Autor: Lukas Schulte

Enabler for the Digital Twin

Enabler for the Digital Twin

Requirements for Technical Documentation 4.0
Christian Koch, Lukas Schulte, René Wöstmann, Jochen Deuse ORCID Icon
The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 76-85
Interactive 8D as Application for Sustainable Problem Solving

Interactive 8D as Application for Sustainable Problem Solving

A Knowledge-based IT Assistance for Structured 8D Problem Solving in the Automotive Industry
Martin Kempel, Ralph Richter, Jochen Deuse ORCID Icon, Lukas Schulte
n the automotive industry, preventive quality actions are applied to ensure the quality of the end products. During production ramp-up the occurrence of nonconformities can be a critical issue. Nonconformities with new and innovative products can be especially challenging due to limited experience of previously unknown processes. To address this challenge, an IT application has been developed to capture the organization's existing knowledge and use this to support the problem- solving team in applying an enhanced 8D method.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 5 | Pages 35-39
Transdisciplinary competence development for role models in data-driven value creation – The Citizen Data Scientist in the Centre of Industrial Data Science Teams

Transdisciplinary competence development for role models in data-driven value creation - The Citizen Data Scientist in the Centre of Industrial Data Science Teams

Univ.-Prof. Dr.-Ing. Jochen Deuse, Thorben Panusch, Lukas Schulte, René Wöstmann
Increasing digitalisation is fundamentally changing the understanding and possi-bilities of value creation as well as labour organisation. The systematic collection, storage and analysis of data is becoming a decisive competitive factor and is the basis for intelligent products, processes and production technology. This results in new competence requirements and roles in mechanical and plant engineering and in the manufacturing industry in general. Machine Learning in particular, as the basis of Artificial Intelligence, poses great challenges for companies, as the demand for experts, so-called Data Scientists, significantly exceeds the offer and furthermore, these experts rarely have the required domain knowledge - the core competences of manufacturing companies. In this context, the new job descrip-tion of the Citizen Data Scientist as a link between the most important disci-plines of information technology, domain knowledge and data science enters the focus of attention. The article ...
Industry 4.0 Science | 2021 | | DOI 10.30844/wgab_2021_3
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