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Smart Industrial Packaging and Sorting System

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Strategic System Assurance and Business Analytics

Part of the book series: Asset Analytics ((ASAN))

Abstract

In today’s world, automation has become an inseparable part of our lives. Simultaneously, food is the basic unit on which humans survive and when technology and food are thought together, it becomes important to heed to the hygiene, quality and cost. As of today’s scenario, 34% food available in the globe is packaged which makes it crucial to look forward towards the shortcomings and the advancements in the production, packaging, inventory, supply chain, package handling, quality management and other related aspects. In the conventional packaging system, power failure and human error affect the efficiency of the system, miss-match of the processes and waste of time which is non-negotiable for any FMCG. This paper encompasses engineering solution to the mentioned challenges. It includes barcode sensor assisted with a load cell, a decision-making device—Arduino Mega 2560, programmable logic controller(s) (PLC) for the identification and sorting of CLD(s) or RSC(s) along a retracting conveyor. For the packaging solution, a revolving disc type lid closing system and a compact taping mechanism have been introduced which can reduce dependency on manpower for CLD packing. GSM module in the feedback to the microcontroller is placed, such that any deviation from the default parameters will be sensed automatically, and area of problem occurrence (AOC) is easily identified. In addition to above advents, this paper also talks about the SCADA implemented HMI(s) in order to meet the industry standards of monitoring and control along with the overall condition monitoring and data acquisition for research and analysis.

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Correspondence to Vikas Singh Bhadoria .

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Tripathi, S., Shukla, S., Attrey, S., Agrawal, A., Bhadoria, V.S. (2020). Smart Industrial Packaging and Sorting System. In: Kapur, P.K., Singh, O., Khatri, S.K., Verma, A.K. (eds) Strategic System Assurance and Business Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3647-2_18

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