In the present research work, AA7075 composite reinforced with silicon carbide particles has been fabricated using Friction stir processing (FSP). The silicon carbide particles having a size of 40 μm were placed in grooves of length 160 mm, width 2 mm, depth 3.5 mm, that were generated on the AA7075 plate. The square pin tool is utilized for fabricating the composite at two different rotational speed i.e. 700 and 1000 rpm. Effect of processing, particle addition and tool rotational speed is analyzed on mechanical and wear properties of the material. On friction stir processing the microhardness value and elongation of the material increased. Reinforcement addition contributed to decrease in ductility and tensile strength while on the contrary microhardness and wear resistance of the material improved. Tool rotational speed showed a direct relation with the tested mechanical and wear properties. Adhesive wear was the prominent wear mechanism and Fe layer formation was observed on the worn surface, contributing to increased wear resistance. These fabricated composites can find vast application in industries like automotive, defence and aerospace.

Additive manufacturing (AM) is a rapidly growing technology with promising results and challenges. The aim of this study is to optimize the process parameters of fused deposition modeling (FDM) by exploring the wear performance of Polylactic acid (PLA). In this work, variation of process parameters like layer thickness, orientation and extruder temperature has been investigated. Based on these parameters wear specimen (accordance to ASTM G99) was printed by using FDM. The wear behavior of polymer pin under low sliding speed was investigated. Taguchi Design of experiments by using L₉ orthogonal array is applied to optimize the process parameters at which minimum wear rate is obtained and the same has also been investigated by using analysis of variance (ANOVA) and artificial neural network (ANN) technique for rigorous validation / optimization. Results shows that build orientation have major influence on the wear performance of polymer pin. The paper is presented with the display of results, discussion, and conclusions drawn.

Magnetic abrasive finishing (MAF) is an advanced precise finishing method that achieves micro-level to nano-level surface roughness. In industries, MAF is highly recommended where zero or negligible post-process surface defects are an obligatory requirement. In the same context, process optimisation is essential for making it commercially viable. This study presents an artificial neural network and genetic algorithm (ANN-GA), a robust modelling and optimisation tool (applicable to any sort of data set orthogonal array design or non-orthogonal array design) that is applied to scrutinise and improve the performance of the magnetic abrasive finishing of stainless steel SS302. In addition, the results from ANN-GA modelling and optimisation have been compared with conclusions drawn from conventionally used Taguchi-ANOVA analysis. An L₂₇ non-orthogonal array design has been opted for as per machining set-up restriction. Abrasive size, voltage, machining gap, and rotational speed were the design variables considered in the present research work. It was found that the parametric design used in this study provides a straightforward, methodical, and proficient method of modelling and optimisation of change of surface roughness or finishing behaviour during the MAF process. Modelling and optimisation done with ANN-GA show a maximum value of (ΔR _a)_max equal to 0.256 µm, which is 7% better than the result obtained from Taguchi-ANOVA analysis.

In the present study, fabrication of surface composite using AA7075-T6 as a matrix material and boron carbide nanoparticles as reinforcement through Friction stir processing (FSP) has been done. FSP technique has been widely utilized for surface modification and the formation of composite material. The B ₄ C nanoparticles size (<30 nm) as reinforcement were padded in dimension of 2 mm width and 1.5 mm depth groove of AA7075-T6 plate as a matrix material. The single-pass process executed using a square tool pin with tool rotation and traverse speed of 1000 rpm and 40 mm min ⁻¹ respectively. This research aims to observe and process Self-Assembled Monolayer (SAM), investigate the effect of the B ₄ C nano-ceramic particles on the AA7075-T6 and its mechanical properties of the nano-ceramic surface composite. Frictional and wear analysis investigations under various physical conditions have highlighted surface durability characteristics of the metal matrix composite of AA7075-T6. Microstructure results along with fractography-image highlight the homogeneous distribution of boron carbide nano-ceramic particle. Tensile test, Microhardness, microstructure, Field Emission Scanning-Electron Microscope (FESEM), and X-Ray Diffraction (XRD) analyzed the fabricated (Al + B ₄ C) nano-ceramic surface composite. The fabricated nano-ceramic surface composite could be utilized in lightweight applications such as aerospace, marine, defence, and automotive industry.

In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L₁₆ design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B₄C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.

In the present research study, Friction stir processing (FSP) has been utilized to prepare nano surface composites, AA7075 based matrix was reinforced with B ₄ C nanoparticles (size <30 nm). The aim of this study is to form a thin layer of B ₄ C over the surface of AA7075 based matrix material through Self-Assembled Monolayer (SAM) technique followed by Friction stir processing. The major advantage of SAM is to minimize the quantity of B ₄ C nanoparticles used in the preparation of nano surface composites. Additionally, this research also investigates the effect of tool rotation speed of Friction stir processing on mechanical and wear properties of processed nano surface composite. The results observed a uniform dispersion of nanoparticles in the processed nano surface composite and an improved value of microhardness with maximum value was found to be 185 Hv of the sample processed at 1200 rpm, compared to base metal. For the constant load, as FSP tool rotation speed increases, wear resistance increases from 1000 to 1200 rpm and decreases slightly for 1400 rpm. Scanning Electron Microscope (SEM) micrograph, tensile test and Field Emission Scanning-Electron Microscope (FESEM) fractography image used to study microstructure and the mechanical properties of processed nano surface composite. The x-ray Diffraction (XRD) showed the presence of B ₄ C nanoparticles. Processed nano surface composites can be used for aircraft and automobile industry applications.