Abstract
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.
Highlights
Intelligent manufacturing systems is targeted at transforming and upgrading manufacturing technologies by Cyber-physical systems (CPS), the internet of Things (IoT), and cloud computing[1]
A new generation of machine tool i.e. machine tool 4.0 known as cyber physical machine tool (CPMT) has been proposed, which is intended to integrate machine tool, machining processes, computation and networking for monitoring and control of machining processes with feedback loops [3]
In 2012, GE developed the idea of the Industrial Internet of Things (IIoT), suggesting that intelligent machines, advanced analytics, and connected people are the key elements of future manufacturing in order to enable smarter decisionmaking by humans and machines
Summary
Intelligent manufacturing systems is targeted at transforming and upgrading manufacturing technologies by Cyber-physical systems (CPS), the internet of Things (IoT), and cloud computing[1]. Systems have been equipped with the ability of monitoring the physical processes and make smart decisions through co-operation with humans, machines, and intelligent devices through real time communication[2]. Data acquisition systems, computer network and cloud computing have prepared infrastructure for designing and implementing intelligent manufacturing systems. The implementation of this technology has resulted into generation of huge amount of data from these intelligent devices. 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. The section reviews the recent technologies being gradually adopted for intelligent manufacturing
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