Bio-inspired computing algorithms are emerging approaches that are based on the principles and vision of the biological evolution of nature to implement new and robust competing techniques. Recently, bio-inspired algorithms have been identified in machine learning to find the optimal solutions of problems in production processes. In this framework, swarm intelligence, which is a subfield of artificial intelligence concerning the intelligent actions of biological swarms by the relationship of individuals in such environments, is used to solve problems in the world by simulating such biological behaviours. Swarm intelligence is defined as the development of intelligent algorithms that mimick the behaviour of different animal societies. In particular, the Bees Algorithm displays the foraging behaviour of honeybees to solve optimisation and search problems. The algorithm performs a sort-of exploitative neighbourhood search combined with random explorative search. This chapter describes the use of the Bees Algorithm in its basic formulation for tool wear identification and measurement during turning operations.

Bees Algorithm Models for the Identification and Measurement of Tool Wear

D'Addona D. M.
Primo
2022

Abstract

Bio-inspired computing algorithms are emerging approaches that are based on the principles and vision of the biological evolution of nature to implement new and robust competing techniques. Recently, bio-inspired algorithms have been identified in machine learning to find the optimal solutions of problems in production processes. In this framework, swarm intelligence, which is a subfield of artificial intelligence concerning the intelligent actions of biological swarms by the relationship of individuals in such environments, is used to solve problems in the world by simulating such biological behaviours. Swarm intelligence is defined as the development of intelligent algorithms that mimick the behaviour of different animal societies. In particular, the Bees Algorithm displays the foraging behaviour of honeybees to solve optimisation and search problems. The algorithm performs a sort-of exploitative neighbourhood search combined with random explorative search. This chapter describes the use of the Bees Algorithm in its basic formulation for tool wear identification and measurement during turning operations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/902221
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