machine learning

Requirements for the Use of Digitization and AI

Requirements for the Use of Digitization and AI

Applications for increasing energy efficiency
Dennis Bode, Henry Ekwaro-Osire, Klaus-Dieter Thoben ORCID Icon
Innovative digital and AI solutions for more energy-efficient production can decisively contribute to the environmental impact and competitiveness of companies, especially in the manufacturing industry. Requirements for the functionality and implementation of these solutions are complex and diverse; multiple stakeholders need to be addressed when eliciting requirements and various technology and business aspects have to be considered. This article presents a procedure for requirements elicitation for energy efficiency digitalization and AI projects.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 1 | Pages 17-22 | DOI 10.30844/I40M_22-1_17-22
A Self-Learning Assistance System for Industrial Robots

A Self-Learning Assistance System for Industrial Robots

Gestenbasierte Programmierung von skillbasierten Robotersystemen in der Montage
Ulrich Berger, Marlon Lehmann, Ronny Porsch
In the project ARAS (Advanced Robot Assistance Solution) a robot programming assistant was developed, which allows for automated generation of robot programs for assembly processes. By using a multimodal approach for human-machine-interaction, assembly steps are recognized with machine learning algorithms while a worker is showing the robot how an assembly process is performed. Afterwards, a robot program is generated automatically. This way, new robot programs are created within minutes without the user having any knowledge about programming or robotics.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 6 | Pages 23-26
Autonomous Quality Inspection 4.0

Autonomous Quality Inspection 4.0

Reducing pseudo defects in PCB production by integrating machine learning (ML)
Florian Meierhofer, Jochen Deuse ORCID Icon, Lukas Schulte, Nils Killich
Customers are increasingly demanding electronic components with high quality, which forces companies to continuously fulfil these requirements. This leads to a high number of inspection gates with high inspection severity and a high number of pseudo defects. Double inspections by process experts reduce these defects but generate high inspection costs. Autonomously acting inspection systems meet this challenge. Within this article, a machine learning algorithm was integrated into the solder paste inspection process to form an autonomous quality inspection system.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 6 | Pages 52-56
Quantum Computing: A Brief History

Quantum Computing: A Brief History

With applications of quantum computing in automotive
David von Dollen, Daniel Weimer, Florian Neukart
In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?
Industrie 4.0 Management | Volume 37 | 2021 | Edition 4 | Pages 34-36
Modeling the Usage of Knowledge for Industry 4.0

Modeling the Usage of Knowledge for Industry 4.0

Norbert Gronau ORCID Icon
This paper describes an analysis and design method for knowledge management integrating man and machine in the age of the 4th Industrial revolution (Industry 4.0). Digitized work p rocesses require employees in an Industry 4.0 environment to have the competence to adequately deal with fluid situations on the basis of their own knowledge and the ability to place this knowledge in situation-specific contexts. To this end, the development of a comprehensive understanding of processes is elementary.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 3 | Pages 6-10 | DOI 10.30844/I40M_21-3_S6-10
Digital Twin in Plastics Technology

Digital Twin in Plastics Technology

Lifetime-optimized production of technical components by using data-driven methods
Jacqueline Schmitt, Ralph Richter, Jochen Deuse ORCID Icon, Jan-Christoph Zarges, Hans-Peter Heim
The quality of injection-molded components is becoming increasingly important in polymer technology due to extended areas of application with higher mechanical loads. As established methods of quality assurance are increasingly reaching their limits, the digital twin as a basis for cross-process and cross-company data analysis opens up new possibilities in plastics technology for proactive and predictive monitoring and improvement of process and component quality when processing plastics into technical components.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 17-20
Machine Learning in Supply Chain Management

Machine Learning in Supply Chain Management

An overview of existing approaches based on the SCOR model
Benjamin Seifert, Theo Lutz ORCID Icon
With increasing availability of data, the use of machine learning to optimize supply chains becomes attractive, as the accuracy of data analysis can be increased and simultaneously the effort can be reduced. Based on the SCOR model, exemplary approaches are described as a guidance and suitable machine learning methods are presented.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 49-51
Approach to the Condition Description of Technical Components

Approach to the Condition Description of Technical Components

Prediction of remaining useful life based on discretely recorded component states using mobile sensor technology
Lukas Egbert ORCID Icon, Anton Zitnikov ORCID Icon, Thorsten Tietjen, Klaus-Dieter Thoben ORCID Icon
This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 35-38 | DOI 10.30844/I40M_21-2_S35-38
A Machine Learning Compass for Product Development and Production

A Machine Learning Compass for Product Development and Production

Identification and planning of machine learning algorithms in manufacturing companies
Alexander Jacob, Carmen Krahe, Rebecca Funk, Gisela Lanza ORCID Icon
Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 7-11
Autonomous Productions and Robots

Autonomous Productions and Robots

Possibilities and research fields of machine learning methods for production environments
Marco Huber
Everyone is talking about artificial intelligence and machine learning. However, knowledge about what the terms actually mean is often not yet extensively available. The article presents some basic knowledge and shows which application possibilities and added values machine learning can offer for production. Robotics, for example a bin-picking system, benefits in particular from the technologies described. Finally, the article deals with the topic of explainability of machine learning processes. For technical, legal and social reasons, decoding the “black box” is an essential task.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 15-18
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