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The reliability and accuracy of finite element models of metal machining are heavily reliant on the underlying material flow stress models. The aim of this study is to characterize the relation between the flow stress models and the chip morphology to provide a deeper insight into the process of serrated chip formation. Firstly, in the context of a flow stress model, the critical conditions for generation of the serrated chips are theoretically analyzed. Then, simulations are performed using two different constitutive models with five parameters sets, and the results are discussed in relation to how they verify the theoretical results. In order to better understand the process of serrated chip formation, attention is concentrated on the critical steps characterizing the formation of a single chip segment. The cutting process parameters (stress, strain, and temperature) are discussed. In addition, the mechanism by which the chip morphology transforms from continuous to serrated with increasing cutting speed is investigated in terms of the variation of flow stress curves. The results show that the slope of decrease and the strain value at the peak point of flow stress curves both greatly affect chip morphology. That is, the slope of decrease of the flow stress curve largely controls the formation of serrated chips, while the strain point at the peak determines the frequency and degree of serration of chips. It is found that simulations by manipulating well the flow stress models can produce results for chip morphology, cutting forces, etc. that are closer to those obtained experimentally. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have gained wide research and applications in production scheduling fields, but the efficiency and effectiveness of a GA significantly depend on its parameters and operators. In contrast to the rich research on determination of optimal and adaptive parameters, little research has been done on determining optimal combination of genetic operators. Different from the traditional way by trial and error, this paper presents a novel and systematical approach based on ordinal optimisation (OO) and optimal computing budget allocation (OCBA) technique to determine optimal combination of genetic operators for flow shop scheduling problems. Simulation results show that the proposed methodology is able to determine optimal combination of genetic operators and simultaneously to provide a good solution with reasonable performance evaluation for scheduling problem. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are a class of effective parallel searching algorithms inspired by the idea of “survival of the fittest”, which has been successfully applied to a variety of problems, especially in the fields of manufacturing and scheduling. However, it is reported that traditional GAs often suffer from the weaknesses of premature convergence as well as parameter and operator dependence. So far, many improved methods with adaptive parameters or hybrid structures have been proposed, but there is little literature considering the adaptive control of genetic operators. In this paper, an adaptive GA (AGA) with multiple operators is proposed for flowshop scheduling, which is a typical NP-hard optimisation problem with many industrial applications and has been widely studied in both academic and engineering fields. In AGA, multiple different genetic operators are employed in an adaptive hybrid way to enhance the exploration and exploitation abilities so as to prevent premature convergence and achieve superior performance. It especially important to stress that the utilising ratio of each operator for hybridisation is adaptively and dynamically controlled during the evolutionary searching process. Simulation results based on benchmarks demonstrate the effectiveness of AGA by contrast with traditional GAs. And the effect of the adaptive control of the operator and the effects of some parameters on the optimisation performance are discussed as well. [ABSTRACT FROM AUTHOR]