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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.

Modeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the majority of the numerical investigations on the prediction of the deformation behavior of AA2024 alloy are limited to the use of phenomenological or empirical constitutive models, which fail to take into account the actual microscopic-level mechanisms (i.e. crystallographic slip) causing plastic deformation. In order to achieve accurate predictions, the microstructure-based constitutive models involving the underlying physical deformation mechanisms are more reliable. Therefore, the aim of this work is to predict the mechanical response of AA2024-T3 alloy subjected to uniaxial tension at different strain rates, using a dislocation density-based crystal plasticity model in conjunction with computational homogenization.

A dislocation density-based crystal plasticity (CP) model along with computational homogenization is presented here for predicting the mechanical behavior of aluminium alloy AA2024-T3 under uniaxial tension at different strain rates. A representative volume element (RVE) containing 400 grains subjected to periodic boundary conditions has been used for simulations. The effect of mesh discretization on the mechanical response is investigated by considering different meshing resolutions for the RVE. Material parameters of the CP model have been calibrated by fitting the experimental data. Along with the CP model, Johnson–Cook (JC) model is also used for examining the stress-strain behavior of the alloy at various strain rates. Validation of the predictions of CP and JC models is done with the experimental results where the CP model has more accurately captured the deformation behavior of the aluminium alloy.

The CP model is able to predict the mechanical response of AA2024-T3 alloy over a wide range of strain rates with a single set of material parameters. Furthermore, it is observed that the inhomogeneity in stress-strain fields at the grain level is linked to both the orientation of the grains as well as their interactions with one another. The flow and hardening rule parameters influencing the stress-strain curve and capturing the strain rate dependency are also identified.

Computational homogenization-based CP modeling and simulation of deformation behavior of polycrystalline alloy AA2024-T3 alloy at various strain rates is not available in the literature. Therefore, the present computational homogenization-based CP model can be used for predicting the deformation behavior of AA2024-T3 alloy more accurately at both micro and macro scales, under different strain rates.

Because of the mechanical properties of aluminium (Al), an accurate prediction of its properties has been challenging. Researchers are seeking reliable models for predicting the mechanical strength of Al alloys owing to the continuous emergence of new Al alloys and their applications. There has been widespread use of empirical and statistical models for the prediction of different mechanical properties of Al and Al alloy, such as linear and nonlinear regression. Nevertheless, the development of these models requires laborious experimental work, and they may not produce accurate results depending on the relationship between the Al properties, mix of other compositions and curing conditions.

Numerous machine learning (ML) models have been proposed as alternative approaches for predicting the strengths of Al and its alloys. The hardness of Al alloys has been predicted by implementing various ML algorithms, such as linear regression, ridge regression, lasso regression and artificial neural network (ANN). This investigation critically analysed and discussed the application and performance of models generated by linear regression, ridge regression, lasso regression and ANN algorithms using different mechanical properties as training parameters.

Considering the definition of the problem, linear regression has been found to be the most suitable algorithm in predicting the hardness values of AA7XXX alloys as the model generated by it best fits the data set.

The work presented in this paper is original and not submitted anywhere else.

The purpose of this paper is to compile a comprehensive status report on pipes/piping networks across different industrial sectors, along with specifications of materials and sizes, and showcase welding avenues. It further extends to highlight the promising friction stir welding as a single solid-state pipe welding procedure. This paper will enable all piping, welding and friction stir welding stakeholders to identify scope for their engagement in a single window.

The paper is a review paper, and it is mainly structured around sections on materials, sizes and standards for pipes in different sectors and the current welding practice for joining pipe and pipe connections; on the process and principle of friction stir welding (FSW) for pipes; identification of main welding process parameters for the FSW of pipes; effects of process parameters; and a well-carved-out concluding summary.

A well-carved-out concluding summary of extracts from thoroughly studied research is presented in a structured way in which the avenues for the engagement of FSW are identified.

The implications of the research are far-reaching. The FSW is currently expanding very fast in the welding of flat surfaces and has evolved into a vast number of variants because of its advantages and versatility. The application of FSW is coming up late but catching up fast, and as a late starter, the outcomes of such a review paper may support stake holders to expand the application of this process from pipe welding to pipe manufacturing, cladding and other high-end applications. Because the process is inherently inclined towards automation, its throughput rate is high and it does not need any consumables, the ultimate benefit can be passed on to the industry in terms of financial gains.

To the best of the authors’ knowledge, this is the only review exclusively for the friction stir welding of pipes with a well-organized piping specification detailed about industrial sectors. The current pipe welding practice in each sector has been presented, and the avenues for engaging FSW have been highlighted. The FSW pipe process parameters are characteristically distinguished from the conventional FSW, and the effects of the process parameters have been presented. The summary is concise yet comprehensive and organized in a structured manner.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

This study aims to outline the current challenges in ultrasonic additive manufacturing (AM). AM has revolutionized manufacturing and offers possible solutions when conventional techniques reach technological boundaries. Ultrasonic additive manufacturing (UAM) uses mechanical vibrations to join similar or dissimilar metals in three-dimensional assemblies. This hybrid fabrication method got attention due to minimum scrap and near-net-shape products.

This paper reviews significant UAM areas in process parameters such as pressure force, amplitude, weld speed and temperature. These process parameters used in different studies by researchers are compared and presented in tabular form. UAM process improvements and understanding of microstructures have been reported. This review paper also enlightens current challenges in the UAM process, process improvement methods such as heat treatment methods, foil-to-foil overlap and sonotrode surface roughness to increase the bond quality of welded parts.

Results showed that UAM could solve various problems and produce net shape products. It is concluded that process parameters such as pressure, weld speed, amplitude and temperature greatly influence weld quality by UAM. Post-weld heat treatment methods have been recommended to optimize the mechanical strength of ultrasonically welded joints process parameters. It has been found that the tension force is vital for the deformation of the pre-machined structures and for the elongation of the foil during UAM bonding. It is recommended to critically investigate the mechanical properties of welded parts with standard test procedures.

This study compiles relevant research and findings in UAM. The recent progress in UAM is presented in terms of material type, process parameters and process improvement, along with key findings of the particular investigation. The original contribution of this paper is to identify the research gaps in the process parameters of ultrasonic consolidation.

The purpose of this study is to investigate and evaluate the impact of varying proportions of reinforcement on the mechanical properties of a modified Al₂O₃-LM6 cast composite under self-pouring temperature conditions. This study aims to determine the optimal mixture proportion of fine powders of Al, Si and xAl₂O₃ (with x values of 2%, 3% and 4%) through the application of design of experiment (DoE) and statistical analysis using the Minitab software. This study also involved evaluating the microstructural estimation and other physical properties of the cast composite to understand the combined effect of the reinforcement proportion on the material’s properties.

The researchers initially mixed the powders through ball milling and then compacted the moisture-free powder mix in a closed steel die. The resulting preforms were heated at the self-pouring temperature in an inert environment to fabricate the final cast composite. By applying DoE and performing an analysis of variance (ANOVA), the researchers sought to optimize the mixture proportion that would yield the best mechanical properties.

The experimental results indicated that a mixture combination of 83.5% Al blended with 12.5% Si and 4% Al₂O₃ led to the greatest improvement in mechanical properties, specifically in terms of increased density, hardness and impact strength. The ANOVA further supported the interaction effect of each processing parameter on the observed results. The results of this study offer valuable insights for the fabrication of modified Al₂O₃-LM6 cast composites under self-pouring temperature conditions. The identified optimal mixture proportion provides guidance for manufacturing processes and material selection to achieve improved mechanical properties in similar applications.

This study focuses on a specific composite material consisting of modified Al₂O₃ and LM6. Although Al₂O₃ and LM6 have been studied individually in various contexts, the combination of these materials and their impact on mechanical properties under self-pouring temperature conditions is a novel aspect of this research. The researchers use DoE methodology, along with statistical analysis using Minitab software, to optimize the mixture proportion and analyze the data. This systematic approach allows for a comprehensive exploration of the parameter space and the identification of significant factors that influence the mechanical properties of the composite.

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 reduce the weight of the door, improve the operating efficiency and ensure the safety of vehicle operation.

Based on traditional aluminium alloy doors, a new type of honeycomb composite material was developed. Tests were conducted to determine the honeycomb compression resistance, honeycomb and skin shear performance, plate bending, thermal conductivity and environmental protection. Eight doors were developed based on the full-side open structure, and static strength and stiffness analyses were performed simultaneously. To solve door vibration problems, modal analysis and test were carried out.

The test results showed that the weight of the door was reduced by more than 40% whilst ensuring the strength and stiffness of the vehicle. The first–sixth-order test mode of the door was increased by more than 14% compared with existing aluminium alloy doors.

A new type of honeycomb composite material was used in this study. The test results showed that the weight of the door was reduced by more than 40% whilst ensuring the strength and stiffness of the vehicle. The 1st-to-6th order test mode of the door was increased by more than 14% compared with the existing aluminium alloy door.

The conflicting results on the corrosion characteristics of aluminium matrix composites reinforced with agrarian waste have stimulated an investigation on the hardness and corrosion rate of sugar palm fibre ash (SPFA) reinforced LM26 Al/alloy composite by varying the SPFA from 0 to 10 wt% in an interval of 2 wt%. This paper aims to discuss the aforementioned issue.

The composites were produced via stir-casting and the hardness was determined using a Vickers hardness testing machine, corrosion rate was examined through the weight loss method by immersion in 0.5, 1.0 and 1.5 M hydrochloric acid (HCl) at temperatures of 303, 318, and 333 K for the maximum duration of 120 h. The morphological study was conducted using a scanning electron microscope (SEM) on the samples before and after immersion in HCl.

The incorporation of SPFA improved the hardness of the alloy from 58.22 to 93.62 VH after 10 wt% addition. The corrosion rate increases with increased content of SPFA, the concentration of HCl and temperature. The least corrosion rate of 0.0272 mpy was observed for the LM26 Al alloy in 0.5 M after 24 h while the highest corrosion rate of 0.8511 mpy was recorded for LM26 Al/10 wt% SPFA in 1.5 M HCl acid after 120 h. The SEM image of corroded samples revealed an increased number of pits with increased SPFA content.

The work is limited to SPFA up to 10 wt% as reinforcement in LM26 Al alloy, the use of HCl as corrosion medium, temperatures in the range of 303–333 K, and a weight loss method were used to evaluate the corrosion rate.

The corrosion rate was determined for LM26 Al/SPFA composites with various amounts of SPFA in 0.5, 1.0 and 1.5 M HCl at 303, 318 and 333 K and compared with the matrix alloy.