Editorial European Special Issue – V2 Accepted Manuscript Vincent Wagner Vincent Wagner 47, avenue d'azereix Tarbes, France 65000 France Email: vincent.wagner@enit.fr Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information Vincent Wagner 47, avenue d'azereix Tarbes, France 65000 France Email: vincent.wagner@enit.fr J. Manuf. Sci. Eng. 1-2 (2 pages) Paper No: MANU-24-1126 https://doi.org/10.1115/1.4065003 Published Online: March 6, 2024 Article history Received: February 23, 2024 Revised: February 29, 2024 Accepted: March 4, 2024 Published: March 6, 2024
The objective of the research is to analyze the impact of the ultrasonic vibration assistance on the wear resistance due to the topography enhancement caused on Ti6Al4V cylindrical samples after surface modification through a ball burnishing process. The process parameters considered for this study are the burnishing force, the number of passes, and the addition of a 40 kHz ultrasonic assistance, which are thought to enhance the surface topography and increase the hardener effect in terms of depth. A complete screening design of 3 factors was performed, revealing that the number of passes and the vibration assistance are the most important parameters for minimizing the wear volume loss, being 170 N of burnishing force, 3 passes, and the addition of the vibration assistance as the best combination found. However, no significant difference in the friction force was observed. The topography results show that there is a decrease in the average roughness from 1.50 to 0.45 μm with optimal burnishing conditions. It was also analyzed the microstructure originated after the turning, the conventional ball burnishing, and the vibration-assisted ball burnishing, being the last optimal in terms of depth affectation to the microstructure. Therefore, these results confirm an improvement in terms of wear enhancement, friction reduction, and topography results when vibration assistance is added to a conventional ball burnishing process.
Controlling a quadrotor UAV is a growing interest in the field of control engineering due to the increasing use of these flying machines in the field of the internet of things, so classic PID controller is a proper solution, the purpose of this study is to investigate the impact of the meta-heuristic algorithms on tuning the PID controller parameters in order to maintain a stationary flight of a quadrotor on a specific altitude, the X4-flyer model of the dynamics is used in the tests, where we claim that there is an objective function which is faster while the optimization process is running and there is a meta-heuristically tuned PID controller combination which is the best in terms of the minimal error, also there is one which is the best while speaking about the overshoot and another combination which is fast in the altitude response, an open-loop simulation was tested to prove that the height control of the model is unstable, meta-heuristic algorithms were proposed to tune a parallel PID controller parameters such as DA-PID, GOA-PID, GWO-PID, PSO-PID, WOA-PID, ALO-PID, HHO-PID and SSA-PID, four different objective functions were the target to be minimized for each algorithm, and the winning optimization approach according to the system response characteristics was compared to a PD controller found in the literature. The results show that SAE and STAE are the best objective functions to use to reach a faster optimization process, the minimal error is better reached with ALO_SAE, the minimal overshoot is well achieved with WOA_STAE and DA_SAE which is the winning combination to have a faster altitude response.
Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality. The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact). The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are “Classification”, “Diagnosis”, “Disease”, “Prediction”, and “Risk”. This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.
