool chipping of less than 0.25mm size. The method is proposed to optimise production by increasing productivity through intelligent decision and prediction for tool change, tool failure, maintenance, adjustment of operating parameters.

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Section: Introductionmentioning

confidence: 99%

Optimising Production through Intelligent Manufacturing

Olalere

Olanrewaju

2020

E3S Web Conf.

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An IoT system for managing machine tool spindles in operation

Bachim

Martens

Gonçalves

et al. 2023

Int J Adv Manuf Technol

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With the development of production processes over the last decades, machine tools have evolved, and in everyday life today it is impossible not to visualize the new facilities arising from technological developments, such as IoT (Internet of Things) systems. Because they are so relevant, they have become a competitive differential in all markets and will not be different in the transformation industry or in its operational areas. Estimating when any machine will fail beforehand is fundamental to maximizing the company's results and consequently minimizing operating costs. In this sense, this research aimed to develop an IoT system for the online management of machine tool spindles in operation and to provide reliable data for maintenance management in the context of Industry 4.0 (I4.0). The development of the system took place through the Design Science Research (DSR) methodology. Thus, with the systematic review of the literature, the artifact/theoretical system of the method type for online monitoring of spindles in operation was developed, and its implementation was demonstrated and validated via a case study with participant observation in an automotive industry. The main contributions of the research are the artifact/system developed and validated and the associated predictive maintenance method. With the artifact, the normal behavior of the spindles in operation is demonstrated, contributing both to the academy eld and to the practice in the industry, in the sense of advancing knowledge and preventing catastrophic failures in machine tools.

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Study of Motorized Spindle Reliability Monitoring

Cited by 2 publications

References 1 publication

Optimising Production through Intelligent Manufacturing

Optimising Production through Intelligent Manufacturing

An IoT system for managing machine tool spindles in operation

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