Optimizing industrial transport with a connected automated vehicle demonstrator for assembly systems and end-of-line production.

In recent years, the automotive industry has witnessed significant progress in the development of automated driving technologies. The integration of advanced sensors and systems in vehicles has led to the emergence of various functionalities, such as driving assistance and autonomous driving. Applying these technologies on the assembly line can enhance the efficiency, safety, and speed of transportation, especially at end-of-line production. This work presents a connected automated vehicle (CAV) demonstrator for generating autonomous driving systems and services for the automotive industry. Our prototype electric vehicle is equipped with state-of-the-art sensors and systems for perception, localization, navigation, and control. We tested various algorithms and tools for transforming the vehicle into a self-driving platform, and the prototype was simulated and tested in an industrial environment as proof of concept for integration into assembly systems and end-of-line transport. Our results show the successful integration of self-driving vehicle platforms in the automotive industry, particularly in factory halls. We demonstrate the localization, navigation, and communication capabilities of our prototype in a demo area. This work anticipates a significant increase in efficiency and operating cost reduction in vehicle manufacturing, despite challenges such as current low traveling speeds and high equipment costs. Ongoing research aims to enhance safety for higher vehicle speeds, making it a more viable business case for manufacturers, considering the increasing standardization of automated driving equipment in cars. The main contribution of this paper lies in introducing the general concept architecture of the integration of automated driving functionalities in end-of-line assembly and production systems. Showing a case study of the effective development and implementation of such functionalities with a CAV demonstrator in a more standardized industrial operational design domain.

Human trust in otherware - a systematic literature review bringing all antecedents together.

Technological systems are becoming increasingly smarter, which causes a shift in the way they are seen: from tools used to execute specific tasks to social counterparts with whom to cooperate. To ensure that these interactions are successful, trust has proven to be the most important driver. We conducted an extensive and structured review with the goal to reveal all previously researched antecedents influencing the human trust in technology-based counterparts. In doing so, we synthesised 179 papers and uncovered 479 trust antecedents. We assigned these antecedents to four main groups. Three of them have been explored before: environment, trustee, and trustor. Within this paper, we argue for a fourth group, the interaction. This quadripartition allows the inclusion of antecedents that were not considered previously. Moreover, we critically question the practice of uncovering more and more trust antecedents, which already led to an opaque plethora and thus becomes increasingly complex for practitioners.Practitioner summary: Future designers of intelligent and interactive technology will have to consider trust to a greater extent. We emphasise that there are far more trust antecedents - and interdependencies - to consider than the ethically motivated discussions about "Trustworthy AI" suggest. For this purpose, we derived a trust map as a sound basis.

Trust in artificial intelligence within production management - an exploration of antecedents.

Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems. Practitioner summary: In the future, artificial intelligence will assist cognitive tasks in production management. In order to make good decisions, humans trust in AI has to be well calibrated. For trustful human-AI interactions, it is beneficial that humans subjectively perceive AI as capable and comprehensible and that they themselves are digitally competent.

Digital press shops: Data of an online survey among press shop experts.

Modern automotive press shops are reaching their process limits due to increasing demands on car body shapes. At the same time, transmission of information and readjustment in the event of quality losses because of process errors is still largely controlled manually. The survey presented here, deals with better connected processes as well as data acquisition, and track and trace applications in press shops. The survey was directed to experts from the automotive industry and is to determine how automated and connected the processes in press shops already are. The survey was conducted from March till April 2020. With a total of 24 questions, an attempt is made to gain a comprehensive picture of the current status and the existing potential regarding smart press shops. In addition to questions on the marking and tracking of pressed parts, the objective is to find out which process data is already being recorded today and what conclusions can be drawn from it regarding the expected part quality. The evaluation of the survey is intended to build the basis for research activities on smart, connected press shops.

Survey based dataset on automation decisions for assembly systems in Germany.

The data provided in the present article provides information on the importance of a list of monetary and non-monetary influencing factors on decisions regarding the automation of an assembly system. A survey among German industry representatives conducted between July 2018 and October 2018 is the basis for this dataset. It contains the characteristics of industrial companies based in Germany that participated in the survey as well as their attitude towards the development of the automation level in assembly systems. The focus of the survey lies on the influencing factors of the production and production environment and their influence on the automation level. The participants were able to evaluate the factors on a six-step ordinal scale from "no influence" to "very strong influence". Interpretation of this data can be found in the research article titled "Automation decisions in flow-line assembly systems based on a cost-benefit analysis" [1].

A clustering approach for topic filtering within systematic literature reviews.

Within a systematic literature review (SLR), researchers are confronted with vast amounts of articles from scientific databases, which have to be manually evaluated regarding their relevance for a certain field of observation. The evaluation and filtering phase of prevalent SLR methodologies is therefore time consuming and hardly expressible to the intended audience. The proposed method applies natural language processing (NLP) on article meta data and a k-means clustering algorithm to automatically convert large article corpora into a distribution of focal topics. This allows efficient filtering as well as objectifying the process through the discussion of the clustering results. Beyond that, it allows to quickly identify scientific communities and therefore provides an iterative perspective for the so far linear SLR methodology.•NLP and k-means clustering to filter large article corpora during systematic literature reviews.•Automated clustering allows filtering very efficiently as well as effectively compared to manual selection.•Presentation and discussion of the clustering results helps to objectify the nontransparent filtering step in systematic literature reviews.

Data on the current state of modular systems in a highly dynamic environment: Empirical analyses in the manufacturing industry and automotive industry of Germany.

Today, many companies develop modular systems to realise considerable product differentiation and variation while simultaneously reducing costs through economies of scale [1]. Designing these modular systems to be lasting and robust in a highly dynamic environment [2], avoiding subsequent modification cost [3,4] and staying innovative over the product lifecycle is crucial for sustainable success in an increasingly competitive market [5]. Two closely related surveys were carried out on the initial situation described above. The first survey deals with the major conflict between planning reliability and flexibility with regard to future boundary conditions that the described challenges lead to. The data presented in this article on the first survey was collected from German companies, mainly from the automotive industry, developing and manufacturing complex products using modular systems. Other represented companies operate in the machine and plant construction industry. The survey comprises the answers of 39 participants, gathered via online questionnaire. The 17 questions of the survey are divided into two topics: characteristics of the participants, and the problem description of modular systems in highly dynamic environments and the management thereof. The second survey deals with the anticipation of innovations in the design of modular systems and the selection of the right choice from a variety of possible innovations to be considered. The data presented in this article on the second survey was collected from German automotive manufacturers, which develop and manufacture complex products using modular systems. The survey comprises the answers of 501 participants, gathered via online questionnaire. The 14 questions of the survey are divided into two topics: characteristics of the participants, and the problem description of modular systems and the management thereof. The data obtained allows the identification of existing deficits and dedicated research on solutions.

Data on the current state of problem solving and improvement during physical product development within complex (manufacturing) systems.

Due to the exponential increase of failure cost during the product development process, problems have to be effectively remedied as early as possible and with shortened innovation cycles, increasingly efficient. For the manufacturing of complex products at low maturity levels (referred to as physical product development), nonconformance problem solving constitutes a major difficulty in this regard (Camarillo et al., 2017; Walter et al., 2010). The data presented in this article was collected from German companies, differing in size and industry sector, manufacturing highly complex products at low maturity. Selected and consulted companies therefore operate in (or comparable to) the automotive prototyping, air and space, shipbuilding, special machinery or electronics domain. The survey comprises the answers of 46 participants, gathered via online questionnaire. It subdivides into 18 questions covering the companies' characteristics, knowledge management and documentation systems within the product development process, as well as the appraisal of technological potentials. The obtained data gives an insight into the industrial status quo of nonconformance problem solving. The data allows to derive existing deficits and dedicated research on solutions.