Powder bed fusion (PBF) is recognized as one of the most common additive manufacturing technologies because of its attractive capability of fabricating complex geometries using many possible materials. However, the quality and reliability of parts produced by this technology are observed to be crucial aspects. In addition, the challenges of PBF-produced parts are hot issues among stakeholders because parts are still insufficient to meet the strict requirements of high-tech industries. This paper discusses the present state of the art in PBF and technological challenges, with a focus on selective laser melting (SLM). The review work focuses mainly on articles that emphasize the status and challenges of PBF metal-based AM, and the study is primarily limited to open-access sources, with special attention given to the process parameters and flaws as a determining factor for printed part quality and reliability. Moreover, the common defects due to an unstrained process parameter of SLM and those needed to monitor and sustain the quality and reliability of components are encompassed. From this review work, it has been observed that there are several factors, such as laser parameters, powder characteristics, material properties of powder and the printing chamber environments, that affect the SLM printing process and the mechanical properties of printed parts. It is also concluded that the SLM process is not only expensive and slow compared with conventional manufacturing processes, but it also suffers from key drawbacks, such as its reliability and quality in terms of dimensional accuracy, mechanical strength and surface roughness.

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.

Laser powder bed fusion (L-PBF) shows potential in metal additive manufacturing for producing complex components. However, achieving ideal hardness and minimizing porosity poses a significant challenge. This study explores the impact of part orientation, scanning methods, and hatching distance on the hardness and porosity of AlSi10Mg alloy produced through L-PBF. Utilizing a Box–Behnken design of experiments (DOE), cubic samples were systematically produced. Hardness was quantitatively assessed using Vickers hardness tests, while porosity measurements involved 2D image analysis of polished scanning electron microscopy (SEM) samples, the porosity percentages analyzed using ImageJ software. The results demonstrate that both scanning strategy and hatching distance significantly influence hardness and porosity. The spiral scanning pattern notably enhances hardness and reduces porosity. In contrast, the bidirectional scanning strategy results in lower hardness and more pronounced porosity formations. An inverse correlation between grain size distribution and hardness was observed, with finer grain sizes leading to higher hardness values, indicating that grain refinement improves mechanical properties. Additionally, a negative relationship between hardness and porosity was established, emphasizing the importance of minimizing porosity to enhance material hardness. These findings contribute to the overall understanding of the L-PBF additive manufacturing process, providing valuable insights for optimizing material properties and ensuring the mechanical integrity of high-performance L-PBF produced metal parts.