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The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

This study aims to analyse the changes in mechanical and wear performance of aluminium alloy when yttrium oxide particles are incorporated. The microstructures are studied to analyse the change in the grain structures. Worn surfaces are observed via scanning electron microscope to study the wear mechanism in detail.

Stir casting is used to incorporate varying composition of yttrium particles, having an average particle size of 25 micrometer, in aluminium alloy 6063 matrix. Wear testing is carried out by DUCOM manufactured high temperature rotatory tribometer, and an indentation test is used for analysing the microhardness of the fabricated samples.

Microhardness of the material is increased with the increasing content of particulate addition. With the increasing content of reinforcement, more refined grains are produced. The load is transferred from the matrix to more rigid yttrium oxide particles. These factors contributed to escalated microhardness of the reinforced samples. Particulate addition enhanced the wear performance of the material; this might be attributed to increased microhardness and formation of an oxide layer.

Aluminium composites are finding wide applications in various industries, and there is always a requirement of material with enhanced tribological properties. Yttrium oxide particles exhibit improved mechanical properties, and their interaction with the aluminium matrix has not been studied much in the past. So, in this work, yttrium oxide incorporated aluminium matrix is studied.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.

Friction stir processing (FSP) is overviewed with the process variables, along with the thermal aspect of different metals.

With its inbuilt advantages, FSP is used to reduce the failure in the structural integrity of the body panels of automobiles, airplanes and lashing rails. FSP has excellent process ability and surface treatability with good corrosion resistance and high strength at elevated temperatures. Process parameters such as rotation speed of the tool, traverse speed, tool tilt angle, groove design, volume fraction and increase in number of tool passes should be considered for generating a processed and defect-free surface of the workpiece.

FSP process is used for modifying the surface by reinforcement of composites to improve the mechanical properties and results in the ultrafine grain refinement of microstructure. FSP uses the frictional heat and mechanical deformation for achieving the maximum performance using the low-cost tool; the production time is also very less.

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The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.

In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.

The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.

The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

The purpose of this paper is to explore a multi-criteria decision-making (MCDM) methodology to determine an optimal combination of process parameters that is capable of generating favorable dimensional accuracy and product quality during turning of commercially pure titanium (CP-Ti) grade 2.

The present paper recommends an optimal combination of cutting parameters with an aim to minimize the cutting force (F_c), surface roughness (R_a), machining temperature (T_m) and to maximize the material removal rate (MRR) after turning of CP-Ti grade 2. This was achieved by the simultaneous optimization of the aforesaid output characteristics (i.e. F_c, R_a, T_m, and MRR) using the MCDM-based TOPSIS method. Taguchi’s L₉ orthogonal array was used for conducting the experiments. The output responses (cutting force: F_c, surface roughness: R_a, machining temperature: T_m and MRR) were integrated together and presented in terms of a single signal-to-noise ratio using the Taguchi method.

The results of the proposed methodology depict that the higher MRR with desirable surface quality and the lower cutting force and machining temperature were observed at a combination of cutting variables as follows: cutting speed of 105 m/min, feed rate of 0.12 mm/rev and depth of cut of 0.5 mm. The analysis of variance test was conducted to evaluate the significance level of process parameters. It is evident from the aforesaid test that the depth of cut was the most significant process parameter followed by cutting speed.

The selection of an optimal parametric combination during the machining operation is becoming more challenging as the decision maker has to consider a set of distinct quality characteristics simultaneously. This situation necessitates an efficient decision-making technique to be used during the machining operation. From the past literature, it is noticed that only a few works were reported on the multi-objective optimization of turning parameters using the TOPSIS method so far. Thus, the proposed methodology can help the decision maker and researchers to optimize the multi-objective turning problems effectively in combination with a desirable accuracy.

This study aims to comparatively analyze the cut parts obtained as a result of cutting the Ni-based Inconel 625 alloy, which is widely used in the aerospace industry, with the wire electro-discharge machining (WEDM) and abrasive water jet machining (AWJM) methods in terms of macro- and microanalyses.

In this study, calipers, Mitutoyo SJ-210, Nikon SMZ 745 T, scanning electron microscope and energy dispersive X-ray were used to determine kerf, surface roughness and macro- and microanalyses.

Considering the applications in the turbine industry, it has been determined that the WEDM method is suitable to meet the standards for the machinability of Inconel 625 alloy. In contrast, the AWJM method does not meet the standards. Namely, while the kerf angle was formed because the hole entrance diameters of the holes obtained with AWJM were larger than the hole exit diameters, the equalization of the hole entry and exit dimensions, thanks to the perpendicularity and tension sensitivity of the wire electrode used in the holes drilled with WEDM ensured that the kerf angle was not formed.

It is known that the surface roughness of the parts used in the turbine industry is accepted at Ra = 0.8 µm. In this study, the average roughness value obtained from the successful drilling of Inconel 625 alloy with the WEDM method was 0.799 µm, and the kerf angle was obtained as zero. In the cuts made with the AWJM method, thermal effects such as debris, microcracks and melted materials were not observed; an average surface roughness of 2.293 µm and a kerf of 0.976° were obtained.

This paper aims to enhance the surface finish of the fused deposition modeling (FDM) part using the vapor smoothening (VS) post-processing method and to study the combined effect of FDM and VS process parameters on the quality of the part.

Analysis of variance method is used to understand the significance of the FDM and VS process parameters. Following this, the optimized parameter for multiple criteria response is reported using the technique for order preference by similarity to ideal solution. The process parameters alternatives are build orientation angle, build surface normal and exposure time and the criteria are surface roughness and dimensional error percentage.

The result observed contradicts the result reported on the independent parameter optimization of FDM and VS processes. There is a radical improvement in the surface finish on account of the coating process and an increase in the exposure time results in the decrease of the surface roughness. Minimum surface roughness of 0.11 µm is observed at 1,620 build angle and the least dimensional error of 0.01% is observed at build orientation angle 540. The impact of VS on the up-facing surface is different from the down-facing surface due to the removal of support material burrs and the exposure of the surface to vapor direction.

A study on the multi-criteria decision-making to ascertain the effect of post-processing on FDM component surface normal directed both to downward (build angle 0°–90°) and to upward (build angle 99°–180°) are reported for the first time in this article. The data reported for the post-processed FDM part at the build angle 0°–180° can be used as a guideline for selecting the optimal parameter and for assigning appropriate tolerance in the CAD model.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Many projects focus on the reliable operation of the activities in the project. Any failure in the process activities leads to major problems resulting in waste, defects, equipment damage, which has a direct impact on the consumers. In addition, Lean Six Sigma (LSS) is not new to this issue. LSS projects have faced an interruption in the process flow and unforeseen defects. Therefore, the purpose of this paper is to identify the vital failure factors of LSS projects.

Through extant literature review, the researchers found 44 critical failure factors (CFFs) of LSS. Using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) SIMOS approach, the decision makers’ (DMs) rating and weight for each factor were collected. Moreover, the study was conducted in both the manufacturing and service industries to identify the impact of CFFs in LSS projects.

CFFs and their evaluation have received little attention in the literature. Most of the previous studies deal only with the identification of the success factors in general. Therefore, the study identified 44 CFFs and ranked them through DMs. In addition, the TOPSIS SIMOS approach ranked the vital failure factors enabling the management to avert the LSS project from failures.

The study focused on project failures due to CFFs of LSS. Nevertheless, it did not consider other failure factors of project and knowledge management. Further, the sample used to test the approach was considerably small. Therefore, the study can be repeated with significant samples and the vital failure factors compared.

In real-life application, all the parameters in the LSS project need to be understood in a better manner. In such a condition, the practitioner needs to know that the project never fails due to the CFFs and TOPSIS SIMOS approach can prevent the LSS project failures.

The study applied TOPSIS SIMOS approach to rank the CFFs in an LSS project, which is first of its kind and aids the practitioners to make the right decisions in the business environment.