Hybrid Artificial Intelligence System for the Design of Highly-Automated Production Systems

Hagemann, Simon; Sünnetcioglu, Atakan; Stark, Rainer

doi:10.1016/j.promfg.2018.12.026

Cited by 24 publications

(8 citation statements)

References 8 publications

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“…(i) assembly systems of divergent (nonsequential) structures (ii) station design: a. multiple robots per station (for handling and joining) b. geometry stations and respot stations (iii) product variants (iv) joining process design: a. different joining technologies b. fixture-and stationary-based joining (v) handling process design: a. station loading b. part collection c. unloading It has been shown that none of the here presented scenarios equal the RASD problem tackled in this paper. Hence, in previous studies of (Hagemann und Stark 2018), (Hagemann et al 2019) existing methods of ruled-based systems, artificial intelligence (AI) and mathematical optimization have been analyzed and evaluated. The evaluation has revealed that discrete mathematical optimization techniques, as already applied for numerous SALB, RALB and RALD problems, suit the RASD problem best due to the generation of optimal solutions.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

“…The optimality and the lack of transparency of AI systems or even the feasibility of their results cannot be ensured. (Hagemann et al 2019) (Damaševičius 2008) Hence, in this paper, an algorithm, which covers all of the above-listed requirements (i-v), is introduced. A discrete optimization formalization will be applied which automatically generates solutions for robotic assembly systems, specialized on the automotive body-in-white production stages.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

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An optimal algorithm for the robotic assembly system design problem: An industrial case study

Hagemann

Stark

2020

CIRP Journal of Manufacturing Science and Technology

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The design process of flow-oriented assembly systems is characterized by being both highly complex and time consuming. Especially those design processes categorized into robotic and multi variant encountered in the automotive body-in-white production stages. Unlike established manual and template-based assembly system design models, which are currently applied in industry, the here presented novel approach uses a knowledge-based search algorithm and automatically generates optimal assembly system configurations. The algorithm has been implemented in a software prototype and the results have been validated against different largesize industrial scenarios in the automotive field of body-in-white production.

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Section: Literature Review and Research Gapmentioning

confidence: 99%

Section: Literature Review and Research Gapmentioning

confidence: 99%

An optimal algorithm for the robotic assembly system design problem: An industrial case study

Hagemann

Stark

2020

CIRP Journal of Manufacturing Science and Technology

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“…Different to what is found in the literature for systems that manage and improve the performance of production lines, the self-learning capabilities that could be provided by the AI techniques have not been fully deployed in systems for decisiontaking among different processes. In this sense, currently, the common use widely seen is the so-called hybrid intelligence learning use: the DSS is capable to produce a ranking or a statement on costs or other attributes [30]. However, the final decision-taking on the process and the follow-up and accumulation of new experiences still rely heavily on human operators.…”

Section: Formal Models and Trends Of Dss Applied To Manufacturing Promentioning

confidence: 99%

Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels

Minguella-Canela¹,

Buj-Corral²

2020

New Trends in the Use of Artificial Intelligence for the Industry 4.0

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Digital manufacturing has opened a new window in the way to approach the manufacture of parts. The possible switch from manufacturing and holding physical stock to manoeuvring with a fully-digital one is promising but still has not been undertaken-or only in a small proportion-by the majority of the manufacturing companies. What are the cost and productivity frontiers that halt the transformation taking place so far? When does it make sense, in terms of production volume and costs, to undertake this transformation? What level of savings could be achieved and what investments would be favourable? The base line of the present chapter is to depict quantitative tools to address the potential impact of endeavouring digital transformation in manufacturing environments, considering costing and production variables, as well as technological decision-making parameters. Keeping the modelling of the demand very basic, some exploration on the degree of postponement of the production is discussed. Also, decision support systems (DSSs) for manufacturing selection are reviewed. Finally, a case study serves to apply the mathematical framework presented and to quantify the results in a realistic industrial case. Using this case, the chapter outlines and describes how to apply artificial intelligence (AI) techniques to implement the DSSs.

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“…Simon Hagemann [7] presented a novel AI methodology capable of generating initial production configurations base. He concluded that to ensure high efficiency in the automotive industry AI techniques are vital.…”

Section: Literature Reviewmentioning

confidence: 99%

Transforming Manufacturing Processes With the Help of Ai

Aggarwal¹,

Garg²,

Budhaulia³

2020

International Journal of Mechanical Engineering and Technology (Ijmet)

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In manufacturing businesses, productivity and cost savings are two important parameters. Technology ease out the load, increase efficiency and mitigate the financial pressure. Modern manufacturing is characterized by high level of automation and technology-driven processes. This digitization of manufacturing has given rise to Industry 4.0where cyber-systems, connectivity and data are the key. In this paper we attempt to highlight how each step in the manufacturing process can be changed and made more efficient with the help of technology. To reinforce this fact, examples of various companies have been mentioned which have transformed their supply chains with the help of technology. To better understand this transforming phase in manufacturing industry, we analyzed the role of artificial intelligence in industries, the major issue in implementation of artificial intelligence in manufacturing units, possible ways to overcome these issues and what are the different tasks that can be handled easily with implementation of AI in manufacturing units. With the help of this paper, the reader will get a holistic view of the transforming manufacturing trend and its associated applications in the real world. Certain challenges in the current fourth industrial revolution of manufacturing have also been identified upon which actions can be taken to further improve productivity, cost savings and efficiency.

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Hybrid Artificial Intelligence System for the Design of Highly-Automated Production Systems

Cited by 24 publications

References 8 publications

An optimal algorithm for the robotic assembly system design problem: An industrial case study

An optimal algorithm for the robotic assembly system design problem: An industrial case study

Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels

Transforming Manufacturing Processes With the Help of Ai

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