Digital twin modelling for optimizing the material consumption: A case study on sustainability improvement of thermoforming process

Abstract

Reducing material consumption is a rising issue for manufacturers as the UN’s Sustainable Development Goals and the European Union’s Green New Deal minimize carbon footprints. Sensors and PLC data-enabled digital twin applications stand as a remedy to minimize material consumption, maximize product performance and prevent rework through process quality improvements. They also provide insights to process parameters, enabling preventive actions and hence optimize production. However, digital twin applications require in-depth simulation expertise and an advanced understanding of the process and the product. This paper aims to present a digital twin modeling application for improving process and product quality. Hence, the sustainable production performance in a refrigerator production line of a large Turkish durable goods manufacturer, Arçelik, collaborates with a Digital Twin Startup, Simularge. The digital twin modeling used sensors and PLC data. The development team validated the digital twin after a detailed analysis of the process characteristics, process parameters, and process challenges with material modeling. Finite element simulations and several data analytic tools were combined to obtain the digital twin. Arçelik used the digital twin of the thermoforming process in its production system for better optimization. This implementation has significantly improved production KPIs and quality by decreasing the scrap ratio by 50 % and the raw material consumption by 10 %, resulting in an annual savings of USD 2 Million. Digital twins may enable production optimization with an improved sustainability performance. Collaboration and production data availability are the enablers of success. © 1905 Elsevier Science. All rights reserved