Article
Metallurgy & Metallurgical Engineering
R. Balachandhar, R. Balasundaram, M. Ravichandran
Summary: This study examines the Surface Roughness (SR) of composite consisting Aluminium alloy, Magnesium and Rock dust during turning process. The analysis confirms that speed is the most significant factor for SR, and adding AZ31 and rock dust can improve the surface finish of AA6061.
JOURNAL OF MAGNESIUM AND ALLOYS
(2021)
Article
Green & Sustainable Science & Technology
Manoj Nikam, Hamad A. Al-Lohedan, Faruq Mohammad, Surekha Khetree, Vinayak Patil, Girish Lonare, Firdos Jahan Khan, Govind Jagatap, Jayant P. Giri, Ankit D. Oza, Manoj Kumar, Rajkumar B. Chadge, Ahmed A. Soleiman
Summary: Glass-fibre-reinforced plastic (GFRP) is popular for various applications due to its high strength-to-weight ratio, stiffness, fatigue resistance, low coefficient of thermal expansion, and tailorable properties. Milling GFRP composites is challenging because of their heterogeneous nature and two-phase structure which result in high cutting forces and delamination. A statistical experiment using the Taguchi design of experiments was conducted to investigate the effect of machining settings on GFRP composite performance metrics.
Article
Engineering, Multidisciplinary
Waheed Sami B. Abushanab, Essam Moustafa, Mooli Harish, S. H. Shanmugan, Ammar Elsheikh
Summary: This research investigates the post-processing surface characteristics of Ti6Al4V alloy using abrasive water jet machining (AWJM). The effects of process factors including water pressure, abrasive flow rate, feed rate, and stand-off distance on the characteristics of the cut surfaces have been studied. Parametric ranges involving lesser heat affected regions and improved surface characteristics are determined through comprehensive experimentation.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Anh-Tuan Nguyen, Xuan-Hung Le, Van-Tung Nguyen, Dang-Phong Phan, Quoc-Hoang Tran, Dinh-Ngoc Nguyen, Manh-Cuong Nguyen, Ngoc-Pi Vu
Summary: This study reported an optimization process of powder-mixed electrical discharge machining (PMEDM) process for machining cylindrically shaped parts made of hardened 90CrSi steel. The optimal set of process parameters to satisfy the desired responses were determined, including powder concentration, pulse time, current, and voltage.
Article
Automation & Control Systems
Afef Azzi, Lakhdar Boulanouar, Aissa Laouisi, Alima Mebrek, Mohamed Athmane Yallese
Summary: The objective of this study is to investigate the effect of machining parameters on the technological parameters, surface roughness, and material removal rate during the turning process of polytetrafluoroethylene (PTFE) polymer. The study found that the feed rate significantly influenced the surface roughness and material removal rate. Response surface methodology and artificial neural networks were used to model the output parameters and determine the optimal cutting parameters. The results showed that the optimal parameters for minimizing roughness and maximizing material removal rate were determined.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Chemistry, Physical
Saurabh Shah, Anand Joshi, Kamlesh Chauhan, Ankit Oza, Chander Prakash, Raul Duarte Salgueiral Gomes Campilho, Sandeep Kumar
Summary: The objective of this study was to examine the feasibility of machining cobalt-chromium superalloy Stellite 6 using TiN-coated carbide inserts in an end milling operation. The study found that the depth of the cut, combined with the insert type, had the greatest influence on surface roughness. A regression analysis demonstrated that the created model can accurately forecast surface roughness in end milling of Stellite 6 with 95% confidence intervals.
Article
Materials Science, Ceramics
Mohammad Mohsin Khan, Abhijit Dey
Summary: The study focused on microstructural characterization and investigation on the optimum abrasive wear processing condition of newly developed in-situ TiC reinforced ZA37 alloys. Advanced vortex creating in-situ technique was used to process the MMCs. Non-linear characteristics of response variables were demonstrated by developing a quadratic regression model, and ANOVA was implemented to identify the contribution of process variables towards responses. Significant improvements were observed on wear rates, COF, and temperature when implementing optimal parametric combinations predicted by the GRA based hybrid approach.
CERAMICS INTERNATIONAL
(2021)
Article
Engineering, Manufacturing
Kuldeep A. Mahajan, Raju S. Pawade, Vinod Mishra
Summary: The study found that feed, cutting, and infeed vibrations affect the tool in three directions, with the infeed vibration causing a "tool jump" that leaves material uncut below it. By experimentation and analysis, optimal machining parameters for reducing surface roughness and minimizing vibration responses were determined.
MATERIALS AND MANUFACTURING PROCESSES
(2022)
Article
Materials Science, Multidisciplinary
Hu Qiao, Sibo Hu, Ying Xiang, Shanshan Liu, Li Zhang
Summary: Titanium alloys, as high-performance and difficult-to-machine materials, are increasingly used in high-end manufacturing industries such as aerospace. However, the choice of blade grinding parameters still relies on traditional trial and error methods, resulting in low processing efficiency and uncertain processing quality. In order to achieve high precision and low surface roughness, this paper proposes a theoretical prediction model for surface roughness during abrasive belt grinding of titanium alloy, and analyzes the influence of main process parameters through experiments. The developed model shows certain accuracy and reliability, providing guidance for high-precision prediction of surface roughness and having practical significance in engineering.
Article
Materials Science, Multidisciplinary
Venkateshwar Reddy Pathapalli, Meenakshi Reddy Reddigari, Eswara Kumar Anna, P. Srinivasa Rao, D. V. Ramana Reddy
Summary: The study aims to develop a model for evaluating the machinability of MMCs by machining three distinct types of hybrid MMCs. It explores the effects of different machining parameters on machining performance and provides insights for decision-makers in manufacturing industries.
MULTIDISCIPLINE MODELING IN MATERIALS AND STRUCTURES
(2021)
Article
Chemistry, Physical
Jaroslaw Korpysa, Jozef Kuczmaszewski, Ireneusz Zagorski
Summary: This study investigates a precision milling process using conventional end mills and a standard CNC machine tool, showing that this method can produce components with small scatter in dimension values and high accuracy levels. Additionally, the results demonstrate that the type of tool coating and variations in technological parameters can impact the dimensional accuracy and surface quality obtained.
Article
Materials Science, Multidisciplinary
Xiaoge Wang, Luchun Yan, Kewei Gao, Pengcheng Li, Jiujiu Hao
Summary: Zinc-aluminum layered double hydroxides (ZnAl-LDHs) film was prepared on magnesium alloys with different surface roughness using metallographic preparation combined with the hydrothermal method. The results showed that ZnAl-LDHs film grew most intensely when the surface roughness was at a minimum of 0.094 μm, reaching a thickness of 3.8 μm with a static contact angle of 84.34 degrees and a minimum corrosion current density of 1.12 x 10(-4) A/cm(2). The study also proposed the possible growth and corrosion prevention mechanisms of LDHs films.
Article
Materials Science, Multidisciplinary
Mohammad Mohsin Khan, Abhijit Dey
Summary: This study investigated the effects of TiCp incorporations and other process variables on the abrasive wear behavior of ZA27/TiC metal matrix composites. Through experiments and Grey Relationship Analysis, optimal processing conditions were determined, revealing the contribution of abrasive wear parameters to process properties and analysis using a second-order quadratic regression model.
MATERIALS CHEMISTRY AND PHYSICS
(2022)
Article
Chemistry, Multidisciplinary
Cheng-Hung Chen, Shiou-Yun Jeng, Cheng-Jian Lin
Summary: In the metal cutting process of machine tools, the quality of the surface roughness is crucial for improving product performance. This study proposes a back propagation neural network (BPNN) to predict surface roughness and analyzes the influence of milling parameters using ANOVA. Experimental results show that the BPNN method achieves higher prediction accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Manufacturing
Raveen John, Richard Lin, Krishnan Jayaraman, Debes Bhattacharyya
Summary: This study analyzed the effects of machining parameters on the surface quality of natural fiber reinforced composites during end milling, finding significant differences in silicon content and hardness values of different fibers impacting the surface quality. Kenaf/PP composites showed the lowest delamination damages and best surface finish, while RH/PP composites were the most challenging to machine. Spindle speed was found to have a greater influence on machined surface quality compared to feed rate for NFRCs.
MATERIALS AND MANUFACTURING PROCESSES
(2021)