This study’s objective is to review the literature on the environmental impact of the additive manufacturing process. When this new manufacturing technology is employed, it aims to create a healthy environment free of pollutants. The work is motivated by the lack of universal guidelines on new design approaches, the classification of manufacturing materials, and processes that address environmental concerns. Using additive manufacturing over traditional subtractive technologies may result in considerable material and energy resource savings, especially if the component is appropriately designed for manufacture. In this scenario, additive manufacturing, regarded as a potential breakthrough innovation, has grown in popularity in producing parts with complex geometry. AM encourages constant product development and flexible modifications that enable stakeholders to create better products faster. This study examines the state-of-the-art essentials of the fast-expanding manufacturing technique known as additive manufacturing (or 3D printing) and compares the environmental impact caused due to environmental issues. With increasing pressure on firms to provide transparency in their product sourcing and manufacturing processes, sustainability is no longer a distant goal but a strategic requirement. Manufacturers must also pay particular attention to their products’ total energy usage and overall environmental impact.

Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R² values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

Thin-walled structures are a significant and growing portion of engineering construction, with a wide range of applications, including storage vessels, industrial buildings, warehouses, aircraft, automobiles, bridges, ships, and oil rigs. Thin-walled components with minimum thickness without compromising strength and other quality characteristics are the desire of modern industry. Reducing wall thickness not only aids in lowering the cost of production. It also improves the effectiveness of engineering systems, resulting in lower fuel consumption and lower emissions of hazardous gases to the environment. Nowadays, even though thin-walled parts are demanded, the constraints of the production process, quality, and reliability are the concerns of current research and development. The ability to produce parts with intricate geometries and tight dimensional tolerances are important criteria for advanced manufacturing processes. In the early days of society, investment casting was used to produce jewelry, weapons, and statues. In modern industry, investment casting is still used to produce thin-walled and intricate parts such as turbine blades. The current advancements in SLM, which has the capacity to produce thin-walled and intricate parts, have recently attracted attention due to several benefits, such as the supreme degree of design freedom and the viability of tool-free production directly from CAD data. However, the current technological applications of SLM and investment casting are crucial for producing parts at the desired quality and reliability. This review article focuses on comparative studies of SLM and investment casting at the current technology level. The basis of comparison via systematic approach is mechanical characterization; quality in terms of porosity, microstructure, surface roughness and dimensional accuracy; and residual stress. Therefore, the latest open scientific sources published are considered to obtain sufficient literature coverage. Better tensile strength and fine microstructure are found in SLM, while better surface quality, fatigue load resistance, ductility, and residual stress are found in investment casting. The research gap for further investigation is indicated.

Friction Stir Welding (FSW) is a process of welding materials that generates heat through friction. Plastic deformation, nonlinear material movement, tool-to-structural evolution friction, and heat production from friction and plastic deformation all have an impact on FSW operation. In this paper, thermo-mechanical characteristics of aluminum alloy AA6061-T6 during the FSW process were simulated based on COMSOL® software using a finite element approach. A conceptual model was created to interpret the thermal and structural analyses. According to the obtained results, the temperature rises on the top and bottom surfaces as the axial force increases but decreases along the line perpendicular to the weld direction. The overall temperature decreases as the forward welding speed rises within the acceptable induced temperature range of the workpiece, while the axial force and rotational speeds stay stable.

Aluminum alloy is the second most abundant metal on Earth, known for its wide range of utilization in commercial goods due to its heat capacity and tensile strength. This study examines the effect of nose radius on the turning process. Further, it explores the implications of cutting parameters such as the cutting speed, the rate of feed, the cutting depth, and the nose radius of the tool. The trials were carried out with an Al 6061 workpiece and an Al₂O₃-coated carbide tool as the cutter, utilizing the response surface methodology. A mathematical model was developed to investigate the performance characteristics of the turning operation using the analysis of variance method. The multi-response desirability function analysis combines individual desirability values to create a composite desirability value. The ideal parameter levels were determined using the composite desirability value, and the significant influence of parameters was assessed. The obtained optimum surface roughness and temperature parameters are at a cutting speed of 116.37 m/min, a rate of feed of 0.408 mm/rev, a cutting depth of 0.538 mm, and a tool nose radius of 0.20 mm. The related ideal surface roughness and temperature values are 0.374 µm and 27.439 °C. The optimal overall desirability value is 0.829, close to the target response.

Background: The objective of this literature review is to systematically explore the supply chain (SC)-related issues of additive manufacturing (AM)-based production processes. For SC sustainability, efficiency, and performance improvements, the adoptation of disruptive technologies like AM plays a vital role, because the product’s SC benefits in terms of reduced total lead time and costs. Methods: To explore the state-of-the-art influences of AM on the SC in this study, 978 papers published in peer-reviewed journals from 2004 to 2023 were retrieved, and 70 of these were identified as the most relevant and then reviewed. Results: As an outcome, the results of this review paper indicated a lack of documented studies in developing countries and, as a result, limited research works, for instance, in fashion industries were observed. In addition, AM best practices in the SC context have been identified and categorized as cost-related, time-related, inventor-related, as well as energy-, waste-, and environment-related factors, SC efficiency factors, and flexibility, marketing, and manufacturing-related factors. Conclusions: By identifying these categories, the study aims to contribute to the efforts of transforming traditional manufacturing into AM-based processes, for which a framework for the AM SC implementation is developed. In summary, the systematic review indicated that further research work is needed on the impacts of the identified AM best practices on SC performance.