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a b s t r a c tReliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring.

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Multi-objective Evolutionary Algorithms evolve a population of solutions through successive generations towards the Pareto-optimal Front. One of the most critical questions faced by the researchers and practitioners in this domain, relates to the number of generations that may be sufficient for an algorithm to offer a good approximation of the Pareto-optimal Front, for a given problem. Ironically, till date this question largely remains unanswered and the number of generations are arbitrarily fixed a priori, with potentially punitive implications. If the a priori fixed generations are insufficient, then the algorithm reports suboptimal solutions. In contrast, if the a priori fixed generations are far-too-many, it implies waste of computational resources. This paper proposes a novel entropy based dissimilarity measure that helps identify on-the-fly the number of generations beyond which an algorithm stabilizes, implying that either a good approximation has been obtained, or that it can not be obtained due to the stagnation of the algorithm in the search space. Given that, in either case no further improvement in the approximation can be obtained, despite additional computational expense, the proposed dissimilarity measure provides a termination criterion and facilitates a termination detection algorithm. The generality, on-the-fly implementation, low computational complexity, and the demonstrated efficacy of the proposed termination detection algorithm, on a wide range of multi-and many-objective test problems, define the novel contribution of this paper.