The biomaterials polylactic acid (PLA), polycaprolactone (PCL), and hydroxyapatite (HA) were selected to fabricate composite filaments for 3D printing fused filament fabrication (FFF), which was used to fabricate a composite biomaterial for an interlocking nail for canine diaphyseal fractures instead of metal bioinert materials. Bioactive materials were used to increase biological activities and provide a high possibility for bone regeneration to eliminate the limitations of interlocking nails. HA was added to PLA and PCL granules in three ratios according to the percentage of HA: 0%, 5%, and 15% (PLA/PCL, PLA/PCL/5HA, and PLA/PCL/15HA, respectively), before the filaments were extruded. The test specimens were 3D-printed from the extruded composite filaments using an FFF printer. Then, a group of test specimens was coated by silk fibroin (SF) using the lyophilization technique to increase their biological properties. Mechanical, biological, and chemical characterizations were performed to investigate the properties of the composite biomaterials. The glass transition and melting temperatures of the copolymer were not influenced by the presence of HA in the PLA/PCL filaments. Meanwhile, the presence of HA in the PLA/PCL/15HA group resulted in the highest compressive strength (82.72 ± 1.76 MPa) and the lowest tensile strength (52.05 ± 2.44 MPa). HA provided higher bone cell proliferation, and higher values were observed in the SF coating group. Therefore, FFF 3D-printed filaments using composite materials with bioactive materials have a high potential for use in fabricating an interlocking nail for canine diaphyseal fractures.

The full-thickness articular cartilage defect (FTAC) is an abnormally severe grade of articular cartilage (AC) injury. An osteochondral autograft transfer (OAT) is the recommended treatment, but the increasing morbidity rate from osteochondral plug harvesting is a limitation. Thus, the 3D-printed bilayer’s bioactive-biomaterials scaffold is of major interest. Polylactic acid (PLA) and polycaprolactone (PCL) were blended with hydroxyapatite (HA) for the 3D-printed bone layer of the bilayer’s bioactive-biomaterials scaffold (B-BBBS). Meanwhile, the blended PLA/PCL filament was 3D printed and combined with a chitosan (CS)/silk firoin (SF) using a lyophilization technique to fabricate the AC layer of the bilayer’s bioactive-biomaterials scaffold (AC-BBBS). Material characterization and mechanical and biological tests were performed. The fabrication process consists of combining the 3D-printed structure (AC-BBBS and B-BBBS) and a lyophilized porous AC-BBBS. The morphology and printing abilities were investigated, and biological tests were performed. Finite element analysis (FEA) was performed to predict the maximum load that the bilayer’s bioactive-biomaterials scaffold (BBBS) could carry. The presence of HA and CS/SF in the PLA/PCL structure increased cell proliferation. The FEA predicted the load carrying capacity to be up to 663.2 N. All tests indicated that it is possible for BBBS to be used in tissue engineering for AC and bone regeneration in FTAC treatment.

Post weld heat treatment, or PWHT, is often used to improve the mechanical properties of materials that have been welded. Several publications have investigated the effects of the PWHT process using experimental designs. However, the modeling and optimization using the integration of machine learning (ML) and metaheuristics have yet to be reported, which are fundamental steps toward intelligent manufacturing applications. This research proposes a novel approach using ML techniques and metaheuristics to optimize PWHT process parameters. The goal is to determine the optimal PWHT parameters for both single and multiple objective perspectives. In this research, support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), and random forest (RF) were ML techniques employed to obtain a relationship model between PWHT parameters and mechanical properties: ultimate tensile strength (UTS) and elongation percentage (EL). The results show that the SVR demonstrated superior performance among ML techniques for both UTS and EL models. Then, SVR is used with metaheuristics such as differential evolution (DE), particle swarm optimization (PSO), and genetic algorithms (GA). SVR-PSO shows the fastest convergence among other combinations. The final solutions of single-objective and Pareto solutions were also suggested in this research.

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability.

This research was aimed at developing a dental prototype from 3D printing technology using a synthetic filament of polylactic acid (PLA) and zirconium dioxide (ZrO₂) with glycerol and silane coupling agent as a binder. A face-centered central composite design was used to study the effects of the filament extrusion parameters and the 3D printing parameters. Tensile and compressive testing was conducted to determine the stress-strain relationship of the filaments. The yield strength, elongation percentage and Young’s modulus were also calculated. Results showed the melting temperature of 193 °C, ZrO₂ ratio of 17 wt.% and 25 rpm screw speed contributed to the highest ultimate tensile strength of the synthetic filament. A Nozzle temperature of 210 °C and an infill density of 100% had the most effect on the ultimate compressive strength whilst the printing speed had no significant effects. Differential scanning calorimetry (DSC) was used to study the thermal properties and percentage of crystallinity of PLA filaments. The addition of glycerol and a silane coupling agent increased the tensile strength and filament size. The ZrO₂ particles induced the crystallization of the PLA matrix. A higher crystallization was also obtained from the annealing treatment resulting in the greater thermal resistance performance of the dental crown prototype.

The aim of this research is to investigate the sequence of processes for improving the welded surface integrity of AA7075-T651 aluminum alloy joined by friction stir welding (FSW). The improvement processes that will be investigated herein include mechanical surface improvement with deep rolling (DR) and post-weld heat treatment (PWHT). Therefore, this study investigated welded surface integrity, which comprises residual stress, microhardness, surface roughness, microstructure, and fatigue life (screening). The experiment consists of three sets of combinations. In the first set, only FSW was applied; in the second set, FSW was applied, followed by DR, and then PWHT processes (FSW-DR-PWHT); and in the last set, FSW was applied, followed by PWHT, and then DR processes (FSW-PWHT-DR). Fatigue testing was carried out by undertaking a four-point bending test using a bending stress of approximately 300 MPa with a test frequency of 2.5 Hz at room temperature and stress ratio R = 0. The study found that residual stress plays an important role in the fatigue life. Finally, the fatigue test showed that FSW workpieces subject to the PWHT process followed by the DR process (FSW-PWHT-DR) had the highest fatigue life, with an increase of 239% when compared with unprocessed FSW workpieces.