Evolution of Manufacturing and Its Journey Towards Digital Twin

Pal, Surjya K.; Mishra, Debasish; Pal, Arpan; Dutta, Samik; Chakravarty, Debashish; Pal, Srikanta

doi:10.1007/978-3-030-81815-9_1

Cited by 1 publication

(2 citation statements)

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“…Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by the introduction of Industry 4.0, the fourth industrial revolution.It aims at continuous monitoring of high-value manufacturing assets [1,2]. This article reportsanIndustry 4.0 application developed and implemented on a welding process.…”

Section: Introductionmentioning

confidence: 99%

“…Industry 4.0 has brought network connectivity into manufacturing [5]. It aims at telemonitoring, i.e., to transmit data to remote/cloud servers enabling the Internet of Things (IoT) [2]. This article presents an Industry 4.0-enabled framework for continuous monitoring and feedback-based real-time control of an advanced welding technique named friction stir welding (FSW).In the following, the theory of FSW process is discussed first, and then state of the prior research is elaborated.…”

Section: Introductionmentioning

confidence: 99%

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Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Mishra

Priyadarshi

Das

et al. 2022

JDMD

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Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by Industry 4.0, which aims to continuously monitor high-value manufacturing assets. This article builds upon the Industry 4.0-concept to improve the efficiency of manufacturing systems. The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding (FSW) process. It consists of a CNC manufacturing machine, sensors, edge, cloud systems, and deep neural networks, all working cohesively in real-time. The edge device, located near the FSW machine, consists of a neural network that receives sensory information and predicts weld quality in real-time. It addresses time-critical manufacturing decisions. Cloud receives the sensory data if weld quality is poor, and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine. Several experiments are conducted for training the neural networks. The framework successfully tracks process quality and improves the welding by controlling it in real-time. The system enables faster monitoring and control achieved in less than 1 second. The framework is validated through several experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%