Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME

Abstract

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.

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

“…33 of the selected articles have chosen this approach among others. This can be ascribed, in part, to the limited prevalence of AI adoption within SMEs (Willenbacher et al 2021). While case studies can be conducted and analyzed in technologically pioneering companies, the implementation of quantitative methods, such as surveys, is more difficult to implement due to the high demand for participating SMEs.…”

“…The second most common reason for the hesitation to implement AI in SMEs are the costs (number of articles that addressed this challenge: 24). This is unanimously attributed to the limited financial resources, which are generally much more limited in SMEs than in MNEs (Chen et al 2019;Willenbacher et al 2021). Another complicating factor for SMEs lies in their often-limited awareness of their financial capacities or an inability to accurately assess them (Žigiene et al 2019).…”

“…Thirdly, this perceived intricacy is increased by highly technical case studies on AI in SMEs, for instance, with detailed descriptions of individual algorithms. Only rarely publications simplified to an understandable level for managers without IT expertise (Willenbacher et al 2021).…”

“…In addition to the resulting difficulty in determining the ROI, many scholars (10) also see the risk of failure as a challenge, especially for SMEs. Since these enterprises are often unaware of their own risk profile and also have fewer financial resources, failed attempts are a major problem that strongly influence the company's performance (Willenbacher et al 2021). For this reason, those enterprises rely more frequently on mature technologies, which prevents or slows down the adoption of AI in SMEs at the moment (Bettoni et al 2021).…”

Modeling Energy Consumption Using Machine Learning

“…Classification machine learning models like K-Nearest Neighbors, Neural Networks, Random Forest, and Support Vector Machines are used to validate the quality of production processes and their parameters in the food processing industry (Milczarski et al, 2020). In plastic-processing SME's, machine learning is applied to optimize energy consumption and reduce incorrectly produced plastic parts (Willenbacher et al, 2021).…”