Welding tools of different designs have been used to join friction stir welding 2-mm-thick Al 7075 sheets, to investigate the effect of the tool geometry on the weld performance. Five cylindrical tools with different pin geometries were manufactured from heat-treatable low alloy steel WNr 1.6582/DIN 34CrNiMo6. Additionally, the effect of the welding speed was considered in the work, with six different speeds ranging from 80 mm/min to 300 mm/min. The weld tool rotational speed was kept constant at 1000 rpm and all other parameters were also kept constant in the experiments. The tensile strength was measured to investigate the mechanical properties of the weld. Results were processed with statistical analysis tools, which showed that the mechanical strength was affected by tool geometry as well as welding speed. The weld tool with the highest pin diameter achieved the highest tensile strength. The welding speed affected the tensile strength differently in the different weld tool geometries studied. The highest weld efficiency reported in the tests is 72.20%, achieved with a cylindrical pin weld tool at 250 mm/min.
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http://dx.doi.org/10.3390/ma15228187 | DOI Listing |
Acta Neurochir (Wien)
January 2025
Hamlyn Centre, Imperial College London, London, UK.
Background: Intraoperative ultrasound is becoming a common tool in neurosurgery. However, effective simulation methods are limited. Current, commercial, and homemade phantoms lack replication of anatomical correctness and texture complexity of brain and tumour tissue in ultrasound images.
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January 2025
Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, TX 77705, USA.
Automated ultrasonic testing (AUT) is a critical tool for infrastructure evaluation in industries such as oil and gas, and, while skilled operators manually analyze complex AUT data, artificial intelligence (AI)-based methods show promise for automating interpretation. However, improving the reliability and effectiveness of these methods remains a significant challenge. This study employs the Segment Anything Model (SAM), a vision foundation model, to design an AI-assisted tool for weld defect detection in real-world ultrasonic B-scan images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
PSYCOTRIP Group, Department of Mathematics and Computation, University of La Rioja, 26006 Logroño, Spain.
In today's industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs.
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December 2024
Department of Mechanical Engineering Technology, College of Applied Industrial Technology, Jazan University, Jazan 45142, Saudi Arabia.
This paper estimates friction stir welded joints' ultimate tensile strength (UTS) and hardness using six supervised machine learning models (viz., linear regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbour, and artificial neural network). Tool traverse speed, tool rotational speed, pin diameter, shoulder diameter, tool offset, and tool tilt are the six input parameters in the 200 datasets for training and testing the models.
View Article and Find Full Text PDFMaterials (Basel)
December 2024
Department of Metal Forming, Welding and Metrology, Wroclaw University of Science and Technology, Lukasiewicza Street 5, 50-370 Wroclaw, Poland.
This paper provides a detailed analysis of the operation of representative forging tools (in the context of using various surface engineering techniques) used in the process of the hot forging of nickel-chromium steel elements. The influence of the microstructure and hardness of the material on the durability of the tools is also discussed, which is important for understanding the mechanisms of their wear. The research showed that the standard tools used in the process (only after nitriding) as a reference point worked for the shortest period, making an average of about 1400 forgings.
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