Worker assistance systems in manufacturing: A review of the state of the art and future directions

Mark, Benedikt G.; Rauch, Erwin; Matt, Dominik T.

doi:10.1016/j.jmsy.2021.02.017

Cited by 82 publications

(35 citation statements)

References 43 publications

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“…A summary of the most recent articles in the field of assistance systems, both technologyand impact-wise, is presented below. A comprehensive literature review (121 papers) on worker assistance systems used in the shop floor (i.e., in-situ), including the state-of-the-art, possible future directions, and the limitations of these systems, can be found in [8]. It reveals that although in its infancy, the research field of worker assistance has gained more attention with more industrial adoption, needing to develop more assistance applications that can help operators at their workstations on the shop floor, with a focus on user interface design and biomedical engineering as well.…”

Section: Related Workmentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

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Section: Related Workmentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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“…The main purpose of assistance systems is to extend the capabilities of human operators in different aspects to achieve an individual or organizational goal faster, with fewer errors, or more secure. Assistance systems thereby enhance perceptual (visual, tactile, hearing), decision-making (information-presentation, pre-processing), and activity (range extension, strength amplification) capabilities (Mark, Rauch & Matt, 2021).…”

Section: Taxonomy Of Assistance Systemsmentioning

confidence: 99%

“…While human muscular power and coordination are limited, systems can amplify power e.g., through exoskeletons or limit and dose human power in sensitive areas. Assistance systems in this area are closely associated with human movements and need to capture and translate (Mark, Rauch & Matt, 2021).…”

Section: Classification By Interaction Stagementioning

confidence: 99%

Assistance systems in learning factories - A systematizing overview and case studies

Thim¹,

Vladova²,

Lass³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

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Assistance systems are in use in different domains from their application in everyday life like driving cars and guiding the operation of information systems to industrial usage, e.g., in operating machinery, maintaining facilities, and monitoring production processes. The primary purpose of assistance systems is to extend the capabilities of human operators in different aspects to achieve an individual or organizational goal faster, with fewer errors, or more secure. In the context of learning, they provide new means to engage people in realistic learning scenarios. This paper discusses assistance systems that support learning in production processes. The goal of the paper is to structure the possibilities of assistance systems use regarding different learning goals. It presents a taxonomy of assistance system use and demonstrates this taxonomy in three cases.

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“…Over the last decades, the notion of sustainability has come to be of indisputable social relevance [1], also in manufacturing [2]. Here, the fourth Industrial revolution (I4.0) basic concept is the integration of advanced technologies into the working environment and operations.…”

Section: Introductionmentioning

confidence: 99%

New and Emerging Hazards for Health and Safety within Digitalized Manufacturing Systems

et al. 2021

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The Fourth Industrial Revolution is radically reshaping the procedures and the manufacturing environments through the digitalization process. The digitalization process can change according to the context and to specific solutions, and it is able to modify manufacturing systems and production areas. All the employees are directly affected by the transformation of the working environment, manufacturing tools, and working conditions and by the increasing need for new competencies. In this context, it is crucial to identify new and emerging hazards concerning the health and safety of the employees to ensure a conscious and safe digital transformation for everyone involved. In this regard, the paper presents the state of the research and defines seven areas of interest for a safe and harmless digital transformation for the employees, drawing attention to the hazards in the different technological areas. The state of the research unveils the absence of detailed analysis to identify specific hazards of 4.0 technologies. Therefore, every specific 4.0 technologies is analyzed by an extensive review to provide a comprehensive matrix of new and emerging hazards for health and safety within digitalized manufacturing systems. The results can help manufacturing organizations to perform robust risk assessments for worker when introducing specific 4.0 technologies.

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Worker assistance systems in manufacturing: A review of the state of the art and future directions

Cited by 82 publications

References 43 publications

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Assistance systems in learning factories - A systematizing overview and case studies

New and Emerging Hazards for Health and Safety within Digitalized Manufacturing Systems

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