Effect of turning environments and parameters on surface integrity of AA6061-T6: experimental analysis, predictive modeling, and multi-criteria optimization

Javidikia, Mahshad; Sadeghifar, Morteza; Songmené, Victor; Jahazi, Mohammad

doi:10.1007/s00170-020-06027-w

Cited by 20 publications

(9 citation statements)

References 30 publications

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“…As seen in this figure, residual stresses increased with raising feed rate for both modes. This is because increasing feed rate increases the tool–chip contact area and the frictional heat, which increases temperature and residual stresses [ 1 , 11 ].…”

Section: Resultsmentioning

confidence: 99%

“…Machining operations are commonly used in the aerospace industry to produce the desired shape of components as reported by Javidikia et al [ 1 ] and Touazine et al [ 2 ]. Dry machining is frequently carried out due to the environmental and health regulations and reduction in the machining costs, as mentioned by Krolczyk et al [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…High tool wear can result in reduction in tool life and decrease the quality of the components, thereby increasing machining costs. Large tensile residual stresses can significantly affect fatigue life and corrosion resistance of machined components, which lead to crack propagation, as mentioned by Javidikia et al [ 1 ] and Sadeghifar et al [ 11 ], who carried out research on surface integrity induced by turning of AA6061-T6 and residual stresses and machining characteristics generated by orthogonal turning of 300M Steel, respectively. Based on the research work by Javidikia et al [ 9 ], the machining with the cutting speed below 900

is considered as LSC, while above 900

is known as HSC.…”

Section: Introductionmentioning

confidence: 99%

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Low and High Speed Orthogonal Cutting of AA6061-T6 under Dry and Flood-Coolant Modes: Tool Wear and Residual Stress Measurements and Predictions

et al. 2021

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The present research work aimed to study the effects of cutting environments and conditions on tool wear and residual stresses induced by orthogonal cutting of AA6061-T6. Cutting environments included dry- and flood-coolant modes and cutting conditions consisted of cutting speed and feed rate. A 2D finite element (FE) model was developed to predict tool wear and residual stresses and was validated by experimental measurements including machining forces, tool wear, and residual stresses. This was obtained by exploring various magnitudes of the shear friction factor and heat transfer coefficient and choosing proper coefficients using the calibration of the predicted results with the measured ones. The experimental results showed that the effect of cutting environment including dry and flood-coolant modes was negligible on machining forces. The experimental investigation also demonstrated that increasing feed rate raised machining forces, tool wear and residual stresses in both cutting environments. Low Speed Cutting (LSC) led to the highest value of tool wear and High Speed Cutting (HSC) provided the lowest values of resultant machining forces and residual stresses in both modes. Flood-coolant mode reduced tool wear and slightly decreased tensile residual stresses in comparison with dry mode. As a result, low feed rate and high-speed cutting under flood-coolant mode were proposed in order to improve tool wear and residual stress in orthogonal cutting of AA6061-T6.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

is considered as LSC, while above 900

is known as HSC.…”

Section: Introductionmentioning

confidence: 99%

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Low and High Speed Orthogonal Cutting of AA6061-T6 under Dry and Flood-Coolant Modes: Tool Wear and Residual Stress Measurements and Predictions

et al. 2021

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“…A great sense of importance exists in a consistent definition of quality of life, because only a competitive region can create a sustainable standard of living or a positive contribution to the growth of quality of life [9,10]. Many scientific studies prefer quality of life indexes in regions to compare quality of life, as they bring benefits in inter-score detection of differences and benchmarking [11,12]. Each indicator shall be homogeneously defined and shall ensure consistency of the system under examination in a given area.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Criteria Decision Analysis Using TOPSIS and WSA Method for Quality of Life: The Case of Slovakia Regions

Hurajová

Hajduová

2021

Mathematics

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Quality of life can be expressed as a highly subjective measure of happiness, which is an important part of many financial and non-financial decisions. The issue of quality of life, namely its quantification, is a current topic, evidenced by a number of literary sources in both foreign and domestic literature. The paper deals with this issue in the conditions of the Slovak Republic and studies the quality of life for each region of Slovakia with respect to the given indicators. In this paper, we deal with the use of the methods of multi-criteria analysis in evaluating the quality of life in Slovakia. The aim of this paper is to highlight that it is important to monitor the level of quality of life at the regional level and that one of the indicators influencing the quality of life in the region may be its location in relation to neighboring countries.

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“…Influence of cutting parameters on SLP are mentioned in standard reference works for different machining processes [1,16,17]. Several researchers report relations between cutting speed vc, tool feed fz, cutting width ae and the resulting SLP [3,[18][19][20][21][22]. Microhardness and residual stresses are influenced differently depending on the feed direction [23,24].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Surface Layer Property Prediction for Milling Operations

Uhlmann

Holznagel

Schehl

et al. 2021

JMMP

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Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since the relation between tool wear, cutting parameters, and surface layer properties is mostly unknown, the latter cannot be controlled during production and may vary from part to part as tool wear progresses. To account for this uncertainty and to prevent premature failure, components often need to be oversized or surface layer properties need to be adjusted in subsequent manufacturing processes. Several approaches have been made to obtain models that predict the surface layer properties induced by manufacturing processes. However, those approaches need to be calibrated with a considerable number of experimental trials. As trials are time-consuming and surface layer measurements are laborious, no industrial applications have been realized. Complex models have one major drawback. They have to be re-parameterized as soon as process characteristics change. Therefore, manual experimental parameterization does not appear to be a feasible approach for industrial application. A highly automated approach for the machine learning of the relation between tool wear, cutting parameters and surface layer properties is presented in this paper. The amount of obtained measurement data allows a fundamental analysis of the approach, which paves the way for further developments.

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Effect of turning environments and parameters on surface integrity of AA6061-T6: experimental analysis, predictive modeling, and multi-criteria optimization

Cited by 20 publications

References 30 publications

Low and High Speed Orthogonal Cutting of AA6061-T6 under Dry and Flood-Coolant Modes: Tool Wear and Residual Stress Measurements and Predictions

Low and High Speed Orthogonal Cutting of AA6061-T6 under Dry and Flood-Coolant Modes: Tool Wear and Residual Stress Measurements and Predictions

Multiple-Criteria Decision Analysis Using TOPSIS and WSA Method for Quality of Life: The Case of Slovakia Regions

Machine Learning of Surface Layer Property Prediction for Milling Operations

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