TY - JOUR

T1 - Machine learning for optimization of energy and plastic consumption in the production of thermoplastic parts in SME

AU - Willenbacher, Martina

AU - Scholten, Jonas

AU - Wohlgemuth, Volker

N1 - Funding: In cooperation with Novapax Kunststofftechnik Steiner GmbH & Co. KG, the University of Applied Sciences Berlin is working on the implementation of a prototype in the Nova [26] research project to monitor and optimize waste minimization and energy savings in an SME in the plastics industry using machine learning. This research was funded by Deutsche Bundesstiftung Umwelt, grant number 34589/10.

PY - 2021/6/16

Y1 - 2021/6/16

N2 - In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.

AB - In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.

KW - Artificial intelligence

KW - Energy saving

KW - Machine learning

KW - Reduction of emissions and material

KW - Sustainability

KW - Sustainability sciences, Communication

UR - http://www.scopus.com/inward/record.url?scp=85108887610&partnerID=8YFLogxK

U2 - 10.3390/su13126800

DO - 10.3390/su13126800

M3 - Journal articles

AN - SCOPUS:85108887610

VL - 13

JO - Sustainability

JF - Sustainability

SN - 2071-1050

IS - 12

M1 - 6800

ER -