To characterize and optimize the surface quality attributes in slot milling operation

Niknam, Seyed Ali; Balazinski, Marek; Songmené, Victor

doi:10.1007/s00170-017-0460-5

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…where l is the sampling length, y is the ordinate of the profile curve, and n is the peak in the surface profile. 6 The average surface roughness R a was measured by a TR200 portable profilometer, which was calibrated within the measurement range of 0.025–12.5 µm. In each test, three different points were measured, and the average values were determined.…”

Section: Experimental Studymentioning

confidence: 99%

“…The surface quality of machined parts and cutting tool wear are two critical machinability factors, 5 which are widely affected by various cutting parameters. Surface roughness is the main characteristic of the surface quality, 6 which plays an important role in tool performance. 7 Using appropriate cutting fluids, proper strategies for cooling the tool-chip surface, and rapid and efficient evacuation of the generated chips are the most critical solutions to reduce surface roughness and tool wear.…”

Section: Introductionmentioning

confidence: 99%

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Eco-green machining of superalloy A286: Assessment of tool wear morphology and surface topology

Musavi

Davoodi

Niknam

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Unstable crude oil derivatives are among the main substances added to the mineral-based lubricants (MBL). However, the high purification and disposal costs of such lubricants and consequent environmental difficulties are the major drawbacks of cutting fluid consumption. The lower use of MBL or replacing them with less harmful ones, in principle, vegetable-based lubricants (VBL) are the prime importance of achieving less operating costs and pollution. Furthermore, the use of VBLs is much more recommended if machinability aspects, including tool life, surface, and edge quality, would also be improved. Therefore, in the present study, cutting parameters and lubricant types were used as the experimental variables in the MQL-turning of A286 superalloy. A286 is classified as a difficult-to-cut material with a wide range of applications in the energy and turbine industries. No experimental study was found on the machining of A286 under separate cooling and lubrication with two MQL systems simultaneously. The effects of cutting parameters on both average surface roughness Ra and tool wear morphology were evaluated. Experimental results denoted that the use of VBLs led to better surface topology. On the other hand, the tendency of the built-up layer (BUL) and built-up edge (BUE) phenomenon were intensified when MBL was used. On the word, better tool life is also expected when using VBL. This observation revealed that the use of VBL not only tends to improve productivity and environmental safety aspects but also machinability aspects; in principle, better surface topology and longer tool life are also expected.

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Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eco-green machining of superalloy A286: Assessment of tool wear morphology and surface topology

Musavi

Davoodi

Niknam

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Duboust et al [21] showed a change in surface quality when using different types of cutting tools, fibre orientation, and cutting settings for milling operations. Niknam et al [22] determined the optimal levels of adjustment of the milling parameters by improving the surface quality in the process of slot milling of aluminium alloys 2024-T351 and 6061-T6. However, based on the information available in the open access literature, especially for slot milling operations, it can be observed that the published work did not consider the processing of hardened steel.…”

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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The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling

Yeganefar

Niknam

Asadi

2019

Int J Adv Manuf Technol

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To characterize and optimize the surface quality attributes in slot milling operation

Cited by 6 publications

References 41 publications

Eco-green machining of superalloy A286: Assessment of tool wear morphology and surface topology

Eco-green machining of superalloy A286: Assessment of tool wear morphology and surface topology

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

The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling

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