Energy and cost integration for multi-objective optimisation in a sustainable turning process

Bagaber, Salem Abdullah; Yusoff, Ahmad Razlan

doi:10.1016/j.measurement.2018.12.096

Cited by 40 publications

(18 citation statements)

References 34 publications

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“…Tian et al (2019) developed a multi-objective cutting parameter optimization by considering the tool wear conditions. Bagaber and Yusoff (2019) integrated the energy and cost for multi-objective optimization in a sustainable turning process. Their research considered the dry and wet cutting processes.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Pangestu

Pujiyanto

Rosyidi

2021

Heliyon

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Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing.

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Section: Introductionmentioning

confidence: 99%

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Pangestu

Pujiyanto

Rosyidi

2021

Heliyon

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“…Based on the available information in the open literature for wiper insert and other machining applications [26][27][28], intensive efforts have been made in order to address the optimization of conflicting machining responses. However, none of them provided any sort of adaptive approach which is able to offer different solutions for different designs' orientations of the same process.…”

Section: Introductionmentioning

confidence: 99%

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

et al. 2020

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The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multicriteria decision making respectively. This integrated approach, to best of the authors' knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design. INDEX TERMS Adaptive design, Artificial neural networks, Genetic Algorithm, Modeling, Wiper inserts, Turning, NOMENCLATURE ANN Artificial Neural Network B Balanced design DOC Depth of Cut EC Intensive cost-oriented design EP Intensive productivity-oriented design EQ Intensive quality-oriented design f Feed GRA Gray Relation Analysis IC Intensive cost-oriented design IP Intensive productivity-oriented design IQ Intensive quality-oriented design LINMAP

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“…Multiple response optimization provides reasonable correlations among heterogeneous responses and helps to attain the solo optimum setting of process parameters that meets the specifications of all responses. 25 Biswesh et al 26 proposed an response surface methodology (RSM)-based nondominated sorted genetic algorithm to optimize the electrochemical machine operating parameters to increase the rate of material removal and reduce the roughness of the machined surface during the machining of hardened steel. A Pareto optimal solution was obtained by uniting the current, voltage, and flow rate of electrolyte and inter-electrode gap, thus providing a simultaneous reduction of surface roughness and a greater rate of material removal.…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB₂in situ composites using the Entropy–VIKOR method

Chandrasekhar

Prasad

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This article describes the multi-response optimization of electrochemical machining operating parameters such as voltage, concentration of the electrolyte, and current to maximize the rate of material removal in addition to minimize the over cut and delamination at the same time in micro-drilling of AA6061-TiB2 in situ composite. A novel Entropy–VIKOR method is applied to handle such mutually conflicting responses. The weight of each response is computed from the entropy method, and VIKOR method is used to rank the various levels of parameters. This method yields the combination of 2 mol of electrolyte concentration, 16 V of applied voltage, and 4 A of current as optimal parameters to minimize the over cut and delamination in addition to maximize the rate of material removal at the same time. This optimal process-governing parameters satisfied the conditions for compromising solution such as acceptable advantage and acceptable stability condition. The response graph illustrates that, of the parameters investigated, electrolyte concentration has the greatest effect on the VIKOR index, followed by applied voltage and current. Scanning electron microscopic analysis illustrates that the radius of the hole is equal throughout the periphery, except for one instance of micro-delamination.

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Energy and cost integration for multi-objective optimisation in a sustainable turning process

Cited by 40 publications

References 34 publications

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB₂in situ composites using the Entropy–VIKOR method

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Energy and cost integration for multi-objective optimisation in a sustainable turning process

Cited by 40 publications

References 34 publications

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB2in situ composites using the Entropy–VIKOR method

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Product

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Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB₂in situ composites using the Entropy–VIKOR method