Search Results (291)

The reliable joining of dissimilar stainless steel and carbon steel remains a critical challenge in Metal Inert Gas (MIG) welding due to complex thermal–metallurgical interactions and the formation of brittle phases at the weld interface. In this study, a Taguchi-based design of experiments was employed to systematically optimize MIG welding parameters for SUS201/S20C dissimilar joints using a SUS201 filler wire, with particular attention to the welding current, voltage, travel speed, and electrode stick-out. The welding process was performed using an automatic welding robot. Tensile specimens were tested on a universal testing machine. Microstructural analysis was performed using a metallurgical microscope. The microstructure reveals that the development of the carbon side’s large ferrite and the stainless steel side’s δ-ferrite both significantly degrade joint quality. Among all process parameters, electrode stick-out is identified as the most influential parameter governing both tensile and bending performance, highlighting a critical process sensitivity that has received limited attention in prior studies. Optimized parameter combinations are required to maximize tensile and flexural responses. The highest tensile strength is 450.96 MPa. These findings advance the understanding of parameter–microstructure–property relationships in dissimilar MIG welding. Future work applying numerical welding simulations and advanced evaluation techniques is recommended. Full article

The rising global demand for prosthetic limbs, driven by approximately 185,000 amputations annually in the United States, underscores the need for innovative and cost-efficient solutions. This study explores the integration of hybrid materials, advanced 3D printing techniques, and smart sensing technologies to enhance prosthetic finger production. A Taguchi-based design of experiments (DoE) approach using an L09 orthogonal array was employed to systematically evaluate the effects of infill density, infill pattern, and print speed on the tensile behavior of FDM-printed PLA components. Findings reveal that higher infill densities (90%) and hexagonal patterns significantly enhance yield strength, ultimate tensile strength, and stiffness. Additionally, the rheological properties of polydimethylsiloxane (PDMS) were optimized at various temperatures (30–70 °C), characterizing its viscosity, shear-thinning factors, and stress behaviors for 3D bioprinting of flexible sensors. Barium titanate (BaTiO₃) was incorporated into PDMS to fabricate a flexible tactile sensor, achieving reliable open-circuit voltage readings under applied forces. Structural and functional components of the finger prosthesis were fabricated using FDM, stereolithography (SLA), and extrusion-based bioprinting (EBP) and assembled into a functional prototype. This research demonstrates the feasibility of integrating hybrid materials and advanced printing methodologies to create cost-effective, high-performance prosthetic components with enhanced mechanical properties and embedded sensing capabilities. Full article

Defects in printed circuit boards (PCBs), if not detected promptly, may persist over time until they cause the failure of critical components. Traditional monitoring methods, which are limited to simulations or superficial measurements, obstruct predictive maintenance and real-time fault detection. To address these issues and enhance real-time diagnostics of thermal anomalies in PCBs, this work proposes an integrated system that combines infrared thermography (IRT), artificial intelligence (AI) algorithms, and Taguchi–ANOVA statistical techniques. IR thermography was employed to identify thermal stresses in the devices during normal operation. The IR acquisitions were used to build a dataset for specialized AI model’s training, which combines thermal anomalies segmentation using U-Net with a Multilayer Perceptron (MLP) classifier for heat distribution patterns. The Taguchi method determines the optimal configuration of the selected parameters, while Analysis of Variance (ANOVA) evaluates the effect of each factor on the F1-score response. These techniques statistically validated the AI performance, confirming the optimal set of selected hyperparameters and quantifying their contribution to F1-score. The novelty of the study lies in the integration of real-time infrared thermography with an interpretable AI pipeline and a Taguchi–ANOVA statistical framework, which enables both optimisation and rigorous validation of AI performance under real-time operating conditions. Full article

This study focuses on improving the fatigue strength and overall performance of sustainable biopolymer polylactic acid (PLA) components manufactured via Fused Deposition Modelling (FDM) additive manufacturing process. PLA, as a biodegradable and renewable polymer derived from natural resources, represents a promising alternative to conventional petroleum-based plastics in engineering and research applications. The influence of key FDM process parameters—layer height, infill density, and number of perimeters—on critical performance indicators such as filament consumption, printing time, and fatigue strength (number of cycles to failure) was systematically analyzed using the Taguchi L9 orthogonal array. Subsequently, Grey Relational Analysis (GRA) was applied as a multi-objective optimization technique to identify the parameter settings that achieve an optimal balance between mechanical durability and resource efficiency. The obtained results demonstrate that a proper combination of process parameters can significantly enhance the mechanical reliability and sustainability profile of FDM-printed PLA parts, contributing to the broader adoption of eco-friendly materials in additive manufacturing. Full article

This study presents the development and validation of an in situ monitoring method for the laser direct energy deposition (DED) process, utilizing an integrated optical camera (720 HD, 60 fps) to analyze melt pool imagery. The approach is grounded in an experimental framework employing Taguchi orthogonal arrays, which ensures a stable dataset by controlling process variability and enabling reliable extraction of relevant features. The monitoring system focuses on analyzing brightness distribution regions within the melt pool image, identified as specific clusters that reflect external process conditions. The method emphasizes precise segmentation of the melt pool area, combined with automatic detection and classification of cluster features associated with key process parameters—such as focus distance, the number of deposited layers, powder feed rate, and scanning speed. The main contribution of this work is demonstrating the effectiveness of using an optical camera for DED monitoring, based on an algorithm that processes a set of melt pool identification features through computer vision and machine learning techniques, including Random Forest and HistGradient Boosting, achieving classification accuracies exceeding 95%. By continuously tracking the evolution of these features within a closed-loop control system, the process can be maintained in a stable, defect-free state, effectively preventing the formation of common process defects. Full article

Flourensia cernua, commonly known as hojasén, is an endemic species from northern Mexico used in herbal medicine as a remedy for various stomach and respiratory ailments. In addition to its medicinal applications, this plant has notable antioxidant potential, making it a promising area of study. A crucial aspect in plant studies is the extraction method used, as conventional approaches can diminish the bioactivities present and affect the environment. This study aims to compare two sustainable extraction techniques, ultrasound and microwave-assisted (UAE/MAE), to maximize the yield of polyphenolic compounds from F. cernua. The Taguchi L18 orthogonal array was employed to evaluate total polyphenols and to examine independent variables, such as solvent concentration, temperature, and time. Additionally, the total flavonoid content and antioxidant activity were evaluated using the radicals ABTS^●+ and DPPH^●, and the compounds were identified using RP-HPLC-ESI-MS. The results indicated that ultrasound showed better performance in recovering total bioactive compounds, correlating with antioxidant activity. Moreover, the in vitro, hemolytic, and antihemolytic assays demonstrated that F. cernua extracts are biocompatible and exhibit significant protective activity against oxidative damage in erythrocytes, supporting their potential cytoprotective and antioxidant properties. This suggests that ultrasound-assisted extraction (UAE) is an effective method for extracting phenolic compounds from F. cernua, with potential for optimizing conditions and facilitating biotechnological and therapeutic applications. Full article

In recent years, unconventional machine techniques have made the machining process simpler than it was under conventional machining methods. EDM is recognized as one of the leading methods in unconventional machining processes. The need for materials with improved mechanical properties continues to rise due to constant advancements in the mechanical industry. Cryogenic treatment is used for property enhancement and can be useful in an extensive range of metals. This research investigates the performance of a cryogenically treated copper electrode during EDM of Monel 400. The EDM parameters varied during the research are pulse on time, pulse off time, gap voltage, and discharge current. The experiments were designed using Taguchi’s design of experiment. The constraints of the process are fine-tuned for both MRR and surface smoothness, with their proportion impacts assessed through the ANOVA technique. Regression analysis is accomplished, creating an experimental correlation between both MRR and surface smoothness, examined using RSM technique. This comprehensive study demonstrates that cryogenic treatment of electrode provides better MRR and SR. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al₂O₃ nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al₂O₃-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Full article

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (T_on) and pulse off time (T_off) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B₄C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R²) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors. Full article

Due to the exceptional corrosion resistance, chemical stability, and dense microstructure, carbon-based thin films are extensively employed in hydrogen energy systems. This study employed magnetron sputtering to fabricate amorphous carbon (a-C) films on SS316L substrates, aiming to improve the corrosion resistance of bipolar plates (BPs) in proton exchange membrane fuel cells (PEMFCs). Using a Taguchi design, the effects of working pressure, sputtering power, substrate bias, and deposition time on film properties were systematically examined and optimized. Films were examined via field emission scanning electron microscopy (FE-SEM), contact angle measurements, and electrochemical tests. Comprehensive evaluation by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method identified optimal conditions of 1.5 Pa pressure, 150 W radio frequency (RF) power, −250 V bias voltage, and 60 min deposition, yielding dense, uniform films with a corrosion current density of 1.61 × 10⁻⁶ A·cm⁻² and a contact angle of 106.36°, indicative of lotus leaf-like hydrophobicity. This work enriches the theoretical understanding of a-C film process optimization, offering a practical approach for modifying fuel cell bipolar plates to support hydrogen energy applications. Full article

In this study, aluminium matrix hybrid composites reinforced with boron carbide (B₄C) and silicon carbide (SiC) were fabricated using the direct hot-pressing technique under 35 MPa pressure at 600 °C for 5 min. Particle size distribution and scanning electron microscope analysis were conducted for the input powders. The microstructure, mechanical properties, and drillability of the fabricated composites were examined. As the SiC content increased, the density decreased, hardness improved, and transverse rupture strength declined. Drilling experiments were performed based on the Taguchi L₁₈ orthogonal array. The control factors included cutting speed (25 and 50 m/min), feed rate (0.08, 0.16, and 0.24 mm/rev), point angle (100°, 118°, and 136°), and SiC content (0%, 5%, and 10%). Quality characteristics such as thrust force, torque, surface quality indicators, diameter deviation, and circularity deviation were evaluated. The Taguchi method was applied for single-response optimization, while the Entropy-weighted, Taguchi-based CoCoSo method was used for multi-response optimization. Analysis of Variance was conducted to assess factor significance, and regression analysis was used to model relationships between inputs and responses, yielding high R² values. The optimal drilling performance was achieved at 50 m/min, 0.08 mm/rev, 136°, and 10% SiC, and the confirmation tests verified these results within the 95% confidence interval. Full article

This paper presents the optimization of the power required to activate a jaw-type blowout preventer (BOP) in the oil industry using an axial piston pump. Experimental and numerical methods were combined to analyze the effects of pressure, flow rate, volumetric efficiency, and clearance leakage on energy consumption. Taguchi methodology with an orthogonal array and the “smaller-is-better” criterion was used in the experiments, while regression analysis provided a predictive model. Optimization was performed using the Grey Wolf Optimizer (GWO) in Python 3.13. The results show that pressure and flow rate significantly affect power consumption, while higher volumetric efficiency leads to notable energy savings. The optimal configuration reduced the power demand to 5.0001 kW. Based on this, reliability models were created to assess deviations from optimal conditions. The study demonstrates the effectiveness of combining statistical and optimization techniques for improving safety systems in the oil industry. The key contribution of this study lies in the integration of experimental Taguchi-based modeling with Grey Wolf Optimizer (GWO) metaheuristic techniques to optimize the energy-efficient activation of jaw-type blowout preventers, representing a novel methodological approach in the field of hydraulic safety systems in the oil industry. Full article

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

Aluminium metal matrix composites (AMMCs) incorporate aluminium alloys reinforced with fibres (continuous/discontinuous), whiskers, or particulate. These materials were engineered as advanced solutions for demanding sectors including construction, aerospace, automotive, and marine. Micro- and nano-scale reinforcing particles typically enable attainment of exceptional combined properties, including reduced density with ultra-high strength, enhanced fatigue strength, superior creep resistance, high specific strength, and specific stiffness. Microstructural, mechanical, and tribological characterizations were performed, evaluating input parameters like reinforcement weight percentage, applied normal load, sliding speed, and sliding distance. Fabricated nanocomposites underwent tribometer testing to quantify abrasive and erosive wear behaviour. Multiple investigations employed the Taguchi technique with regression modelling. Analysis of variance (ANOVA) assessed the influence of varied test constraints. Applied load constituted the most significant factor affecting the physical/statistical attributes of nanocomposites. Sliding velocity critically governed the coefficient of friction (COF), becoming highly significant for minimizing COF and wear loss. In this review, the reinforcement homogeneity, fractural behaviour, and worn surface morphology of AMMCswere examined. Full article