Einsatzmöglichkeiten von Methoden der Künstlichen Intelligenz zur Optimierung von Stoff- und Energieströmen und prototypische Umsetzung auf der Basis von Stoffstromnetzen

Willenbacher, Martina; Wohlgemuth, Volker

doi:10.1007/978-3-658-20380-1_10

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…A CNN is applied by Scime and Beuth [89] to additive manufacturing, to detect anomalies and defects in the interaction of the deposition blade and the powder bed in selective laser melting or laser powder bed fusion. Willenbach et al [90] combined different AI methods, mainly CNN, to improve the operational material flow management. Choi and Kim [91] developed an "edge AI" using CNN to control the defrosting operation of industrial mobile terminals in logistics.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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“…Furthermore, SVMs do not scale precisely enough for large data sets, because they are designed for smaller quantities. The reason for the decision not to use this algorithm for this work is its limited interpretability [14].…”

Section: Support Vector Machinesmentioning

confidence: 99%

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

2021

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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.

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Einsatzmöglichkeiten von Methoden der Künstlichen Intelligenz zur Optimierung von Stoff- und Energieströmen und prototypische Umsetzung auf der Basis von Stoffstromnetzen

Cited by 2 publications

References 8 publications

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

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

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