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BACKGROUND: Work-related musculoskeletal disorders (WMSDs) are leading cause of injuries among economically backward workers employed under small scale metal casting units especially in developing countries. In India, most casting unit’s falls under small and medium enterprises having inadequacy of advanced technological equipment’s due to several economic constraints and rely intensively on manual labour. Foundry work is very much prone to WMSDs involving much physical interaction of workers with their jobs which includes several risk factors. OBJECTIVE: The study objectives were to analyse the musculoskeletal risk prevalence among small scale casting workers using ergonomic assessment tools and statistical approach. METHODS: In present study, WMSDs risk prevalence has been examined using Rapid Entire Body Assessment (REBA) and virtual ergonomics. Further, risk evaluations were analysed using Mann–Whitney U test and Taguchi L25 orthogonal array. RESULTS: Results revealed manual handling task as being most vulnerable followed by the fettling section. Statistically significant differences were observed (p-value 0.05) for left side region; and lift-lower task and fettling section (p = 0.230; p > 0.05) for the right side region, where differences were not statistically significant. ANOVA results indicated that workstation height followed by population percentile and object weight were dominant factors significantly affecting the response parameter i.e. L4-L5 spine compression (p-value 0.05) had no significant effect. CONCLUSION: The present study may guide foundry industrialists in analysing the mismatch between the workers’ job profile and redesigning existing workstation layouts in small scale foundries based on minimizing the WMSDs risk severity associated with the work tasks.

Electrochemical discharge machining drilling (ECDD) is acknowledged as a hybrid machining process that employs the thermal erosion and etching action for drilling. Apart from experimental findings, very rare 3D simulation studies have been covered for analyzing the ECDD. In the present investigation, a 3D simulation of the ECDD process is performed to analyze the process based on the finite element method (FEM). The predicted results are found to be in consensus with the experimental results. An increment of 10.79 mg and 5.04 mg in MRR is noticed with the concentration level increase from 10 wt.% to 60 wt.% and voltage level increase from 35V to 55V. However, a maximum error of 12.68% is observed with the FEM method of prediction when compared to experimental results. The simulation study is followed by the multi-objective optimization (Taguchi’s L16) using grey relational analysis (GRA) for controlling the ECDD parameters. The simultaneous numerical and experimental assessment of the process with 3D Gaussian heat distribution using a novel tool feed monitoring system during soda-lime glass machining leads to the article’s novelty. A unique study on a number of tool contacts (NTC) with the work material is performed to minimize it to improve the geometrical characteristics. Material removal rate, overcut, number of tool contacts, and heat-affected zone are picked as response characteristics while tool feed rate, electrolyte concentration, applied voltage, and inter-electrode gap are selected as input variables. GRA optimized variables are obtained as 40V, 10 wt.%, 3 mm/min, and 35 mm with tool feed rate identified as the ascendant input variable having 35.46 percentage contribution.

Electrochemical discharge machining (ECDM) has been studied numerically by several researchers using single spark simulation during finite element modeling (FEM) for analyzing the material removal rate (MRR). However, the process includes the stochastic nature of the spark striking that leads to complexities. Because of this randomness, FEM based multi-spark simulation has not attempted till date. This article attempts to develop an improved model based on a randomly oriented multi-sparks for estimating the MRR. Gradually growing spark behavior and Gaussian heat input are utilized for acquiring the temperature distributions within the work-material. The temperature distributions are further processed to evaluate the MRR with consideration of craters overlapping formed during the multi-spark occurrence. The predicted results exhibit a fair agreement with the experimental results. The simulation-based parametric studies are performed on MRR using applied voltage, electrolyte concentration, and energy transference since it influences the total heat input energy given by the sparks.

This article statistically investigates the effect of various parameters such as material factors: silicon carbide (SiC) reinforcement, graphite (Gr) reinforcement and mechanical factors: normal load, sliding distance and speed on the sliding wear rate of vacuum stir cast self-lubricating AZ91D-SiC-Gr hybrid magnesium composites. The sliding wear tests have been performed on pin-on-disc tribometer at 10-50N loads, 1-3m/s sliding speed and 1000-2000m sliding distance. It has been examined that hybrid composites yielded improved wear resistance with reinforcement of SiC and solid lubricant graphite. ANOVA and signal-to-noise ratio investigation indicated that applied load was the most critical factor influencing the wear rate, followed by sliding distance. Further, the AZ91D/5SiC/5Gr hybrid composite has exhibited the best wear properties. From the SEM and EDS analysis of worn surfaces, delamination was confirmed as the dominant wear mechanism for AZ91D-SiC-Gr hybrid composites.

Scene understanding from the single image of an indoor scene is identified as a challenging task. This involves interpreting the assessment of multiple scene components, such as to identify the spatial layout from room, detect the objects in 3D space and classify the scene, to understand the nature of an indoor scene. Assessing the spatial structure of indoor scenes offers important geometric details and limits for various activities, such as indoor 3D-reconstruction, navigation, scene awareness and virtual reality. Most of the layout estimation function lacks the clutter and decorations present in the background, instead concentrating mainly on the horizontal wall contours. Room layout states the orientations, heights, and positions of walls with respective to its camera center. Then, a set of estimated boundaries or corner positions, or as a 3D mesh is characterized from layout. Nevertheless, identifying the 3D model from a single 2D picture is an ill-posed and challenging problem. Large number of techniques applies “Manhattan assumption” for layout estimation. The layout estimation approach is combined with few computer vision based techniques like semantic segmentation, object orientation estimation, scene classification, and 3D object detection to achieve indoor scene understanding. Additionally, the object detection and semantic segmentation is also reviewed along with this layout estimation approach. This survey provides valuable information to all researchers and those who are looking for better techniques in layout estimation.

In the current study, an experimental investigation has been done using the hybrid magnetic-field-assisted powder-mixed electric discharge machining (MFAPMEDM) technique to enhance the process performance while machining Inconel 706 superalloy. Inconel 706 is a newly developed superalloy for aircraft applications. All the experimentation work was done according to Taguchi’s L9 orthogonal array (OA) to analyse the effect of controlled input parameters like peak current (Ip), pulse ‘ON’ (Pon) and pulse ‘OFF’ (Poff) duration on response parameters. Material removal rate (MRR), tool wear rate (TWR) and surface roughness (Ra) were selected as response parameters, and further multi-response optimization was done through grey relational analysis (GRA). GRA results revealed that Pon (62.84%) is the most influential input variable followed by Ip (28.64%) and Poff (8.50%) for controlling all the responses simultaneously. GRA optimized parameters were determined as Ip (5 A), Pon (50 µs) and Poff (20 µs). The surface morphology of the tool (cathode) and workpiece (anode) was assessed using field emission scanning electron microscopy (FESEM), which revealed the presence of partially melted crater boundaries/debris, micro-holes and debris on the surface. The results presented in this paper will help future researchers in studying the machining characteristics of the Inconel 706 superalloy.

Electrical Discharge Machining (EDM) is acknowledged as one of the earliest non-conventional machining process employed successfully to process hard/difficult to machine materials. This process can produce components of complex geometries with precision. It works on the principle of thermo electric energy which removes material from non-contacted electrode and workpiece by the repeated sparks produced. The material from the workpiece and the tool is removed by melting and vaporization. The necessary condition for generating spark between the tool and the workpiece is that both the electrodes must be conductive in nature. This review article discusses the research studies conducted in EDM to enhance the process performance while machining Ni-Cr based Superalloys. These superalloys are extensively used in aerospace, automotive industry and gas turbines etc. Furthermore, this paper outlines the future research possibilities in the same field.