Functionalities and Implementation of Future Informational Assistance Systems for Manual Assembly

Petzoldt, Christoph; Keiser, Dennis D.; Beinke, Thies; Freitag, Michael

doi:10.1007/978-3-030-64351-5_7

Cited by 21 publications

(7 citation statements)

References 37 publications

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“…The general implications and dependencies are discussed regarding a case study which uses a pick-by-light and gamification approach within the assistance system. In [ 30 ], Petzoldt et al detailed a first implementation for assembly assistance, with individual instructions also having a motivational incentive approach.…”

Section: Related Workmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

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Section: Related Workmentioning

confidence: 99%

Assembly Assistance System with Decision Trees and Ensemble Learning

Sorostinean

Gellért

Pirvu

2021

Sensors

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“…In [16], the authors analyzed the technology that an assistive system should encompass. They started by analyzing the existing assembly assistance systems and how they can optimize those systems.…”

Section: Related Workmentioning

confidence: 99%

Towards an Assembly Support System with Dynamic Bayesian Network

et al. 2022

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Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.

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“…Figure 1 depicts examples of such systems: (1, 2) show cognitive assistance systems based on a smartphone (1) and data glasses (2), which display context-dependent Augmented Reality (AR) virtualizations for assembly and maintenance support based on image recognition methods (Quandt et al, 2020;Stern et al, 2021). (3) shows a collaborative robot system that supports the unloading process of containers by autonomously performing unloading operations under human supervision (Petzoldt et al, 2020;Rolfs et al, 2020).…”

Section: Human Operators In Cppsmentioning

confidence: 99%

Human-Centered Design of Hybrid Cyber-Physical Production Systems - Use of Human Autonomy Teaming as a Future Way of Working

Stern¹,

Freitag²

2022

Digitization of the Work Environment for Sustainable Production

Self Cite

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Many production processes are changing towards hybrid CPPS, in which humans work together with intelligent as well as automated or autonomously acting systems. This enables economic advantages through more efficient processes, error reduction, and quality improvements. It further contributes to a sustainable production, which preserves resources and enhances operators' well-being. To achieve these advantages, an adequate system design is required. Here, various design approaches are available, such as the creation of active involvement of people and adequate situational awareness, and the consideration of both operators and automation as equal and independent actors. But how do they affect its performance and the operators in detail to achieve both economic and human-centered goals? We present the results of a first literature review on the interaction of humans and autonomous systems in production systems and the associated allocation of work (Human Autonomy Teaming, HAT) and open research problems, respectively. Finally, we propose and discuss preliminary ideas and starting points to solve these design issues as a basis for future research work.

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Functionalities and Implementation of Future Informational Assistance Systems for Manual Assembly

Cited by 21 publications

References 37 publications

Assembly Assistance System with Decision Trees and Ensemble Learning

Assembly Assistance System with Decision Trees and Ensemble Learning

Towards an Assembly Support System with Dynamic Bayesian Network

Human-Centered Design of Hybrid Cyber-Physical Production Systems - Use of Human Autonomy Teaming as a Future Way of Working

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