Open Access

The lack of appropriate decision support tools is a major challenge in the industrial environment where the selection of high-precision indoor localization technologies (ILT) is crucial. Companies often face difficulties analyzing the advantages and disadvantages of different indoor localization technologies due to the wide range of selection criteria involved. These criteria include accuracy, coverage area, power consumption, cost, scalability, response time, and robustness, which can lead to uncertainty and sub-optimal investment in case of an inappropriate decision. This research project aims to tackle this challenge by developing a tool that supports users from the industry to easily select the most suitable Indoor Localization Technology. The developed tool serves to be a systematic and well-founded solution. It also allows the consideration of the subjective evaluations of the decision-makers in the decision-making process. The tool represents an industry-specific solution designed to meet the requirements of ILT selection. Additionally, the tool features a clear navigation system that guides users through the selection process. By providing transparent insights into the Analytic Hierarchy Process (AHP) calculation method, the tool enables companies to make informed decisions. The project involves a systematic literature review on technology selection processes and Indoor Localization Technologies, as well as the development of a Decision Support System.

Mobile robots, particularly autonomous mobile robots (AMRs), play a transformative role in Intralogistics 4.0, enabling automated material transport and handling tasks. The increasing changeability of plant layouts and the dynamic manufacturing environment are critical drivers for their use. While automated guided vehicles (AGVs) are centrally controlled and rely on supporting infrastructure, AMRs can navigate without such guidance, thanks to their onboard sensors. Many studies have delved into the reasons and effects of using AMRs in intralogistics, underscoring their potential to optimise processes and enhance efficiency. This study aims to review existing literature on AMRs in intralogistics, unearthing the latest advancements and existing limitations. As a result, this review identifies five main research subjects: complexity of the environment, safety, resource scheduling and power consumption, artificial intelligence algorithms and interoperability. The study concludes by summarising these fields and emphasising the existing limitations and research gaps for further innovations in this area.

This paper proposes a novel framework - "Transparent Reasoning in Artificial intelligence Cause Explanation" (TRACE) - that combines root cause analysis, explainable artificial intelligence, and machine learning in a comprehensible manner for the shopfloor worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. A human AI collaboration tool in perspective. The paper outlines the need of such a framework, describes the proposed design science approach for the development.

Reliable lot size one capable online quality monitoring for CNC machined parts remains elusive. To address this challenge, the proposed approach aims to bridge the current gap in research by developing a cost-effective and reference-independent monitoring concept for material defect detection in CNC-machined parts. This paper presents a novel digital twin-based method, utilising machining vibrations and a g-code-based encoding of the cutting process. The objective is to detect material defects, such as blowholes, without the need for individual workpiece references. The proposed method aims to reduce barriers to entry, minimise waste, and enhance machine productivity by enabling automated early online quality control. To develop and validate the model, a dataset combining machining vibration with technological context data such as chip-shape is generated. The feasibility and potential of the approach is demonstrated in a job shop setting on a 3-axis CNC mill.

Deterioration modeling plays a pivotal role in various industries, enabling predictive maintenance strategies and cost-effective resource allocation. Furthermore, with an escalating influx of data across multiple domains, opportunities for predictive analysis continue to expand. The development of wear models becomes a more and more complex process especially when acquiring and handling large amounts of customer data of different products and stakeholders since additional topics such as data privacy have to be included. Therefore, the development process causes high efforts in time, capacity and coordination between the different disciplines such as domain experts, data scientists and IT-administration. This paper presents a reference model for predictive maintenance model development. Based on adopted best practice approaches from the industrial production context, the reference model is structured around the four phases of the CRISP-DM model. Altogether it encompasses 48 defined steps. Covering the whole life cycle, beginning with component identification, use case description and culminating in model deployment and maintenance, each step is meticulously crafted to ensure quality and speed up the predictive maintenance model development. By adhering to this systematic approach, wear models for components can be developed with confidence, with lower effort and costs and mitigating uncertainties. The reference model is validated in the automotive industry since there already exist large amounts of fleet data of different products and customer. By providing a reliable and systematic approach to predictive maintenance model development, this reference model empowers stakeholders to optimize maintenance schedules, reduce downtime, and enhance overall operational efficiency.