Study of Wear, Stress and Vibration Characteristics of Silicon Carbide Tool Inserts and Nano Multi-Layered Titanium Nitride-Coated Cutting Tool Inserts in Turning of SS304 Steels

Abstract

Cutting tool characterization plays a crucial role in understanding the behavior of machining operations. The selection of a suitable cutting material, the operating conditions for the work piece, is necessary to yield good cutting-tool life. Several pieces of research have been carried out in cutting-tool characteristics for turning operation. Only a few pieces of research have focused on correlating the vibrations and stress with wear characteristics. This research article deals with stress induced in silicon carbide tool inserts and coated tool inserts while machining SS304 steel. Since this material is much less resistant to corrosion and oxidation it is widely used in engineering applications such as cryogenics, the food industry and liquid contact surfaces. Moreover, these materials have much lower magnetic permeability so they are used as nonmagnetic engineering components which are very hard. This article focuses on the machining of SS304 by carbide tool inserts and then, the cutting forces were observed with a tool dynamometer. Using observed cutting forces, the induced stress in the lathe tool insert was determined by FEA investigation. This research also formulates an idea to predict the tool wear due to vibration. Apparently, the worn-out tool vibrates more than new tools. Using the results, the relation between stress, strain and feed rate, depth of cut and speed was found and mathematically modeled using MINI TAB. It was observed that carbide tool inserts with coating withstand better than uncoated tools while machining SS304. The results were anticipated and correlation between the machining parameters furnished the prediction of tool life and obtaining the best machining outcomes by using coated tool inserts.

The Microstructure and Properties of Ni-Si-La2O3 Coatings Deposited on 304 Stainless Steel by Microwave Cladding

“…The average hardness of the SS-304 alloy was observed to be 210HV. The outcomes exhibit that there is about a 32.85% enhancement in the “hardness of SS-304 alloy” after the “microwave cladding of the mixture” of 69.72% Ni, 19.28% Si, and 11% La 2 O 3 particles on “SS-304 at optimum process parameters” [ 29 , 30 , 31 ]. In Figure 10 a–d, the SEM image displays the "microwave clad samples" produced using a "mixture of 69.72% Ni, 19.28% Si, and 11% La2O3 particles" on "SS-304" that was developed under optimal cladding parameters.…”

“…By retaining the best microwave settings, a uniform cladding layer and surface were produced. The created cladding surface’s dimensional deviation was minimized with increased “cladding layer and cladding surface homogeneity” [ 30 , 31 ]. The hardness of the cladding surface is better distributed when there is less variation in its dimensions.…”

“…RSM was used to identify the optimum combination of microwave cladding process parameters [ 29 , 30 , 31 ]. In the present study, the central composite design (CCD) in DOE was employed for experimental work [ 32 , 33 , 34 ].…”

A novel study on the influence of graphene-based nanofluid concentrations on the response characteristics and surface-integrity of Hastelloy C-276 during minimum quantity lubrication

“…The main reason behind little tool erosion than previous case is due to smaller v, f and higher accumulation of nanoparticles (1%) [ [85] , [86] , [87] ]. However, few spots of adhesion, breakage of nose part along with abrasion marks have been reported [ [88] , [89] , [90] ]. Finally, the larger flank, crater wear accompanying nose damage and complete failure of tool have been reported during 116 m/min, 0.246 mm/rev having 0.5% NC illustrated in Fig.…”

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

“…The nature of the burst was determined by a detailed study of each individual vibration signal which was dependent on the defect width [ 72 , 73 ]. Scalogram created by Continuous Wavelet Transform (CWT) offers critical information on roller entry and exit to the defect [ 76 , 77 , 78 ] and provides the basis for defect width measurement, and concludes misalignment conditions significantly affecting the remaining life of the bearing.…”