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The purpose of this paper is to optimize the gas metal arc welding (GMAW) process input parameters simultaneously considering the multiple output variables (bead width (BW), bead height (BH) and depth of penetration (DP)).

Grey‐based Taguchi approach was used for designing the experiment, L27 orthogonal array was used which composed of three levels and 27 rows, which means that 27 experiments were carried out. Design of experiments was selected based on a four welding parameters with three levels each. The selected welding parameters for this paper are gas flow rate, voltage, travel speed and wire feed rate. The bead‐on‐plate welding trials are carried out on AISI 904L super austenitic stainless steel (SASS) sheets and evaluate the shape of the fusion zone depends upon a number of input parameters.

Bead‐on‐plate welding of 904L SASS sheet is successfully performed (without any cracks and discontinuity) by GMAW process and the bead profiles are measured. The predicted bead profiles have the better DP and lower BH and BW. It is found that the optimized setting values are improving the response values by 10 per cent.

The optimal welding conditions are identified in order to increase the productivity and minimize the total operating cost. The process input parameters effect is determined under the optimal welding combinations.

Friction welding (FW) is a solid state joining process. Super austenitic stainless steel is the preferable material for high corrosion resistance requirements. These steels are relatively cheaper than austenitic stainless steel and it is expensive than nickel base super alloys for such applications. The purpose of this paper is to deal with the optimization of the FW parameters of super austenitic stainless steel using artificial neural network (ANN) simulation and particle swarm optimization (PSO).

The FW experiments were conducted based on Taguchi L-18 orthogonal array. In FW, rotational speed, friction pressure, upsetting pressure and burn-off length are the important parameters which determine the strength of the weld joints. The FW trials were carried out on a FW machine and the welding time was recorded for each welding trial from the computerized control unit of the welding machine. The left partially deformed zone (L.PDZ) and right partially deformed zone (R.PDZ) were identified from the macrostructure and their values are considered for the output variables. The tensile test was carried out, and the yield strength and tensile strength of the joints were determined and their fracture surfaces were analyzed through scanning electron microscope (SEM).

The tensile test was carried out, and the yield strength and tensile strength of the joints were determined and their fracture surfaces were analyzed through SEM. An ANN was designed to predict the weld time, L.PDZ, R.PDZ and tensile strength of the joints accurately with respect to the corresponding input parameters. Finally, the FW parameters were optimized using PSO technique.

There is no limitations, difficult weld by fusion welding process material can easily weld by FW process.

The research work described in the paper is original.

Friction welding is a solid state bonding process, where the joint between two metals has been established without melting the metal. The relative motion between the faying surfaces (surfaces to be joined) under the application of pressure promotes surface interaction, friction and heat generation which subsequently results in joint formation. Stainless steel is an iron based alloy, contains various combinations of other elements to give desired characteristics, and found a wider range of applications in the areas such as petro‐chemical, fertilizer, automotive, food processing, cryogenic, nuclear and beverage sectors. In order to exploit the complete advantages of stainless steels, suitable joining techniques are highly demanded. The Friction welding is an easily integrated welding method of stainless steel, which considered as non‐weldable through fusion welding. Grain coarsening, creep failure and failure at heat‐affected zone are the major limitations of fusion welding of similar stainless steels. Friction welding eliminates such pitfalls. In the present work an attempt is made to investigate experimentally, the mechanical and metallurgical properties of friction welded joints, namely, austenitic stainless steel (AISI 304) and ferritic stainless steel (AISI 430). Evaluation of the characteristics of welded similar stainless steel joints are carried out through tensile test, hardness measurement and metallurgical investigations.

Friction stir welding (FSW), a process that involves joining of metals without fusion of filler materials. It is used already in routine, as well as critical application for the joining of structural components made of Aluminum and its alloys. Indeed it has been convincingly demonstrated that the process results in strong and ductile joints, some times in systems, which have proved difficult using conventional welding techniques. The process is most suitable for components that are flat & long (plates & sheets) but it can be adapted for pipes, hollow sections and positional welding. The welds are created by the combined action of frictional heating and mechanical deformation, due to a rotating tool. Recently, a new technology called friction stir spot welding (FSSW) has been developed that has a several advantages over the electric resistance welding process widely used in automotive industry in terms of weld quality and process efficiency. This welding technology involves a process similar to FSW, except that, instead of moving the tool along the weld seam, the tool only indents the parts, which are placed on top of each other. The conditions under which the deposition process in FSSW is successful are not fully understood. However, it is known that only under specific thermo‐mechanical conditions does a weld formation occur. The objective of the present work is to analyze the primary conditions under which the cavity behind the tool is filled. For this, a fully coupled thermo‐mechanical three‐dimensional FE model has been developed in ABAQUS/Explicit using the adaptive meshing scheme and the Johnson‐Cook material law. The contact forces are modeled by Coulomb’s law of friction, making the contact condition highly solution dependent. Temperature graph in the radial direction as well as stress, strain plots are presented.

This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.

To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.

The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.

The models are presented for static multiple crack growth problems. No material anisotropy is observed.

In practical crack-growth analyses, the NN approach provides significant benefits and savings.

The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

The purpose of this study is to optimize the process parameters (wire feed rate (F), voltage (V), welding speed (S) and torch angle (A)) in order to obtain the optimum bead geometry (bead width (W), reinforcement (R) and depth of penetration (P)), considering the ranges of the process parameters using evolutionary algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm.

The modeling of welding parameters in flux cored arc welding process using a set of experimental data and regression analysis, and optimization using GA and SA algorithm.

The adequate mathematical model was developed. The multiple objectives were optimized satisfactorily by the GA and SA algorithms. The feasible solution results are very closer to the optimized results and the percentage error was found to be negligibly small.

The optimal welding parameters were identified in order to increase the productivity. The welding input parameters effect was found.

There are many problems in civil or mechanical engineering related to structural design. In such a case, the solution techniques which lead to deterministic results are no longer valid due to the heuristic nature of design problems. The purpose of this paper is to propose a computational tool based on genetic algorithms, applied to the optimal design of cross-sections (solid tubes) of 3D truss structures.

The main feature of this genetic algorithm approach is the introduction of a selective-smart method developed in order to improve the convergence rate of large optimization problems. This selective genetic algorithm is based on a preliminary sensitivity analysis performed over each variable, in order to reduce the search space of the evolutionary process. In order to account for the optimization of the total weight, the displacement (of a specific section) and the internal stresses distribution of the structure a multiobjective optimization function was proposed.

The numerical results presented in this paper show a significant improvement in the convergence rate as well as an important reduction in the relative error, compared to the exact solution.

The variables sensitivity analysis put forward in this approach introduces a significant improvement in the convergence rate of the genetic algorithm proposed in this paper.

Connected machines are the automation of several types of machines connected towards beneficial growth of sustainable construction especially in the era of the Fourth Industrial Revolution. This chapter gave an insight into the emergence of digitalisation in the construction sector and the importance of connected machines in sustainable construction. It further elucidated its mode of operations, devices applicable, drivers and challenges for its full application in construction and benefits on construction projects. It finally gave a conclusion on its urgent need for full incorporation due to its technological benefits for present and future construction works.

This study aims to determine the near optimal welding process parameters (beam power (BP), travel speed (TS) and focal position (FP)) using grey relational analysis by simultaneously considering multiple output parameters (depth of penetration and bead width). Further, the optimized parameters were evaluated through the microstructural characterization and hardness measurements across the weld zone.

It is appropriate to apply Taguchi's technique to a complex system like welding process. Therefore, this study is made to determine the near optimal welding process parameters (BP, TS and FP) using grey relational analysis by simultaneously considering multiple output parameters (depth of penetration and bead width).

Taguchi experimental design for determining welding parameters was successful. The hardness of the Argon shielded weld metal was comparatively lesser than the Helium shielded weld metal. The Helium shielded weld metal microstructure comprises of finer grains and higher amounts of equiaxed grains. Argon and Helium shielded weld metal microstructure was endowed with a higher amount of secondary interdendritic austenite phase.

The optimal welding conditions were identified in order to increase the productivity and minimize the total operating cost. The process input parameters effect was determined under the optimal welding combinations.

The primary aim of this article is to ascertain the modalities of leveraging Lean Six Sigma (LSS) for Industry 4.0 (I4.0) with special reference to the process industries. Moreover, it intends to determine the applicability of simulation-based LSS in the automation of the mineral water industry, with special emphasis on the robust design of the control system to improve productivity and performance.

This study adopts the action research methodology, which is exploratory in nature along with the DMAIC (define-measure-analyze-improve-control) approach to systematically unearth the root causes and to develop robust solutions. The MATLAB simulation software and Minitab statistical software are effectively utilized to draw the inferences.

The root causes of critical to quality characteristic (CTQ) and variation in purity level of water are addressed through the simulation-based LSS approach. All the process parameters and noise parameters of the reverse osmosis (RO) process are optimized to reduce the errors and to improve the purity of the water. The project shows substantial improvement in the sigma rating from 1.14 to 3.88 due to data-based analysis and actions in the process. Eventually, this assists the management to realize an annual saving of 20% of its production and overhead costs. This study indicates that LSS can be applicable even in the advent of I4.0 by reinforcing the existing approach and embracing data analysis through simulation.

The limitation of this research is that the inference is drawn based on a single case study confined to process industry automation. Having said that, the methodology deployed, scientific information related to optimization, and technical base established can be generalized.

This article is the first of its kind in establishing the integration of simulation, LSS, and I4.0 with special reference to automation in the process industry. It also delineates the case study in a phase-wise manner to explore the applicability and relevance of LSS with I4.0. The study is archetype in enabling LSS to a new era, and can act as a benchmark document for academicians, researchers, and practitioners for further research and development.