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The Finite Element Method and Response Surface Method are used to find the effect of milling parameters (Cutting speed, Feedrate and Axial depth) on plastic strain when milling Hastelloy C‐22HS. This simulation gain more understanding of the strain distribution in metal cutting. Response surface method (RSM) has been used to minimize the number of simulation. The contour plot from the RSM shows the relationship between variables (cutting speed, feedrate and axial depth) and response (plastic strain ‐ rate).The friction interaction along the tool‐chip interface is modeled with Coulomb friction law.

The purpose of this paper is to investigate the efficiency of cutting fluids when end milling AISI 304 stainless steels.

Two groups of cutting tests were conducted, one with the application of a coolant (wet machining) and the other – without (dry cutting), using multilayer coated carbide inserts. The findings of tool life and tool wear mechanisms are compared.

Coolant application proves to be efficient at low‐cutting speeds. With increasing the cutting speed, the coolant effect on improving tool life becomes less significant. Built‐up edge and nose wear are the dominant failure mechanisms in dry machining, while in wet machining, the dominant mechanisms are found to be notch wear and cutting edge grooving.

This paper provides useful information for manufacturing engineers dealing with end milling of stainless steel components. It helps select beneficial cutting conditions for dry and wet end milling operations.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

This paper aims to examine the performance of the machining parameters used in the hard-turning process of DIN 1.2738 mold steel and identify the optimum machining conditions.

Experiments were carried out via the Taguchi L18 orthogonal array. The evaluation of the experimental results was based on the signal/noise ratio. The effect levels of the control factors on the surface roughness and flank wear were specified with analysis of variance performed. Two different multiple regression analyses (linear and quadratic) were conducted for the experimental results. A higher correlation coefficient (R²) was obtained with the quadratic regression model, which showed values of 0.97 and 0.95 for Ra and Vb, respectively.

The experimental results indicated that generally better results were obtained with the TiAlN-coated tools, in respect to both surface roughness and flank wear. The Taguchi analysis found the optimum results for surface roughness to be with the cutting tools of coated carbide using physical vapor deposition (PVD), a cutting speed of 160 m/min and a feed rate of 0.1 mm/rev, and for flank wear, with cutting tools of coated carbide using PVD, a cutting speed of 80 m/min and a feed rate of 0.1 mm/rev. The results of calculations and confirmation tests for Ra were 0.595 and 0.570 µm, respectively, and for the Vb, 0.0244 and 0.0256 mm, respectively. Developed quadratic regression models demonstrated a very good relationship.

Optimal parameters for both Ra and Vb were obtained with the TiAlN-coated tool using PVD. Finally, confirmation tests were performed and showed that the optimization had been successfully implemented.

This paper aims to investigate the effect of laser surface texturing (LST) on the wear behavior of C-263 nickel-based superalloy and to identify the optimum wear operating condition.

C-263 nickel-based superalloy was selected as substrate material and pico-second Nd-YAG laser was used to fabricate the waviness groove texture on their surface. Wear experiments were designed based on Box-Bhenken design with three factors of sliding velocity, sliding distance and applied load. Wear experiments were performed using pin on disc tribometer. Morphologies of textures and worn-out surfaces were evaluated by scanning electron microscopy and energy dispersive spectroscopy. Surface topographies and surface roughness of the textures were evaluated by weight light interferometry. The response surface methodology was adopted to identify the optimum wear operating condition and ANOVA to identify the significant factors.

LST improves the wear resistance of C-263 nickel-based superalloy by appeoximately 82 per cent. Higher wear rate occurs at maximum values of all operating conditions, and applied load affects the coefficient of friction. Applied load significantly affects the wear rate of un-textured specimen. The interaction of sliding velocity and applied load also affects the wear rate of textured specimens. The optimum parameters to get minimum wear rate for un-textured specimens are 1.5 m/s sliding velocity, 725 m sliding distance and 31 N of applied load. For textured specimens, the optimum values are 1.5 m/s sliding distance, 500 m sliding distance and 40 N of the applied load.

Literature on laser texturing on nickel-based superalloy is very scarce. Specifically, the effect of laser texturing on wear behavior of the nickel-based superalloy C-263 alloy is not yet reported.