Enabling manual assembly and integration of aerospace structures for Industry 4.0 - methods

Grendel, Heiko; Larek, Roland; Riedel, Felix; Wagner, Jan

doi:10.1016/j.promfg.2017.11.004

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…In one example: researchers have developed the integration of guidance lights and projections within the workplace environment to instruct the worker [1,32]. Other approaches focus on the integration of screens or wearables to support the worker in manual production processes [10] or on the interaction with CPS in general [29]. The integration of robots has aimed to reduce the strain of the workload [18,19].…”

Section: State Of the Researchmentioning

confidence: 99%

Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration With a Robot Within the Self-Adapting-Production-Planning-System

Ender¹,

Wagner²,

Kunert³

et al. 2019

IAPGOS

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For some time, the focus of past research on industrial workplace designs has been the optimization of processes from the technological point of view. Since human workers have to work within this environment the design process must regard Human Factor needs. The operators are under additional stress due to the range of high dynamic processes and due to the integration of robots and autonomous operating machines. There have been few studies on how Human Factors influence the design of workplaces for Human-Robot Collaboration (HRC). Furthermore, a comprehensive, systematic and human-centred design solution for industrial workplaces particularly considering Human Factor needs within HRC is widely uncertain and a specific application with reference to production workplaces is missing. The research findings described in this paper aim the optimization of workplaces for manual production and maintenance processes with respect to the workers within HRC. In order to increase the acceptance of integration of human-robot teams, the concept of the Assisting-Industrial-Workplace-System (AIWS) was developed. As a flexible hybrid cell for HRC integrated into a Self-Adapting-Production-Planning-System (SAPPS) assists the worker while interaction.

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Section: State Of the Researchmentioning

confidence: 99%

Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration With a Robot Within the Self-Adapting-Production-Planning-System

Ender¹,

Wagner²,

Kunert³

et al. 2019

IAPGOS

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“…On the other hand, especially the high-tech industry, such as the production of air-and spacecrafts and other special machinery lacks of these factors. In comparison small batch sizes and complex assembly sequences hinder automation and manual work is the most important factor for value adding [2]. Therefore, production planning systems that are used for coordinating the manual work are seen as a platform for pushing digitalization [3].…”

Section: Introductionmentioning

confidence: 99%

The concept of a maximal network plan to enhance automated decision making in production steering

Larek

Wagner

Grendel

et al. 2022

Prod. Eng. Res. Devel.

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Digitalization is a major driver of competitiveness in industry. Especially, automated scheduling systems are broadly available to deal with production steering in complex production systems. Their underlying concept is the network plan to describe the logical relation between different production steps for the completion of a production process. The network plan, however, is limited to contain only one standard process and allows therefore basically only the automated variation of execution time of each step within the given logical sequence. This is sufficient as long as the production process and the boundary conditions, such as resource availability, are stable to a certain degree. This work introduces an enhancement of the concept of the network plan, the Maximal Network Plan, that is designed to automatically cope with disruptions. It stores additional alternative sequences, weighted according to their priority in comparison to the standard process. Therefore, a numerical decision making is possible in terms of also varying the order of production tasks to achieve a maximum performance for the overall process. Instead of waiting for a disruption in the standard process to be solved, an alternative sequence will be proposed to increase overall progress by circumventing the blocked step. This concept has been developed and tested for complex assembly tasks, such as the production of air- and spacecraft structures and systems. In conjunction with the use of mobile computing devices instead of paper-based work order documents it allows a very fast and dynamic production steering, also in the case of substantial disruptions in the standard process.

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“…There are studies of digitalization which helpfully use these sectors as examples (e.g. Grendel et al (2017) point out that digital technologies are more immediately useful in highly automated sectors (like automotive) rather than in sectors which involve significant levels of manual assembly to create very large structures (like aerospace); see also Theorin et al (2017)). Similarly, Ceruti et al (2019) look at the potential uses of additive manufacturing and AR in aerospace MRO.…”

Section: Introductionmentioning

confidence: 99%

The impact of Industry 4.0 on supply chains and regions: innovation in the aerospace and automotive industries

Hickie

2021

European Planning Studies

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Enabling manual assembly and integration of aerospace structures for Industry 4.0 - methods

Cited by 6 publications

References 5 publications

Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration With a Robot Within the Self-Adapting-Production-Planning-System

Concept of a Self-Learning Workplace Cell for Worker Assistance While Collaboration With a Robot Within the Self-Adapting-Production-Planning-System

The concept of a maximal network plan to enhance automated decision making in production steering

The impact of Industry 4.0 on supply chains and regions: innovation in the aerospace and automotive industries

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