Application of soft computing techniques in machining performance prediction and optimization: a literature review

Chandrasekaran, M.; Muralidhar, M.; Krishna, C. M.; Dixit, Uday S.

doi:10.1007/s00170-009-2104-x

Cited by 302 publications

(134 citation statements)

References 122 publications

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“…The use of neural networks for modelling is based on their ability to generalise relationships occurring in the process despite the presence of noise in learning signals and in cases of the occurrence of redundant or correlated signals [26,27]. Issues explaining the principles of operation of artificial neural networks and their applications for modelling were discussed in numerous overview studies [14,26].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation

Lipiński

Bałasz

Rypina

2017

Int J Adv Manuf Technol

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The article presents a new methodology for modelling the influence of parameters and conditions of surface grinding process on the value of roughness and grinding forces. Grinding processes are characterised by numerous factors influencing results of the process with a complex mechanism of the cumulative effects of their interactions. Therefore, authors for the development of the model used an artificial neural network. The input parameters of the model, apart from the processing parameters, were the properties of the workpiece and features of the grinding wheel. In the selection process of the neural model structure, an evaluation criterion was proposed which included the character of the influence of processing parameters on the resultant values of the grinding process for the given pair workpiece-tool. A high ability to data generalisation by the developed neural model was demonstrated.

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…Issues explaining the principles of operation of artificial neural networks and their applications for modelling were discussed in numerous overview studies [14,26]. The structure most commonly used in the literature for process modelling is a feedforward multilayer network.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation

Lipiński

Bałasz

Rypina

2017

Int J Adv Manuf Technol

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“…An extensive review of the optimization techniques used in machining can be found in [39] and [40]. In …”

Section: Optimization Modulementioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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“…Certain studies have attempted to predict chatter phenomena in deep drilling using analytic solutions (Mehrabadi et al 2009). With regard to soft computing techniques, many publications are either devoted to drilling modelling (Chandrasekaran et al 2010) or refer to the problem (Choudhary et al 2009), but not so many examine deep drilling and even fewer study MQL deep drilling, where the physical phenomena differ from standard drilling. Fuzzy logic has been used to predict forces and surface quality on MQL deep drilling of aluminium (Nandi and Davim 2009), drill life (Biglari and Fang 1995;Jantunen and Vaajoensuu 2010) and better cutting conditions (Hashmi et al 2000) in deep drilling of steel with conventional flood cooling.…”

Section: Deep Drilling and Lubrication Systemsmentioning

confidence: 99%

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

2010

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A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented. The input data includes cutting parameters and axial cutting force obtained from the power consumption of the feed motor of the milling centres. Two different coolant strategies are tested: traditional working fluid and Minimum Quantity Lubrication (MQL). The model is constructed in three phases. First, a new strategy is proposed to evaluate and complete the set of available measurements. The primary objective of this phase is to decide whether further drilling experiments are required to develop an accurate roughness prediction model. An important aspect of the proposed strategy is the imputation of missing data, which is used to fully exploit both complete and incomplete measurements. The proposed imputation algorithm is based on a genetic algorithm and aims to improve prediction accuracy. In the second phase, a bag of multilayer perceptrons is used to model the impact of deep drilling settings on borehole roughness. Finally, this model is supplied with the borehole dimensions, coolant option and expected axial force to develop a 3D surface showing the expected borehole roughness as a function of drilling process settings. This plot is the necessary output of the model for its use under real workshop conditions. The proposed system is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.

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Application of soft computing techniques in machining performance prediction and optimization: a literature review

Cited by 302 publications

References 122 publications

Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation

Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

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