FINITE ELEMENT MODELING OF STRESS DISTRIBUTION IN THE CUTTING PATH IN MACHINING OF DISCONTINUOUSLY REINFORCED ALUMINIUM COMPOSITES

ABSTRACT One focus of this paper is to highlight issues on machining of discontinuously reinforced aluminium composites (DRACs), due to the complex deformation and interactions among particles, tool and matrix it is often unable to explore the behavior by an experimental or analytical method. This paper investigates the stress distribution in particles along, above and below cutting path under different cutting speed and constant depth of cut using finite element method. The development of stress fields in the DRACs was analyzed and physical phenomena such particle de-bonding, displacements and inhomogeneous deformation of matrix material were explored. It was found that tool-particle interaction and stress distributions in the particles/matrix are responsible for particle de-bonding and surface damage during machining of DRACs. Keywords: modeling, aluminium, composites, finite element, machining, stress distribution, tool, particle, interaction. Nomenclature σ y = Yield stress. σ o = Initial yield stress ε = Strain rate C and P = Cowper-Symonds parameters for strain rate. e eff = Effective plastic strain. β = Hardening parameter. E P = Plastic hardening modulus. E tan = Tangent modulus. E = Modulus of elasticity τ lim = Limiting shear stress τ = Equivalent shear stress P = Contact pressure µ = Friction of coefficient b = Cohesion sliding resistanc

Statistical and Artificial Neural Network Coupled Technique for Prediction of Tribo-Performance in Amine-Cured Bio-Based Epoxy/MMT Nanocomposites

This study explores the effects of four independent variables—the nanoclay weight percentage, sliding velocity, load, and sliding distance—on the wear rate and frictional force of nanoclay-filled FormuLITETM amine-cured bio-based epoxy composites. An experimental design based on the Taguchi method revealed diverging optimal conditions for minimizing the wear and frictional force. These observations were further validated using a Back-propagation Artificial Neural Network (BPANN) model, demonstrating its proficiency in predicting complex system behavior. Material characterization, conducted through Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS), illustrated the homogeneous distribution of the nanoclay within the FormuliteTM matrix, which is crucial for enhancing the load transfer and stress distribution. Atomic Force Microscopy (AFM) analysis indicated that the incorporation of nanoclay increases the surface roughness and peak height, which are important determinants of the material performance. However, an increase in the nanoclay percentage decreased these attributes, suggesting an interaction saturation point. Due to their augmented mechanical properties, the present study underscores the potential of amine-cured bio-based epoxy systems in diverse applications, such as automotive, aerospace, and biomedical engineering

Dependence of pre-treatment structure on spheroidization and turning characteristics of AISI1040 steel

AbstractDuring machinability, the combination of machining process parameters and the material properties of the component to be machined plays an important role. Material properties depend upon the type of phase form present and the grain size of the formed phases, which in turn depends upon the prior treatment given to alter the initial room temperature types and form. Accordingly, spheroidization treatment was carried out on medium carbon steel (AISI1040) by altering the initial room temperature structure through normalizing and hardening treatment. Machinability experiments were performed on CNC machine by varying machining process constraints. Tool wear and surface roughness of the machined component obtained by turning were analyzed and correlated. Using Minitab and full factorial design, the ANOVA study was carried out. With the help of regression analysis, residual and main effect plots combined optimization (tool wear and surface roughness) was targeted. ANOVA result shows excellent machinability for the as-bought-spheroidized condition where feed has a 67% contribution to tool wear (TW) whereas the depth of cut has a 71.91% contribution to surface roughness (SR). Also, the optimized regression values obtained for machining parameters are feed (0.39 mm/rev), depth of cut (0.6 mm), and spindle speed (780 rpm) with composite desirability of 0.8174. TW and SR experimental values for the optimized machining parameters are 0.039 mm and 2.89 μm, respectively, and the difference between the actual and optimized values is less than 5%

Tribological and Morphological Study of AISI 316L Stainless Steel during Turning under Different Lubrication Conditions

Due to growing environmental concerns and economical and social problems in manufacturing sectors, there is a huge demand for the substitution of existing cutting fluids. Further, the cutting fluids selected are expected to reduce the cutting force, improve the surface roughness and also minimize the tool wear during machining operations. Hence, this paper discusses the tribological and morphological behaviour of AISI 316L stainless steel while turning under minimum quantity lubrication (MQL) such as oil–water emulsion, mineral oil, simarouba oil, pongam oil and neem oil based on Taguchi L25 orthogonal array. From the extensive experimentation, it was observed that neem oil MQL with cutting speed of (140, 140, 60 m/min), feed of (0.30, 0.20, 0.10 mm/rev) and depth of cut of (1.0, 1.0, 1.0 mm) resulted in the lowest surface roughness (0.36 µm),cutting force (235.34 N) and tool wear (100.32 microns), respectively. Further, main effects plots and analysis of variance (ANOVA)can be successfully used to identify the optimum process input parameters and their percentage of contribution (P%) on the output parameters during turning of AISI 316L steel under MQL applications. The results clearly indicate that from both an ecological and economical standpoint, neem oil is the most effective lubricant in reducing cutting forces, tool wear and surface roughness during turning of AISI 316L stainless steel under MQL

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018