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Laser surface polishing of material extrusion additively manufactured 316L stainless steel
(2026-02-01)
Hemwat, Jirayu
;
Saetang, Viboon
;
Qi, Huan
;
Seenawat, Mongkol
;
Chankitmunkong, Suwaree
Material extrusion additive manufacturing (MEX) provides a cost-effective pathway for fabricating metallic components; however, its industrial use remains limited by surface defects and high roughness. This study evaluates nanosecond laser polishing (LP) as a post-processing method to improve the surface characteristics of 316L stainless steel produced by Bound Metal Deposition (BMD). The objective is to understand how laser beam diameter, scan speed, and processing atmosphere (air vs. argon) influence surface integrity. Polishing experiments were performed using 50 W and 100 W laser power with beam diameters of 200 and 400 μm at scanning speeds of 100–400 mm/s. Areal roughness (S<inf>a</inf>), surface waviness (W<inf>a</inf>), surface chemistry, subsurface microstructure, and electrochemical response were systematically characterized. Laser polishing reduced S<inf>a</inf> from 2.003 μm to 0.371 μm (81 % reduction) and W<inf>a</inf> by up to 43 %. Polishing in argon produced cleaner melt tracks with minimal oxidation, a refined remelted layer, and enhanced passive film formation, leading to improved corrosion resistance (E<inf>corr</inf> improved from −0.466 V to −0.062 V). These findings demonstrate that LP effectively mitigates the surface limitations of BMD-fabricated stainless steel and provide process guidelines for achieving high-quality functional surfaces in MEX metal components.
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Towards the Design of Highly Heterogeneous Aluminum Alloys
(2025-01-01)
Eskin, Dmitry G.
;
Chankitmunkong, Suwaree
;
Zhu, Chengbo
Modern engineering applications require new types of alloys with a unique combination of properties. Alloys with the large volume fraction of intermetallic phases started to attract the attention of researchers, for example, eutectic alloys of the Al–Fe–Ni and Al–Ce–Ni systems. Such alloys have high elasticity modulus, thermal stability, hardness and high-temperature properties, however their applications is limited to casting and mostly hypoeutectic or eutectic alloys. Formation of primary intermetallics poses a challenge unless they can be efficiently refined. Here we present a number of approaches to refine the structure of hypereutectic Al Fe, Al–Ni, Al–Ce–Ni–Mn alloys through additions, ultrasonic processing (USP) and increased cooling rates. As a result, the structure of alloys makes them suitable for deformation and additive manufacturing with extra strength gained through dispersion hardening. These approaches may pave way to the new type of alloys for demanding applications.
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Adaptive Slicing of Point Cloud Directly with Discrete Interpolable-Area Error Profile in Additive Manufacturing
(2023-02-01)
Moodleah, Samart
;
Kirimasthong, Khwunta
Point cloud objects have gained popularity in three-dimensional (3D) printing recently due to advancements in reverse engineering technology. Fabricating an object with a fused deposition modeling (FDM) printer requires converting the object to layered contours, which involves a slicing process. The slicing process of a point cloud object usually requires reconstructing a 3D object from a point cloud, which requires users' deep understanding of 3D modeling software and a laborious work process. To avoid these problems, the direct slicing of point cloud objects is gaining more popularity. This research work proposes an adaptive slicing approach from point cloud objects directly without surface reconstruction. The adaptive slicing maintains the global geometry error while requiring a smaller number of fabrication layers and printing time. A new error profile used in the adaptive slicing approach is introduced. It approximates the geometry error from the point cloud directly based on the discrete interpolable-area (DIA) error between two adjacent layers. The interpolable capability of the DIA error profile allows the adaptive slicing algorithm to efficiently measure the geometry error of a point cloud. We perform the proposed algorithm with four point cloud models that represent both symmetrical and asymmetrical shapes. The adaptive slicing results show that the performance is increased by 8.05%-32.73% while maintaining accuracy compared to traditional uniform slicing. Furthermore, the fabrication time and materials used are reduced by 10.30%-39.10% and 1.01%-13.47%, respectively. Based on these results, further research can be focused on finding an optimal threshold between the accuracy of the contour projection and the distance between the layers, which could further improve fabrication performance.
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Prioritizing the Factors Affecting the Application of Industry 4.0 Technology in Electrical Appliance Manufacturing using a Fuzzy Analytical Network Process Approach
(2022-01-01)
Krommuang, Apiwat
;
Krommuang, Atchari
The fourth industrial revolution is a technological advancement that is posing new challenges in manufacturing and services. Industries must adopt innovations to create value-added for their products and services to gain a competitive advantage and increase production efficiency. Therefore, this research aims to study the factors that influence the application of Industry 4.0 technology for managing electrical appliance production by focusing on five major factors: the internet of things, cloud manufacturing, big data analytics, additive manufacturing, and cyber-physical systems, which can be further subdivided into 23 sub-factors. The fuzzy analytic network process (FANP) technique is used to prioritize the factors to develop criteria for selecting appropriate applications of Industry 4.0 technology in manufacturing. Besides, a questionnaire based on the FANP approach is used to collect data from 82 electrical appliance manufacturers to calculate the weight of each factor. Consequently, the Internet of Things is ranked first, followed by big data analytics and additive manufacturing. While the results have indicated the importance of sub-factors as data-driven, data collection, tracking, monitoring, and automation, respectively. The benefit of this research is that manufacturers of electrical appliances can use this research as a criterion for implementing Industry 4.0 technology for long-term effectiveness

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