AI tools for use in assembly automation and some examples of recent applications
Research output: Contribution to journal › Article › peer-review
– 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.
– Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.
– 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.
– 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.
– 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.
– Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.
– 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.
|Pages (from-to)||184 - 194|
|Publication status||Published - 2013|
- Revised paper - s1-ln11730955-1725437349-1939656818Hwf490884981IdV-16564952011730955PDF_HI0001
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