Volume 68, Issue 1

This study investigates the influence of temperature and stress level on the creep behavior of glass fiber reinforced plastic (GFRP) pipes with varying fiber contents. Using constant-load testing devices and environmental chambers, creep experiments were conducted for 1,000 h under three temperature conditions (30 °C, 50 °C, and 70 °C) and three stress levels (10 %, 30 %, and 50 % of the ultimate strength). The experimental data were fitted using the Zener, Burgers, and HKK models, and the time-dependent strain evolution of the pipes from 1,000 to 5,000 h was predicted. The results demonstrate that both temperature and stress level significantly affect the creep performance of GFRP pipes. The creep rate increases with higher temperatures and stress levels, and under high-temperature, high-stress conditions, the pipes experience rapid failure. All three models—Zener, Burgers, and HKK—effectively describe the creep behavior of the pipes with different fiber contents. The Burgers and HKK models exhibit superior fitting accuracy, and their long-term strain predictions are in close agreement. This study establishes a methodological framework for evaluating the long-term mechanical performance of GFRP pipes and provides valuable insights for predicting their creep behavior.

Self-clinching nuts are increasingly used in automotive industry for fastening bolts to metal sheets, which are too thin to be threaded directly. To ensure safe and reliable use, this type of nuts must meet defined mechanical performance criteria, such as push-out and torque-out resistance. Finite element analysis plays a crucial role in observing the deformation of the sheet metal during the clinching process and in assessing whether the nuts meet the expected performance standards. This study focused on the development of finite element models for clinching, push-out, and torque-out tests for self-clinching nuts. The accuracy of the developed models was evaluated by comparing their results with corresponding experimental data. The findings of the study revealed that mesh quality significantly affect the accuracy of the simulation results. Additionally, it was observed that the compliance of the testing machine plays an important role in the discrepancy between experimental and simulation outcomes during the clinching process.

In this study, dissimilar butt welding of 0.8 mm thick DP600 and 1 mm thick DP1180 advanced high-strength steels was performed using fiber laser beam welding with beam oscillation. The effects of oscillation type (linear and circular), oscillation amplitude (0.5, 1, 1.5 mm), and welding speed (50–80 mm s −1 ) on weld bead geometry, microhardness, and tensile load were investigated. A total of 12 experimental sets were conducted by keeping the laser power and frequency constant at 1.2 kW and 100 Hz, respectively. Metallographic evaluations, Vickers microhardness tests, and tensile tests were carried out in accordance with standard procedures. The results revealed that welding speed had a significant influence on weld penetration and width, with optimal parameters determined as 60 mm s −1 speed and 1 mm amplitude in both oscillation types. Circular oscillation generally led to higher microhardness values, whereas linear oscillation produced wider weld seams. While amplitude increase decreased penetration depth, it improved weld width. The tensile load of all joints was largely influenced by the DP600 base metal, where fractures were consistently observed. However, the joint at 1.5 mm amplitude in circular mode fractured in the weld zone, indicating insufficient penetration. The findings suggest that proper selection of oscillation parameters can enhance weld quality and mechanical performance when joining dissimilar high-strength steels for lightweight automotive applications.

AISI 8620 steel is used in gears and machine parts where surface wear resistance is important. The presented study focuses on different surface hardening treatments on microstructural properties and the dry sliding wear behavior of the AISI 8620 steel. The dry sliding wear test used a tribometer with constant wear test parameters according to ASTM G-99 standard. The microstructures were characterized by optical and scanning electron microscopy (SEM), energy-dispersive X-ray (EDX), and X-ray diffraction (XRD). The highest surface hardness (∼1,507 HV) occurs in the boronized sample, attributed to the hard, iron borides (FeB, Fe 2 B, Fe 3 B) layers. The minimum surface hardness (∼507 HV) is determined in nitrided samples. The maximum case hardening depth (∼0.90 mm) is obtained in nitrided samples, as the minimum value of case depth (∼0.1 mm) was found in boronized samples. The mean coefficient of friction was highest for nitrided (0.622) and lowest for boronized (0.367) samples. Boronizing also gave the minimum wear (1.705 mm 3 , 1 mg), compared with nitriding (13.5 mm 3 , 1.8 mg) and carburizing (43.57 mm 3 , 4.9 mg). The boronized samples exhibited the best wear properties. According to the optical profilometer results of worn samples; the carburized sample exhibits a broad and deep wear track (volume loss 43.57 mm 3 ), indicating severe abrasive wear due to lower surface hardness, the boronized sample showed the narrowest and shallowest wear track (volume loss 1.705 mm 3 ), because of superior wear resistance from the dense, hard iron boride layer while the nitrided sample having moderate wear depth and volume loss (13.5 mm 3 ), indicating intermediate wear resistance attributed to a thinner, harder nitride layer providing moderate protection.

In 3D printing, the mechanical properties of the products can be improved by optimizing manufacturing parameters. In this study, the manufacturing parameters that influence the mechanical performance of PLA (polylactic acid) components produced using fused deposition modeling (FDM) were investigated through experimental testing and modeling-based optimization techniques. Three key manufacturing parameters (raster angle, infill type, and infill rate) were selected to evaluate their effects on ultimate tensile strength (UTS) and impact strength (IS), which were used as performance criteria. An L18 orthogonal array was used for the experimental design, and tensile and impact tests were conducted in accordance with ASTM standards. Two different optimization techniques were used to predict mechanical properties: Response surface methodology (RSM) and fuzzy logic (FL). A comparative evaluation based on experimental data revealed that RSM provides superior prediction accuracy, with average error rates of 3.89 % for UTS and 9.57 % for IS, while FL exhibits higher deviations (6.35 % and 13.35 %, respectively). The results highlight the importance of statistical modeling in predicting mechanical behavior in additive manufacturing processes and demonstrate that RSM produces more reliable results than FL for parameter optimization. This study provides practical insights for engineers aiming to improve the mechanical performance of 3D printed components.

This study investigates the development and mechanical characterization of hybrid and non-hybrid composite materials reinforced with carbon fiber, basalt fiber, glass fiber, and 304 stainless steel fabric within an epoxy matrix. The aim is to create composites with enhanced tensile strength, flexural strength, and impact resistance for potential applications in blast-resistant structures. Composite laminates were fabricated using a hand lay-up method, followed by curing under pressure to ensure optimal interfacial bonding. Mechanical testing, including tensile, three-point bending, Charpy impact, and low-velocity drop weight tests, was conducted in accordance with ASTM standards. Scanning electron microscopy (SEM) analysis provided insights into fracture mechanisms and delamination behavior. The results show that hybrid composites exhibit inferior static mechanical properties compared to non-hybrid pure carbon, glass and basalt composites, but certain hybrid configurations show significant improvements energy absorption due to the steel mesh. These findings suggest that the strategic combination of different fibers and metallic fabrics can be tailored to meet specific engineering requirements, offering a pathway for designing advanced materials in the aerospace, automotive, and defense industries.

The application of lattice structures has become increasingly important in designing complex components due to additive manufacturing (AM) advancements. Various types and design parameters of lattice structures allow weight reduction while maintaining the required strength and improving mechanical properties, with the strength varying based on these parameters. One common approach to calculating this strength is by using software solvers like SimSolid, which employs the meshless analysis solution (MAS). Considering the variety of parameters, the complexity of lattice structures, and the computational difficulties in analysis methods, identifying the optimal lattice structure for a design is highly challenging. To overcome this challenge, artificial neural networks (ANNs) are integrated into the optimization algorithm used in this study. The training data for the ANN are obtained from the analysis results of the designs generated using the design parameters selected by the Latin hypercube sampling (LHS) method. The ANNs integrated non-dominated sorting genetic algorithm II (NSGA-II) optimization algorithm is used to minimize the mass while ensuring the strength of the material by keeping the maximum stress within the permissible limits. The method is applied to the weight reduction of the brake pedal, approximately 26.96 % is achieved while maintaining the required strength under existing conditions.

CW511L lead-free brass is a newly developed alloy that has found widespread application across various industrial sectors. It is particularly preferred as a raw material for watering and pumping components, which typically require machining before use. This study investigates the influence of cutting tool geometry on drilling performance, focusing on critical parameters such as radial rake angles, axial rake angles, tip radius, and helix angle. An L27 Taguchi experimental design was employed using the following levels: radial rake angle (4°, 8°, 12°), axial rake angle (−2°, 0°, 2°), tip radius (10, 20, 30 µm), and helix angle (0°, 5°, 10°). Drilling experiments were performed in accordance with this design to evaluate cutting forces, chip breakability, burr height, and surface quality. The effects of tool geometry were analyzed using three-dimensional surface plots and a correlation matrix to reveal significant relationships between input variables and drilling responses.

This study presents a modified version of the Animated Oat Algorithm (AOA), enhanced through the integration of chaotic maps, termed the Chaotic Animated Oat Algorithm (CAOA). Inspired by the seed dispersal mechanisms of the oat plant, AOA offers a population-based metaheuristic framework suitable for complex global optimization tasks. The proposed CAOA was evaluated across four real-world engineering optimization problems: pressure vessel design, bolted rim coupling, gear train cost minimization, and robot gripper arm weight reduction. Results demonstrate that CAOA consistently outperforms traditional and state-of-the-art metaheuristics in terms of solution quality, convergence stability, and robustness, affirming its potential for widespread engineering applications.

In this study, the influence of electronic waste reinforcement on the properties of aluminum was investigated. For this purpose, samples were produced with hot pressing at 600 °C. The reinforcement ratio for the composites was determined as 1 wt.%, 5 wt.%, and 10 wt.%. The wear and corrosion behavior of the samples were investigated by dry sliding wear test and electrochemical corrosion tests. The wear rate values indicated that the increasing reinforcement ratio reduced the wear rate. Moreover, it was determined that the increasing reinforcement ratio improved the corrosion behavior of the composites. The corrosion rate of the samples was reduced with an increasing reinforcement ratio, and a more stable surface was obtained in corrosion environment. In conclusion, it was deduced that Rh, Ag, Pt, and Sn containing electronic waste provided significant improvements in wear and corrosion behaviors.