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The current review is focuses on a comprehensive investigation of the process parameters such as height of layer, orientation, speed of printing, Air gap, fill density, Infill structure, Raster angle, Width of raster, temperature of bed and how they influence the tribological behavior of the ABS specimens fabricated using the Fused Deposition Modelling (FDM), The tribological properties of FDM build parts are highly affected by these process parameters. Due to this, researchers have explained optimum values of these parameters to enhance the tribological behavior. According to the application, for which the part is produced, these process parameters should be chosen cautiously. For a particular demand, some of process parameters are important than rest, so there is need to be identify and optimize these important parameters for these researchers have investigated and employed different Design of Experiments (DOE) which are discussed in this paper. Another way of improving tribological behavior of ABS is the addition of material such as, such as graphite, PTFE, Zirconia, CaCO3, and, carbon fiber etc. The addition of materials to polymers has a significant impact on the wear resistance quality. This not only affect wear but also improve the friction coefficient as compare to the pure polymer. This paper aims at reviewing recent research on improving tribological behavior of ABS parts produced using FDM process.

In the present research, measurement of residual stress induced during turning and threading operations for the fabrication of two types of pin profiled friction stir processing/welding (FSP/FSW) tools, i.e. cylindrical profiled pin tool and cylindrical threaded profiled pin tool, is being dealt with. Workpiece was chosen to be H13 tool steel with a diameter of 22[Formula: see text]mm and 110[Formula: see text]mm length. Turning and threading was done on CNC machine tools using CNMG 12404-THM uncoated tungsten carbide cutting tool. For residual stress measurement of the workpieces, an XRD-based Pulsetec[Formula: see text]-X360n portable residual stress analyzer setup was used. The experimental results show that the cylindrical pin profile tool had a compressive residual stress of [Formula: see text][Formula: see text]MPa and compressive residual shear stress of [Formula: see text][Formula: see text]MPa, while the cylindrical threaded pin profile tool had a compressive residual stress of [Formula: see text][Formula: see text]MPa (51.8% more) and compressive residual shear stress of [Formula: see text][Formula: see text]MPa (40% less). It has been concluded that due to threading operation on the cylindrical threaded pin profile, the value of residual stress is more in it, and since the stress is compressive in nature, it would have a better positive impact while doing FSP/FSW than that of the cylindrical profiled pin tool.

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.

Friction stir processing (FSP) has materialised as a significantly improved procedure for surface modification and fabrication. This has propelled its use in the industries of aerospace, automobile...

Magnetic abrasive finishing is a prevalent advanced method which by applying gentle forces using flexible magnetic abrasives for removing surface irregularities, provides a micro/nano range of defe...

This research focuses on the use of Artificial Neural Network (ANN) for the prediction of the microhardness of friction stir processed aluminium based metal matrix composite (AA6061+Al2O3). Different specimens were obtained by using rotating speeds of 1100, 1210, 1320 and 1430 rpm and travelling speeds of 36, 48, 60, 72 mm/min. The microhardness value (HV) of the processed surface of each of the samples was measured and the data collected from the specimens was used as learning data for ANN. Higher rotational speed and lower transversal speeds resulted in higher hardness value since processing at higher tool rotational speed causes high material flow and good resistance to the tool pin profile. The increase of tool rotational speed also leads to the decrease of spindle torque due to ease of flow of material. A uniform increase in microhardness was observed up to 1320 RPM and a subsequent decrease on any further increments of tool rotational speed. Subsequently, the highest values of microhardness were observed with a square mandrel at 1320 RPM and 36 mm/min. The calculated results were satisfactorily compliant with the measured data and the ANN model was successful in predicting the microhardness.

Fabrication of glass fibre reinforced plastic was done using wet layup process and is discussed in the paper. The main purpose of the experiment was to identify the changes in the GFRP when subjected to various chemical resistance test and toughness test. GFRP was combined with different materials to form hybrid natural composite whose characteristics were studied and analysed. The results showed that GFRP had maximum chemical resistance in 5% Sodium hydroxide and minimum in 20% Sulphuric acid. From the Izod impact test, the maximum impact strength was found in unidirectional GFRP and minimum was found in orthogonal GFRP.

Aluminium surface composite having ceramic reinforcement is successfully developed using friction stir processing at different tool rpm. Pin-on-disc test was performed at different sliding distances (300 m, 600 m, 900 m) and at different applied loads (20 N, 30 N, 40 N), to analyse wear behaviour of the fabricated composites. Response surface methodology (RSM) and Artificial neural network (ANN) are used to successfully develop two different models and a comparative study was done of the predictive capacity of both the developed models. The comparative study shows that the predictive capacity of the ANN model is more efficient than the RSM model. RSM is also utilized to optimize the process parameter. Optimum condition predicted by the model is for the composite developed at 1200 tool rotational speed, applied with a load of 20 N for a sliding distance of 300 m. Scanning electron microscopy (SEM) and Energy dispersive spectroscopy (EDS) analysis of wear surface were done, revealing that adhesive wear is the major wear mechanism and oxide layer formation is present on the wear surface.

Owing to the MAF process’s capabilities for its efficient surface finishing, it is essential to understand the parallel quality of different response parameters and their interdependency. This paper focuses on identifying a relation between surface hardening and roughness induced by the MAF process. In this research work, the influence on micro-hardness from the value of surface roughness has been studied. The different experimental conditions are applied to Ti–6Al–4V specimens treated using a pulsating DC-MAF machining set-up, and Material removal was done using loosely bonded magnetic abrasive media. The surface roughness and microhardness share significant interdependency, represented by a graph showing the best relationship between surface microhardness and roughness.

Aluminum based hybrid metal matrix composites (HMMC) are being employed nowadays in automobile, aeronautical, sports equipment etc. In this study, three different Al6063 based hybrid metal matrix composites (HMMC) samples having reinforcement with 3%, 6% and 9% by weight respectively are fabricated using stir casting method. Reinforcements used are waste products, namely, jute ash, groundnut shell and sugarcane. Surface roughness of these fabricated composite are is tested by varying machining parameters. The design of experiment is constructed by Taguchi method using three factors and two level, with L8 orthogonal array in which surface roughness is the output response parameter. These recorded results are analysed using analysis of variance and optimization of process parameters is done using response surface methodology and genetic algorithm. The Genetic algorithm optimization is achieved within 102 generations which is quite fast. On comparing it was found that results obtained from GA closely agreed with those obtained from the Response surface methodology.

This research paper depicts the process of used liquid nitrogen at the interface of TiN coated carbide cutting tool insert (rake face) and AISI D3 workpiece. Design of experiments (DoE) was planned according to Taguchi L9 (OA) orthogonal array. The experimental results during machining such as cutting force, machining time and temperature were optimized by Taguchi S/N ratio and analysed by ANOVA. The contribution of machining parameters of (i) speed, (ii) feed and (iii) depth of cut for each response were evaluated. Feed had the highest effect on the percentage of contribution of 57.21% and 52.21% for cutting force and machining time, respectively. Speed had the highest effect on the contribution as 79.57% for the temperature at the interface of insert and workpiece. The predicted values at the optimum level of machining parameters for cutting force, machining time and temperature were 44.49 N, 37.09 sec. and 24.99°C, respectively. Regression models were made. The R-Sq values were 96.59, 89.34 and 96.09% for cutting force, machining time and temperature, respectively. The ratio of an average thickness of generated chip and feed was considered as the chip compression ratio. It was observed that the generated chips during cryogenic turning were thin, discontinuous, long snarled and most of the material had side flow on either side.

In the present work composite material is prepared by a combination of two or more constituent’s materials using squeeze casting process. Magnesium based metal matrix composites are casted using magnesium alloy reinforced with various volume fractions of silicon carbide particulates. A squeeze casting process has been employed under an inert atmosphere to produce magnesium matrix composites and this process leads to a complete wetting of silicon carbide particulates in the molten magnesium. Further, the synthesized magnesium based metal matrix composites are machined by computer numerically controlled lathe. Surface roughness and Metal Removal Rate of the composites are measured and their variation in performances are studied using orthogonal array, Also the mechanical behavior of the composites are determined and also evaluated by comparing the results with the unreinforced magnesium alloy.

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.

In the present study, Manufacturing of AA7075-T6 and boron carbide nano-ceramic particles using Friction stir processing (FSP) has been done. FSP technique has been widely utilized for surface modification and the formation of composite material. The B4C nanoparticles size(