Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization

Kumar, S. P. Leo

doi:10.1016/j.measurement.2019.04.029

Cited by 59 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surface integrity, machining efficiency, and cutting force are usually taken as objectives in most of the traditional optimization of cutting parameters. For example, Kumar [4] adopted surface roughness and material removal rate (MRR) as objectives to optimize the cutting parameters in turning C360 copper alloy. Zhou et al [5] obtained the Pareto optimal solution with the maximum MRR and the minimum surface roughness in turning AISI 304 based on the genetic algorithm gradient boosting regression tree (GA-GBRT) model they established.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Zhao

et al. 2020

Metals

View full text Add to dashboard Cite

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Zhao

et al. 2020

Metals

View full text Add to dashboard Cite

show abstract

“…The rate of tool advancement through the workpiece while turning is termed as feed. Feed contributed a high role in surface roughness and material removal rate [9]. From the experimental process it can be clearly studied that feed is one of the major factor effecting the surface roughness.…”

Section: Effect Of Feed On Surface Roughnessmentioning

confidence: 99%

Experimental Studies on Machining EN45 Steel under Dry and MQL using Uncoated Inserts

Satynarayana¹,

Reddy²,

Srikar³

et al. 2020

E3S Web Conf.

View full text Add to dashboard Cite

In owe to the global concern of environment growth and green production acts with use biodegradable and low consumption of lubrications the present paper deals use of MQL in turning EN 45 steel. Literacy over the use of effective production practices increases the efficiency of surface integrity and economical effect of production. With this aspect, the present study deals with of surface roughness characteristic generated while turning EN 45 graded spring steel with uncoated CNMG insert under MQL condition. A total of 27 experiments are performed on a CNC lathe under both Dry and MQL conditions. Surface roughness produced with varying in machining parameter of cutting speed (75, 100, 125 m/min), feed rate (0.1, 0.2, 0.3 mm/rev) and depth of cut (0.3, 0.6, 0.9 mm). Effect of induvial parameter over surface roughness is been clearly pictured out with the graphical representation with comparison under both DRY and MQL conditions. A mathematical model was generated with the experimental results for prediction of roughness within the limits of parameters. ANOVA analysis, feed shows a high contribution towards the surface roughness.

show abstract

“…Uncertainty analysis invariably represents the precision of the measured value. Thus, performing this analysis ensures that the obtained value does not deviate too much from the true value [14]. Therefore, for the 25 trails, the analysis was performed using the equations (1), (2), (3) and (4).…”

Section: Fig 3 Vickers Microhardness Testing Machine E Uncertaintymentioning

confidence: 99%

Uncertainty Error Analysis on Micro Hardness of Al6061-B4C Surface Composites Produced by Friction Surfacing

Mohanasundaram,

Vijay,

et al. 2019

IJEAT

View full text Add to dashboard Cite

Friction surfacing is a confined surface modification process of depositing a layer of a consumable tool (Mechtrode) over the base plate (Substrate). This solid-state surfacing opts for dissimilar material and erosion resistant coatings. It is also utilized for localized repairing of worn-out components. In the present study, the hardness of the coated material is compared with the substrate. In this experiment, the Al-B4C composite consumable rod is prepared with Aluminium 6061 alloy and 3, 6, 9, 12 and 15 weight % of B4C by stir casting and coated over the Aluminium 6061 alloy plate. The 25-run experiment is conducted for the combination of the rotational speed, traverse speed and axial load. The combined effect of process parameters and the increase in weight % of B4C results in the change in hardness. The hardness of the coating is enhanced by 65% than the substrate. The uncertainty analysis revealed that it has a good correlation with the hardness standard value and also it has an error of 5%. The ANOVA analysis concluded that the rotational speed and the weight percentage of the reinforcement improved the microhardness of the coating

show abstract

Measurement and uncertainty analysis of surface roughness and material removal rate in micro turning operation and process parameters optimization

Cited by 59 publications

References 22 publications

Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Experimental Studies on Machining EN45 Steel under Dry and MQL using Uncoated Inserts

Uncertainty Error Analysis on Micro Hardness of Al6061-B4C Surface Composites Produced by Friction Surfacing

Contact Info

Product

Resources

About