Towards Optimization of Machining Performance and Sustainability Aspects when Turning AISI 1045 Steel under Different Cooling and Lubrication Strategies

Abbas, Adel T.; Benyahia, F.; Rayes, Magdy M. El; Pruncu, Catalin I.; Taha, Mohamed; Hegab, Hussien

doi:10.3390/ma12183023

Cited by 45 publications

(25 citation statements)

References 45 publications

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“…To sum up, the integrated approach showed effective capabilities to provide the optimal solution (optimal cutting conditions) which achieved a reasonable balance among all studied responses when machining Ti-6Al-4V. This is an important step towards a reliable machining system as obtained in the open literature [24][25][26].…”

Section: Results Validationmentioning

confidence: 75%

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

et al. 2020

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Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.

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Section: Results Validationmentioning

confidence: 75%

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

et al. 2020

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“…However, the tool wear results were not as severe as the productivity-based case. Thus, it is recommended in the future work to establish a detailed multi-objective optimization model which combines surface roughness, MRR, tool wear, machining cost and sustainability aspects as previously discussed in the open literature [22,23] in order to accurately judge the overall machining performance.…”

Section: Results and Discussion: Analysis Optimization And Validmentioning

confidence: 99%

Towards Optimization of Surface Roughness and Productivity Aspects during High-Speed Machining of Ti–6Al–4V

et al. 2019

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Nowadays, titanium alloys are achieving a significant interest in the field of aerospace, biomedical, automobile industries especially due to their extremely high strength to weight ratio, corrosive resistance, and ability to withstand higher temperatures. However, titanium alloys are well known for their higher chemical reactive and low thermal conductive nature which, in turn, makes it more difficult to machine especially at high cutting speeds. Hence, optimization of high-speed machining responses of Ti–6Al–4V has been investigated in the present study using a hybrid approach of multi-objective optimization based on ratio analysis (MOORA) integrated with regression and particle swarm approach (PSO). This optimization approach is employed to offer a balance between achieving better surface quality with maintaining an acceptable material removal rate level. The position of global best suggested by the hybrid optimization approach was: Cutting speed 194 m/min, depth of cut of 0.1 mm, feed rate of 0.15 mm/rev, and cutting length of 120 mm. It should be stated that this solution strikes a balance between achieving lower surface roughness in terms of Ra and Rq, with reaching the highest possible material removal rate. Finally, an investigation of the tool wear mechanisms for three studied cases (i.e., surface roughness based, productivity-based, optimized case) is presented to discuss the effectiveness of each scenario from the tool wear perspective.

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“…Abbas et al [38] presented an extensive study of the effectiveness of using different cooling and lubrication techniques when turning AISI 1045 steel. Three multi-objective optimization models were employed to select the optimal cutting conditions.…”

Section: Dimensions Of the Sustainability Metrics For Machining Procementioning

confidence: 99%

An Approach to Sustainable Metrics Definition and Evaluation for Green Manufacturing in Material Removal Processes

Ayabaca

Vila

2020

Materials

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Material removal technologies should be thoroughly analyzed not only to optimize operations but also to minimize the different waste emissions and obtain cleaner production centers. The study of environmental sustainability in manufacturing processes, which is rapidly gaining importance, requires activity modeling with material and resource inputs and outputs and, most importantly, the definition of a balanced scorecard with suitable indicators for different levels, including the operational level. This paper proposes a metrics deployment approach for the different stages of the product life cycle, including a conceptual framework of high-level indicators and the definition of machining process indicators from different perspectives. This set of metrics enables methodological measurement and analysis and integrates the results into aggregated indicators that can be considered for continuous improvement strategies. This approach was validated by five case studies of experimental testing of the sustainability indicators in material removal operations. The results helped to confirm or modify the approach and to adjust the parameter definitions to optimize the initial sustainability objectives.

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Towards Optimization of Machining Performance and Sustainability Aspects when Turning AISI 1045 Steel under Different Cooling and Lubrication Strategies

Cited by 45 publications

References 45 publications

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

Towards Optimization of Surface Roughness and Productivity Aspects during High-Speed Machining of Ti–6Al–4V

An Approach to Sustainable Metrics Definition and Evaluation for Green Manufacturing in Material Removal Processes

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