Van-Canh Nguyen

Wire arc additive manufacturing (WAAM) enables cost-effective fabrication of large metallic components, but the repeated thermal cycles during deposition strongly affect microstructure and mechanical properties. This study combined numerical simulation and experimental characterization to examine a 150 mm long, 60-layer ER100S-G steel wall produced by cold metal transfer (CMT)-WAAM. The simulation revealed pronounced heat accumulation: the interlayer temperature rose from about 75 °C to 680 °C and the cooling rate fell from ~266 °C/s to ~1.5 °C/s between the 2nd and 50th layers. The microstructure was predominantly ferritic-bainitic and coarsened along the build direction, evolving from fine acicular ferrite and bainite near the substrate to coarser polygonal and lath ferrite with bainite in the upper layers, with the average grain size increasing from ~3.40 to ~5.47 µm. Microhardness decreased from 237 to 200 HV0.1 (≈15.8%) from bottom to top, consistent with the coarsening. Tensile behavior was anisotropic: horizontal specimens (parallel to the deposition direction) reached 372 MPa yield strength, 542 MPa ultimate tensile strength, and 22% elongation, versus 343 MPa, 522 MPa, and 17% for vertical specimens. All the tensile specimens showed predominantly ductile fracture. Dry-sliding tests performed on a coupon extracted from the middle region gave a stable friction coefficient of about 0.51 and a wear rate of 1.08×10⁻4 mm3/(N·m). These results clarify how heat accumulation governs the microstructural gradients, the tensile anisotropy and the wear response of WAAM HSLA steel and inform thermal control and process optimization.

Dry hard turning of heat-treated SKD11 tool steel offers a sustainable alternative to grinding but poses significant challenges in maintaining surface integrity and tool life due to severe wear and high cutting temperatures. This study develops a physically interpretable, AI-assisted modeling framework that integrates empirical and machine learning methods to predict and optimize surface roughness ([ ]), tool wear ([ ]), and material removal rate (MRR). Seventeen controlled experiments were conducted under varying cutting speed ([ ]), feed rate ([ ]), and depth of cut ([ ]) conditions using CBN inserts (TNGA160404–CBN1020) in dry cutting. A hierarchical modeling approach was established, where [ ] acts as an intermediate variable linking process parameters to [ ], while MRR was computed analytically. The Response Surface Methodology (RSM) provided the baseline regression models with [ ] for [ ] and [ ] for [ ] when wear was included. Support Vector Regression (SVR) further enhanced prediction accuracy to [ ], demonstrating strong nonlinear capability and physical consistency. Multi-objective optimization using the MOPSO–TOPSIS framework achieved a balanced solution at [ ]–160[ ]m/min, [ ]–0.07[ ]mm/rev, and [ ]–0.15[ ]mm, resulting in approximately 10% reduction in surface roughness, 12% reduction in tool wear, and 8% improvement in productivity compared with baseline conditions. The proposed framework bridges physical modeling and AI-based prediction, offering a robust, explainable tool for adaptive control in precision hard turning and a reliable foundation for intelligent machining under limited-data environments.

This paper presents the optimization of the face milling process of the Ti-6Al-4V titanium alloy using the minimum quantity lubrication (MQL) technique. The main objectives are to improve machining efficiency, enhance surface quality, and minimize environmental impact. To achieve these goals, a combined approach using multiobjective particle swarm optimization (MOPSO) and a technique for order preference by similarity to ideal solution (TOPSIS) is proposed. This study focuses on five process parameters: air pressure, P (bar); oil flow rate, Q (ml/h); cutting speed, [ ](m/min); feed rate, [ ] (mm/tooth); and depth of cut, [ ] (mm). The objective is to optimize three key responses: surface roughness, [ ]([ ]m); cutting power, [ ](W); and material removal rate (MRR) (mm³/min). By applying the MOPSO–TOPSIS approach, the optimal cutting parameters were determined as follows: air pressure, P: 3.968 bar; oil flow rate, Q: 50[ ]ml/h; cutting speed, [ ]: 126.8843[ ]m/min; feed rate, [ ]: 0.0697[ ]mm/tooth; and depth of cut, [ ]: 0.8730[ ]mm. These optimized parameters resulted in a surface roughness, [ ], value of 0.3627[ ] [ ]m; cutting power, [ ], of 1772.74[ ]W; and MRR, of 1772.74[ ]mm³/min. This study significantly contributes to the field of machining optimization by presenting a comprehensive methodology for face milling of titanium alloys using the MQL technique. The combination of the MOPSO and TOPSIS methods offers a robust optimization framework that can be applied to similar machining scenarios, making it a valuable tool for manufacturers and researchers. Furthermore, compared with existing methods, the proposed methodology demonstrates superior performance in optimizing the face milling process of the Ti-6Al-4V titanium alloy. The MOPSO–TOPSIS approach provides a more efficient and accurate solution by simultaneously considering multiple objectives. The optimized parameters obtained in this study outperformed those in previous studies in terms of surface roughness, cutting power, and MRR. This study sets a new benchmark for future studies and opens new possibilities for further advancements in the field of machining optimization, particularly for titanium alloys.

This study explores the structural performance and optimization of 3D-printed Polylactic Acid (PLA) lattice structures, focusing on octapeak, hexstar, and dodecahedron designs, for potential load-bearing applications. Through compression testing, the load-displacement behavior of each structure type was analyzed, examining key characteristics such as peak load capacity, deformation patterns, and failure modes. The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was employed to assess each configuration based on two primary criteria: compressive strength and mass. This analysis yielded insights that were structure-type specific as well as overall across all nine samples. Among the hexstar configurations, sample 4 attained the highest rank due to its exceptional load-bearing capacity, making it the optimal choice for high-strength applications. Within the octapeak and dodecahedron groups, samples 2 and 7, respectively, demonstrated balanced performance, suitable for applications prioritizing mass efficiency over maximum strength. In the overall ranking, hexstar emerged as the top-performing structure, with its configurations consistently balancing strength and mass effectively, while octapeak and dodecahedron offered viable alternatives for lighter, less load-intensive uses. The findings demonstrate the utility of the PROMETHEE method in optimizing lattice structure configurations for specific engineering applications, thus contributing to the advancement of sustainable design in additive manufacturing. This research provides a framework for selecting 3D-printed structures that meet application-specific criteria for compressive strength and material efficiency.