AI tools for use in assembly automation and some examples of recent applications

David Sanders; Alexander Gegov

doi:10.1108/01445151311306717

AI tools for use in assembly automation and some examples of recent applications

David Sanders, Alexander Gegov

Research output: Contribution to journal › Article › peer-review

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Abstract

Purpose
– This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Design/methodology/approach
– Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Findings
– Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Research limitations/implications
– Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

Practical implications
– It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Social implications
– Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

Originality/value
– The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

Original language	English
Pages (from-to)	184 - 194
Journal	Assembly Automation
Volume	33
Issue number	2
DOIs	https://doi.org/10.1108/01445151311306717
Publication status	Published - 2013

Keywords

Flexible assembly, Small batch production, Industrial robotics, Vision, Flexible assembly systems, Assembly, Batch manufacturing, Production methods, Robotics

Access to Document

10.1108/01445151311306717

Revised paper - s1-ln11730955-1725437349-1939656818Hwf490884981IdV-16564952011730955PDF_HI0001[1]
This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://my.port.ac.uk/library/emeraldinsight/doi/abs/10.1108/01445151311306717. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited." - See more at: http://www.emeraldgrouppublishing.com/authors/writing/author_rights.htm#sthash.g8BsTutH.dpuf
Accepted author manuscript (Post-print), 647 KB

http://www.emeraldinsight.com/doi/abs/10.1108/01445151311306717

Using Artificial Intelligence for engineering applications
Sanders, D., Gegov, A. & Ndzi, D.
15/03/13 → 11/03/15
Project: Research

Most published author
Sanders, David (Recipient), 1 Jan 2022
Prize: Prize (including medals and awards)

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Cite this

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abstract = "Purpose– This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.Design/methodology/approach– Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.Findings– Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.Research limitations/implications– Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.Practical implications– It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.Social implications– Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.Originality/value– The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.",

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author = "David Sanders and Alexander Gegov",

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AU - Gegov, Alexander

N1 - This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://my.port.ac.uk/library/emeraldinsight/doi/abs/10.1108/01445151311306717. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited." - See more at: http://www.emeraldgrouppublishing.com/authors/writing/author_rights.htm#sthash.g8BsTutH.dpuf

PY - 2013

Y1 - 2013

N2 - Purpose– This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.Design/methodology/approach– Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.Findings– Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.Research limitations/implications– Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.Practical implications– It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.Social implications– Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.Originality/value– The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

AB - Purpose– This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.Design/methodology/approach– Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.Findings– Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.Research limitations/implications– Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.Practical implications– It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.Social implications– Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.Originality/value– The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

KW - Flexible assembly, Small batch production, Industrial robotics, Vision, Flexible assembly systems, Assembly, Batch manufacturing, Production methods, Robotics

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DO - 10.1108/01445151311306717

M3 - Article

SN - 0144-5154

VL - 33

SP - 184

EP - 194

JO - Assembly Automation

JF - Assembly Automation

IS - 2

ER -

AI tools for use in assembly automation and some examples of recent applications

Abstract

Keywords

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Projects

Using Artificial Intelligence for engineering applications

Prizes

Most published author

Cite this