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Motivated by the acoustics of motor vehicles, a coupled fluid–solid system is considered. The air pressure is modeled by the Helmholtz equation, and the structure displacement is described by elastodynamic equations. The acoustic–structure interaction is modeled by coupling conditions on the common interface. First, the existence and uniqueness of solutions are investigated, and then, after recalling fundamental notions of shape optimization, the tensor form of the distributed shape derivative is obtained for the coupled problem. It is then applied to the minimization of the sound pressure by variation of the structure shape through the positioning of beads.

The existence and uniqueness of solutions up to eigenfrequencies are shown by the Fredholm–Riesz–Schauder theory using a novel decomposition into an isomorphism and a compact operator. For the design optimization, the distributed shape derivative is obtained using the averaged adjoint method. It is then used in a closed 3D optimization process of the position of a bead for noise reduction. In this process, the C++ library concepts are used to solve the differential equations on hexahedral meshes with the finite element method of higher order.

The existence and uniqueness of solutions have been shown for the case without absorption, where the given proof allows for extension to the case with absorption in the domain or via boundary conditions. The theoretical results show that the averaged adjoint can be applied to compute distributed shape derivatives in the context of acoustic–structure interaction. The numerical results show that the distributed shape derivative can be used to reduce the sound pressure at a chosen frequency via rigid motions of a nonsmooth shape.

The proof of shape differentiability and the calculation of the distributed shape derivative in tensor form allows to consider nonsmooth shapes for the optimization, which is particularly relevant for the optimal placement of beads or stampings in a structural-acoustic system.

The modeling and optimization of a weld bead in the middle of the weld are often simple, as the forming process is dynamically balanced. However, the arc striking (AS) and arc extinguishing (AE) areas of weld beads are generally abnormal because the dynamic processes at these areas are unstable. The purpose of this paper is to investigate the abnormal areas of the weld bead with optimization modeling methods in wire and arc additive manufacturing (WAAM).

A burning-back method was proposed to fill the slanted plane in the AE area. To optimize the welding parameters and obtain the optimal design, a response surface methodology was proposed to build the relationships between the input parameters and response variables.

The proposed burning-back method could fill the slanted plane in the AE area. Second-order models of abnormal areas were developed and the optimization effects were analyzed. The experimental results indicated that the relationship models at both ends were applicable and preferable for the optimization of weld beads.

In this paper, a burning-back method was proposed to optimize the slanted plane in the AE area. Second-order models of abnormal areas were established. The methods and models were preferable in the optimization of the abnormal areas in WAAM.

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.

In the presented research work, the authors fabricated the multiple MS plate (Grade: IS 2062) specimens and applied a novel integrated computational TR_IF_MR_G approach with grey relational analysis (GRA) toward solving weld bead optimization problem in MIG welding procedure. The objective of research is to determine the optimum setting between MIG welding input process parameters, e.g. welding current, open circuit voltage and thickness of plate in attaining high tensile strength with weld bead geometry quality characteristics, e.g. bead width, reinforcement, penetration and dilution in investigating define MS specimens.

The Taguchi's L₉ orthogonal array (OA) design is respected to conduct the experiments on MS plate specimens to attain output objectives. Later, the evaluated multiple output objectives are transformed into single response by applying a novel integrated computational TR_IF_MR_G approach with GRA. Thereafter, the outset of signal-to-noise ratio (S/N ratio) accompanied by ANOVA (Analysis of variance) is explored to optimize objective function.

The computed results are confirmed by conducting the experiments on same identical specimens. The outcome of the confirmation tests yielded an improvement of 0.24454, 0.372486, 0.686635 and 0.4106846 in grey relational grade (GRG), overall ratio index, reference grade and full multiplicative index, respectively, after validating the results.

In the presented work, the authors constructed a novel integrated computational TR_IF_MR_G approach via clustering GRA, overall ratio index (ORI), full multiplicative index (FMI) with GRA-reference grade (RG) and tested as well as applied with Taguchi concept to attain objective of the research work.

This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.

The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.

The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.

In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

This paper discusses the optimization of the process parameters for the hybrid‐layered manufacturing (HLM) process during its weld layer deposition with subsequent surface machining in attaining the desired accuracy and contour profile of the deposited weld layer thickness.

The HLM process integrates the synergic metal inert gas (MIG) – metal active gas (MAG) welding process for depositing the metal layer of a desired slice thickness and perform the computer numerical control (CNC) machining process on the deposited layer to enhance both the surface quality and dimensional accuracy of the deposited layer. For the HLM process the weld bead geometry plays a vital role in determination of the layer thickness, surface quality, build time, heat input into the deposited layer and the hardness attained by the prototype. A feasible weld bead width and heights are to be formulated for the exterior contour weld path deposition and for the interior weld cladding. Thus, Taguchi methodology was employed with minimum number of trails as compared with classical statistical experiments. This study systematically reveals the complex cause‐effect relationships between design parameters and performance.

Statistical design of experiments using orthogonal arrays and signal‐to‐noise (S/N) ratios are performed to constitute the core of the robust design procedure. Experimental confirmations of the performance characteristic using the derived optimal levels of process parameters are provided to confirm the effectiveness of this approach.

The welding parameters such as current, voltage, arc length, wire feed rates, wire stick‐out distance, shielding gas, filler wire diameter, weld speed, etc. will influence on the deposited weld bead geometry. Further investigations are to be carried out during adaptive layer deposition on the induced thermal stresses and its influence on the hardness of the deposited weld layer.

This paper describes a low cost direct rapid tooling process, HLM. This unique methodology would reduce the cost and time to make molds and dies that are used in batch production.

This paper presents calculation of the welding process parameters for obtaining optimal weld bead geometry in Flux Cored arc welding (FCAW) process. Bead on plate welding was carried as per L16 orthogonal array. In this paper weld bead geometry such as penetration, bead width, reinforcement and percentage of dilution of IS 2062 structural steel plates investigated. Two hybrid techniques firstly Taguchi method coupled with Grey relational analysis and secondly Taguchi method in combination with desirability function (DF) approach has been applied in this paper. Comparison made between two hybrid optimization techniques are made to analyze to choose the best method. Optimal results have been confirmed by confirmatory experiment which showed satisfactory results.

The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions.

Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches.

There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN.

Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

The purpose of the study reported in the paper is to apply a structured problem-solving approach based on the Lean approach to analyse weld defects and derive appropriate solutions.

Manufacturing organisations involved in welding fabrication are expected to reduce weld defects to attain competitive advantage. Weld defects need to be systematically analysed for valve performance enhancement. In this research study, Lean approach is used to reduce variations and waste by annihilating the root causes for failures that occur during submerged arc welding (SAW) process.

The deployment of solutions facilitated weld defect reduction and substantial financial savings for the organisation.

The framework has been test-implemented for analysing variations and wastes generated in the SAW process. In future, studies could be conducted for assessing different welding processes.

The proposed Lean framework has been successfully implemented in a large-scale manufacturing unit involved in fabrication work.

Lean framework has been test-implemented in a large-scale manufacturing organisation involved in weld fabrication work.

This paper aims to present an optimal trajectory planning for industrial MOTOMAN MA1440A gas metal arc welding system. A new and efficient evolutionary algorithm, enhanced multi-objective teaching learning-based optimization (EMOTLBO) method, i.e. TLBO with non-dominated sorting approach has been proposed to obtain the optimal joint trajectory for the defined weld seam path.

The joint trajectory of the welding robot need to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the weld path and for achieving higher positional accuracy. This can be achieved by limiting the kinematic and dynamic variations of the robot joints like joint jerks, squared acceleration and torque induced in the joints while travel of the robot along the weld path. Also, the robot travel should be done within minimum possible time for maintaining productivity. This leads to a multi-objective optimization problem which needs to be solved for maintaining proper orientation of the robot end effector. EMOTLBO has been proposed to obtain the Pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the Pareto front with best trade-off between objectives.

The proposed method has been implanted in MATLAB R2017a for simulation results. The joint positions have been used to program the robot for performing welding operation along the weld seam. From the simulation and experimental results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of MOTOMAN MA 1440 A arc welding robot system as a very smooth and uniform weld bead has been obtained with maximum weld quality.

In this paper, a novel approach for optimal trajectory planning welding arc robot has been performed. Though trajectory planning of industrial robots has been done before, it has not been done yet for welding robot. The objectives are formulated taking in consideration of requirement of welding process like minimization of joint jerks and torques induced during welding operation due to travel of robot with the effect of arc spatter, minimization of squared acceleration for maintaining constant joint velocity and finally minimization of total travel time for maintaining productivity.