Multi-objective robust optimization of the sustainable helical milling process of the aluminum alloy Al 7075 using the augmented-enhanced normalized normal constraint method

Pereira, Robson Bruno Dutra; Leite, Rodrigo Reis; Alvim, Aline Cunha; Paiva, Anderson Paulo de; Ferreira, João Roberto; Davim, J. Paulo

doi:10.1016/j.jclepro.2017.03.121

Cited by 27 publications

(9 citation statements)

References 40 publications

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“…Helical milling in the soft alloy Al 7075 was addressed considering robust multi-response optimisation [34] and considering multivariate robust optimisation [35]. In both cases the noise factor considered was the tool overhang length.…”

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confidence: 99%

Multi-objective robust design of helical milling hole quality on AISI H13 hardened steel by normalized normal constraint coupled with robust parameter design

Pereira

Silva

Lauro

et al. 2019

Applied Soft Computing

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Helical milling is a hole-making process which has been applied in hardened materials. Due to the difficulties on achieving high-quality boreholes in these materials, the influence of noise factors, and multi-quality performance outcomes, this work aims the multi-objective robust design of hole quality on AISI H13 hardened steel. Experiments were carried out through a central composite design considering process and noise factors. The process factors were the axial and tangential feed per tooth of the helix, and the cutting velocity. The noise factors considered were the tool overhang length, the material hardness and the borehole height of measurement. Response models were obtained through response surface methodology for roughness and roundness outcomes. The models presented good explanation of data variability and good prediction capability. Mean and variance models were derived through robust parameter design for all responses. Similarity analysis through cluster analysis was realised, and average surface roughness and total roundness were selected to multi-objective optimisation. Mean square error optimisation was performed to achieve bias and variance minimization. Multi-objective optimisation through normalized normal constraint was performed to achieve a robust Pareto set for the hole quality outcomes. The normalized normal constraint optimisation results outperformed the results of other methods in terms of evenness of the Pareto solutions and number of Pareto optimal solutions. The most compromise solution was selected considering the lowest Euclidian distance to the utopia point in the normalized space. Individual and moving range control charts were used to confirm the robustness achievement with regard to noise factors in the most compromise Pareto optimal solution. The methodology applied for robust modelling and optimisation of helical milling of AISI H13 hardened steel was confirmed and may be applied to other manufacturing processes. KeywordsHelical milling; AISI H13 hardened steel; Multi-objective robust optimization; Robust parameter design; Normalized normal constraint method. R 2 coefficient of determinationRadj 2 adjusted coefficient of determination Radj 2 prediction coefficient of determination

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confidence: 99%

Multi-objective robust design of helical milling hole quality on AISI H13 hardened steel by normalized normal constraint coupled with robust parameter design

Pereira

Silva

Lauro

et al. 2019

Applied Soft Computing

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“…In the experiment of turning steel AISI 1045 [ 59 ], used a cutting fluid containing 3% vegetable oil emulsion. Also, Pereira et al used the wet cutting lubricant which is a soluble biodegradable emulsified oil used to deal with aluminum alloy Al 7075 for milling operations [ 127 ]. In addition, Kara and Li conducted turning and milling experiments on two materials namely brass and mild steel under dry and wet cutting [ 43 ].…”

Section: Optimization With Cooling/lubrication Techniques For Energy ...mentioning

confidence: 99%

Optimization techniques for energy efficiency in machining processes—a review

abdelaoui

Jabri

Barkany

2023

Int J Adv Manuf Technol

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Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving.

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“…In the process of slot milling, the main indicator of the quality of the machined surface is the roughness. However, the environmental aspect of the machining process is of increasing importance today [8,9]. Production should not only provide accurate processing parameters but also use more productive methods and technologies [9] to fulfil the dream of a sustainable society [10].…”

Section: Introductionmentioning

confidence: 99%

Sustainability Assessment, Investigations, and Modelling of Slot Milling Characteristics in Eco-Benign Machining of Hardened Steel

Markopoulos

Karkalos

Mia

et al. 2020

Metals

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The hardened tool steel AISI O1 has increased strength, hardness, and wear resistance, which affects the complexity of the machining process. AISI O1 has also been classified as difficult to cut material hence optimum cutting parameters are required for the sustainable machining of the alloy. In this work, the effect of feed peer tooth (fz), cutting speed (vc), cutting of depth (ap) on surface roughness (Ra, Rt), cutting force (Fx, Fy), cutting power (Pc), machining cost (Ci), and carbon dioxide (Ene) were investigated during the slot milling process of AISI O1 hardened steel. A regression analysis was carried out on the obtained experimental results and the induction of nonlinear mathematical equations of surface roughness, cutting force, cutting power, and machining cost with a high coefficient of determination (R2 = 90.62–98.74%) were deduced. A sustainability assessment model is obtained for optimal and stable levels of design variables when slot milling AISI O1 tool steel. Stable indicators to ensure personal health and safety of operation, P1 values were set to “1” at a cutting speed of 20 m/min or 43.3 m/min and “2” at a cutting speed of 66.7 m/min or 90 m/min. It is revealed that for eco-benign machining of AISI O1, the optimum parameters of 0.01 mm/tooth, 20 m/min, and 0.1 mm should be adopted for feed rate, cutting speed, and depth of cut respectively.

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Multi-objective robust optimization of the sustainable helical milling process of the aluminum alloy Al 7075 using the augmented-enhanced normalized normal constraint method

Cited by 27 publications

References 40 publications

Multi-objective robust design of helical milling hole quality on AISI H13 hardened steel by normalized normal constraint coupled with robust parameter design

Multi-objective robust design of helical milling hole quality on AISI H13 hardened steel by normalized normal constraint coupled with robust parameter design

Optimization techniques for energy efficiency in machining processes—a review

Sustainability Assessment, Investigations, and Modelling of Slot Milling Characteristics in Eco-Benign Machining of Hardened Steel

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