quality control

AI Implementation in Industrial Quality Control

AI Implementation in Industrial Quality Control

A design science approach bridging technical and human factors
Erdi Ünal ORCID Icon, Kathrin Nauth ORCID Icon, Pavlos Rath-Manakidis, Jens Pöppelbuß ORCID Icon, Felix Hoenig, Christian Meske ORCID Icon
Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844/I4SE.26.1.112
AI Smart Workstation for Industrial Quality Control

AI Smart Workstation for Industrial Quality Control

Enhancing productivity through vision systems, real-time assistance, and Axiomatic Design
Leonardo Venturoso ORCID Icon, Simone Garbin ORCID Icon, Dieter Steiner, Dominik T. Matt
Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system’s practical benefits and its scalability.
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 128-134 | DOI 10.30844/I4SE.25.5.124
Digitization of Raster Drawings with Deep Learning

Digitization of Raster Drawings with Deep Learning

Framework outperforms OCR software in extracting data from mechanical drawings
Xiao Zhao, Marko Weber, Jan Schöffmann, Daniela Oelke ORCID Icon
A new look into the depths of technical drawings: A deep learning framework reads CAD drawings more accurately than ever before, recognizing geometrical dimensioning and tolerancing, dimensions, and every other detail. What used to be tedious manual labor is now carried out by an AI that understands the special features of every line and label. This promising technology not only increases accuracy but also speeds up the processing of drawings considerably. The system thus opens up new avenues for precision in production.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 10-17
Real-Time Quality Control

Real-Time Quality Control

Software systems for quality assurance for the processes of forming technology 4.0
Benjamin Lindemann, Nasser Jazdi, Michael Weyrich
Solid forming companies are always faced with the challenge of producing high-quality products that meet the strict requirements of the customers. The quality has to be reproducible despite fluctuations occurring along the value chain. In order to meet the requirements, solutions for an improved process stability and quality are needed. This paper presents a data-driven approach that aims to adapt quality fluctuations. Thus, process data is modeled in-memory in a multidimensional database. Based on the results of an online analytical processing, the process is controlled in real-time.
Industrie 4.0 Management | Volume 33 | 2017 | Edition 6 | Pages 20-24
Logistic Quality Control in Micro Forming

Logistic Quality Control in Micro Forming

Einsatz von Fuzzy-Regelung zur Optimierung von Stichprobenintervallen
Bernd Scholz-Reiter ORCID Icon, Michael Lütjen ORCID Icon, Dennis Lappe, Hendrik Thamer, Nele Brenner
Due to the increased product miniaturization, a number of new applications and market opportunities open up for mechanical micro-manufacturing. In the manufacturing process with part dimensions less than one millimeter and tolerances in the micrometer range occur so-called “size effects”. These prevent a simple scaling of processes known methods from the macro level and lead to an increased occurrence of quality deviations. In conclusion, the process capability according to ISO 21747 is affected and therefore the application of statistical process control (SPC) is more difficult. In this paper, the interaction between technical and logistical quality objectives in terms of logistical quality control are analyzed at the example of micro cold forming. Thereby, methods of statistical process control and fuzzy control are used.
Industrie Management | Volume 26 | 2010 | Edition 4 | Pages 13-16
Quality Gates – An Integrative Quality Control Approach

Quality Gates - An Integrative Quality Control Approach

Ein integrativer Ansatz des Qualitätscontrollings
Horst Wildemann
Against the background of complex and volatile value chains the success factor quality is gaining increasingly in importance. Today, product quality and process quality as well as their continuous improvement are the basis for entrepreneurial success. Thus quality controlling becomes a central function in order to assure competitiveness of companies. New approaches are in demand in order to fulfil quality requirements along value chains and in order to link quality management systems with quality controlling. By an inter-divisional and company-wide implementation of Quality Gates these requirements can be met.
Industrie Management | Volume 26 | 2010 | Edition 4 | Pages 33-35
Surface Inspection of Micro Parts

Surface Inspection of Micro Parts

Bernd Scholz-Reiter ORCID Icon, Michael Lütjen ORCID Icon, Hendrik Thamer
Due to the increasing miniaturization in all areas, mechanical manufacturing processes of micro-components become more important. The combination of high production cycles and low manu-facturing tolerances in the micrometer range requires a comprehensive quality management, which seeks efficient processes with low quality costs. Because of the small component sizes and the difficulties associated with the handling process, the classic visual inspection retires as testing procedure. In combination with a customized micro-manufacturing surface metrology, an efficient image processing system is needed to identify surface imperfections such as cracks, dents and scratches. This paper presents different standards and applications on the basis of quality management that affect the surface inspection of micro components. By reference to a micro-thermoformed component from the Collaborative Research Centre (CRC) 747, the prototypical implementation of an automated image processing system is ...
Industrie Management | Volume 26 | 2010 | Edition 3 | Pages 43-46