Search results

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

Welding is a widely used manufacturing process in many industries. The process consumes a lot of energy and resources, pollutes the environment, and emits gases and fumes into the atmosphere that are dangerous to human health. There are various welding processes, and the suitable welding process is usually chosen based on cost, material, and conditions. Subjectivity is the most significant impediment to selecting an optimal process. As a result, it is critical to develop the appropriate set of criteria, use the best tool and methodology, and collect sufficient data. This study examines the sustainability of welding processes and their environmental impact.

The welding process’s sustainability was examined and discussed in general, considering the technological specifics of each welding process, physical performance, and environmental, economic, and social effects. The study investigates the environmental impact of MMAW, GMAW, and GTAW/GMAW processes through experimental work and LCA methodology.

MMAW is the most environmentally harmful technology, whereas GMAW has the least impact. The GTAW/GMAW process outperformed the other processes in terms of yield stress, but the analyses revealed that it had a greater environmental impact than GMAW.

The study provides an environmental impact summary and demonstrates the effects of welding parameters and processes. This gives users an understanding of choosing the best welding technique or making the process more environmentally friendly. These recommendations help policymakers identify hot spots and implement the right plans to achieve more sustainable manufacturing.

Wire arc additive manufacturing (WAAM) is one of the most researched and fastest-growing AM technique because of its capability to produce larger components with medium complexity. In recent times, the use of WAAM process has been increased because of its ability to produce complex components economically when compared with other AM techniques. The purpose of this study is to investigate the capabilities of wire arc additive manufacturing (WAAM), which has emerged as a recognized method for fabricating larger components with complex geometries.

This paper provides a review of process parameters for optimizing and analyzing mechanical properties, hardness, microstructure and corrosion behavior achieved through various WAAM-based techniques.

Limited analysis exists regarding the mechanical properties of various orientations of Inconel 625 alloy. Moreover, there is a lack of studies concerning the corrosion behavior of Inconel 625 alloy fabricated using WAAM.

The review identifies that the formation of intermetallic phases reduces the desirability of mechanical properties and corrosion resistance of WAAM-fabricated Inconel 625 alloy. Additionally, the study reported notable results obtained by various research studies and the improvements to be achieved in the future.

The paper's goal is to examine and illustrate the useful uses of submodeling in finite element modeling for topology optimization and stress analysis. The goal of the study is to demonstrate how submodeling – more especially, a 1D approach – can reliably and effectively produce ideal solutions for challenging structural issues. The paper aims to demonstrate the usefulness of submodeling in obtaining converged solutions for stress analysis and optimized geometry for improved fatigue life by studying a cantilever beam case and using beam formulations. In order to guarantee the precision and dependability of the optimization process, the developed approach will also be validated through experimental testing, such as 3-point bending tests and 3D printing. Using 3D finite element models, the 1D submodeling approach is further validated in the final step, showing a strong correlation with experimental data for deflection calculations.

The authors conducted a literature review to understand the existing research on submodeling and its practical applications in finite element modeling. They selected a cantilever beam case as a test subject to demonstrate stress analysis and topology optimization through submodeling. They developed a 1D submodeling approach to streamline the optimization process and ensure result validity. The authors utilized beam formulations to optimize and validate the outcomes of the submodeling approach. They 3D-printed the optimized models and subjected them to a 3-point bending test to confirm the accuracy of the developed approach. They employed 3D finite element models for submodeling to validate the 1D approach, focusing on specific finite elements for deflection calculations and analyzed the results to demonstrate a strong correlation between the theoretical models and experimental data, showcasing the effectiveness of the submodeling methodology in achieving optimal solutions efficiently and accurately.

The findings of the paper are as follows: 1. The use of submodeling, specifically a 1D submodeling approach, proved to be effective in achieving optimal solutions more efficiently and accurately in finite element modeling. 2. The study conducted on a cantilever beam case demonstrated successful stress analysis and topology optimization through submodeling, resulting in optimized geometry for enhanced fatigue life. 3. Beam formulations were utilized to optimize and validate the outcomes of the submodeling approach, leading to the successful 3D printing and testing of the optimized models through a 3-point bending test. 4. Experimental results confirmed the accuracy and validity of the developed submodeling approach in streamlining the optimization process. 5. The use of 3D finite element models for submodeling further validated the 1D approach, with specific finite elements showing a strong correlation with experimental data in deflection calculations. Overall, the findings highlight the effectiveness of submodeling techniques in achieving optimal solutions and validating results in finite element modeling, stress analysis and optimization processes.

The originality and value of the paper lie in its innovative approach to utilizing submodeling techniques in finite element modeling for structural analysis and optimization. By focusing on the reduction of finite element models and the creation of smaller, more manageable models through submodeling, the paper offers designers a more efficient and accurate way to achieve optimal solutions for complex problems. The study's use of a cantilever beam case to demonstrate stress analysis and topology optimization showcases the practical applications of submodeling in real-world scenarios. The development of a 1D submodeling approach, along with the utilization of beam formulations and 3D printing for experimental validation, adds a novel dimension to the research. Furthermore, the paper's integration of 1D and 3D submodeling techniques for deflection calculations and validation highlights the thoroughness and rigor of the study. The strong correlation between the finite element models and experimental data underscores the reliability and accuracy of the developed approach. Overall, the originality and value of this paper lie in its comprehensive exploration of submodeling techniques, its practical applications in structural analysis and optimization and its successful validation through experimental testing.

This study aims to assess the pitting resistance of austenitic stainless steel welded joints fusion zone (FZ) with high density of inclusions before and after surface treatment, including potentiostatic pulse technique (PPT) and pickling.

The potentiodynamic polarization tests and critical pitting temperature tests were carried out for estimating pitting resistance. The PPT and pickling were performed as surface treatment. Scanning electron microscope (SEM) and energy dispersive spectrometer were used for characterize the microstructure and elemental distribution. Electron back-scattered diffraction (EBSD) was used to assess the portion of phases and morphology of grains.

The weld metal exhibits a higher degree of alloying compared to the base metal, and it contains d-phase and sulfur-containing inclusions. Sulfur-containing inclusions serve as initiation sites for pitting, and they diminish the pitting resistance of weld metal. Both PPT and pickling can remove sulfur-containing inclusions, but PPT causes localized dissolution of the weld metal matrix around the inclusions, while pickling does not. Because of the high density of inclusions, certain pits initiated by PPT are significantly deeper, which makes the formation of stable pitting easier. Because of the high density of inclusions, certain pits initiated by the PPT are deeper. This characteristic facilitates the progression of these initial defects into fully developed, stable pits.

Analysis of pitting initiation in shielded metal arc welding FZ with PPT and ex situ SEM tracking observation. Explanation of why the PPT surface treatment is not able to enhance the pitting resistance of stainless steel with a high inclusion density.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Aluminium alloys can be used as lightweight and high-strength materials in combination with the technology of laser beam welding, an efficient joining method, in the manufacturing of automotive parts. The purposes of this paper are to conduct laser welding experiments with Al2024 in the lap joint configuration, model the laser welding process parameters of Al2024 alloys and use propounded models to optimize the process parameters.

Laser welding of Al2024 alloy has been conducted in the lap joint configuration. Then, the influences of explanatory variables (laser peak power, scanning speed and frequency) on outcome variables (weld width [WW], throat length [TL] and breaking load [BL]) have been investigated with Poisson regression analysis of the data set derived from experimentation. Thereafter, a multi-objective genetic algorithm (MOGA) has been used using MATLAB to find the optimum solutions. The effects of various input process parameters on the responses have also been analysed using response surface plots.

The promulgated statistical models, derived with Poisson regression analysis, are evinced to be well-fit ones using the analysis of deviance approach. Pareto fronts have been used to demonstrate the optimization results, and the maximized load-bearing capacity is computed to be 1,263 N, whereas the compromised WW and TL are 714 µm and 760 µm, respectively.

This work of conducting laser welding of lap joint of Al2024 alloy incorporating the Taguchi method and optimizing the input process parameters with the promulgated statistical models proffers a neoteric perspective that can be useful to the manufacturing industry.

The weld joint mechanical properties of friction stir welding (FSW) are majorly reliant on different input parameters of the FSW machine. The study and optmization of these parameters is uttermost requirement and aim of this study to increase the suitability of FSW in different manufacturing industries. Hence, the input parameters are optimized through different soft computing methods to increase the considered objective in this study.

In this research, ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) of FSW prepared butt joints of AA6061 and AA5083 Aluminium alloys materials are investigated as per American Society for Testing and Materials (ASTM E8-M04) standard. The FSW joints were prepared by changing the three input process parameters. To develop experimental run order design matrix, rotatable central composite design strategy was used. Furthermore, genetic algorithm (GA) in combination (Hybrid) with response surface methodology (RSM), artificial neural network (ANN), i.e. RSM-GA, ANN-GA, is exercised to optimize the considered process parameters.

The maximum value of UTS, YS and EL of test specimens on universal testing machine was measured as 264 MPa, 204 MPa and 14.41%, respectively. The most optimized results (UTS = 269.544 MPa, YS = 211.121 MPa and EL = 17.127%) are obtained with ANN-GA for the considered objectives.

The optimization of input parameters to increase the output objective values using hybrid soft computing techniques is unique in this research paper. The outcomes of this study will help the FSW using manufacturing industries to choose the best optimized parameters set for FSW prepared butt joint with improved mechanical properties.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

For the purpose of saving nickel, this study aims to develop new duplex stainless steel cored wires suitable for wire arc additive manufacturing (WAAM) with the addition of nitrogen.

The effect of nitrogen content on the microstructure and mechanical properties of the thin-walled deposits is investigated in detail.

The microstructure of thin-walled deposits mainly consists of austenite, ferrite and secondary austenite. With increasing nitrogen content, the austenite in the deposited metals increases. The austenite proportion in the bottom region is more than that in the top region of the deposited metals. The χ phase is randomly distributed at the grain boundaries and within ferrite. The σ phase is mainly precipitated at ferrite and austenite grain boundaries. With increasing nitrogen content, the tensile strength of the deposited metals increases, but the impact toughness of the deposited metals deteriorates.

This study proposes new duplex stainless steel cored wires for WAAM, which realizes the objective of saving nickel.