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Daily Archives: May 12, 2026

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Experimental Investigation of Mechanical Properties in Dissimilar Al-Cu Joints Using Friction Stir Welding

Authors: Miss Gaikwad Janhvi Anurath, Miss. Kadam Vaishnavi Raju, Mr. Chinmay Shinde, Mr. Narayanpure Sujal, Prof. Dr.Ashish Kumar

Abstract: Friction Stir Welding (FSW) is an advanced solid-state joining technique used for welding similar and dissimilar metals without melting the base materials. In this project, an experimental investigation has been carried out to study the mechanical properties of dissimilar joints between Aluminium Alloy AA6061 and Copper (ETP Copper) using the Friction Stir Welding process. The purpose of this study is to evaluate the effect of welding parameters on the strength and quality of the welded joints. The welding experiments were performed using a carbide conical ball nose tool under different process conditions such as rotational speed, welding speed, and plunge depth. Proper fixture arrangements and clamping systems were used to obtain defect-free joints. AA6061 aluminium and copper were selected due to their wide applications in aerospace, automobile, marine, electrical, and heat transfer industries where light weight materials with high thermal and electrical conductivity are required. After the welding process, the joints were examined through visual inspection and tested for various mechanical properties including tensile strength, hardness, and microstructural characteristics. The experimental results showed that welding parameters greatly affect heat generation, material flow, and intermetallic compound formation at the weld interface. Optimized welding conditions produced sound joints with better tensile strength and uniform hardness distribution. The investigation concludes that Friction Stir Welding is an efficient and economical process for joining dissimilar aluminium-copper materials with fewer defects and Improved mechanical properties compared to conventional fusion welding methods. The results of this project can be useful for industrial applications requiring strong, lightweight, and conductive dissimilar metal joints.

DOI: http://doi.org/

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IJSRET EDITORIAL BOARD MEMBER Mrs. Malati Vaibhav Tribhuwan

Mrs. Malati Vaibhav Tribhuwan
Affiliation Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id: tmalativ@gmail.com
Publication:  Books:

  • Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022.

Publications:

  • An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025.
  • Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021.
  • 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019.
  • AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020.
  • Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018
 
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Uncategorized

IJSRET EDITORIAL BOARD MEMBER Mrs. Malati Vaibhav Tribhuwan

Mrs. Malati Vaibhav Tribhuwan
Affiliation Assistant Professor and HOA, Blockchain Technology, Department of Technology, SPPU
Email-Id: tmalativ@gmail.com
Publication:  Books:

  • Python Programming – T.Y.B.Sc.(Computer Science), Sem-IV Vision Publication 2022.

Publications:

  • An Analysis of Internet of Behaviour (IOB): A Crucial Footprint on Healthcare Sector ,London Metropolitan University, London, UK (Venue Partner)2025.
  • Intelligent Agriscience: Epoch of AI in Indian Agriculture IJREAM, Volume-6, Issue-11, ISSN: 2454-9150 February 2021.
  • 3D Password: A Secured Authentication System IJRAR, Volume-6 Issue-2, ISSN No: 2349-5138 April 2019.
  • AIoT with PUF: A Concrete Security IJITEE, Volume-9 Issue-7, ISSN: 2278 ,May 2020.
  • Dr. D. Y. Patil ACS College, Pimpri, Pune ,A study of Artificial Intelligence and its Applications in different areas February 2018
 
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Ai-Powered Analysis For Detecting Sleep Irregularities Through Deep Learning Models

Authors: R.Renuka, Dr.S.Mohana

Abstract: Typically, sleep disorders like insomnia, sleep apnea, and narcolepsy may not receive appropriate diagnosis until serious physical and mental health issues develop. Traditional techniques, though effective, involve polysomnography, which is not only labor-intensive and time-consuming but also demands special clinical conditions. Hence, this study aims to develop a framework that relies on AI techniques to utilize a hybrid model of Deep Learning techniques, including Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM), to process EEG signals to identify sleep disorders. The CNN model can automatically identify spatial features in the raw signals, and the LSTM model can identify temporal dependencies in the signals to correctly classify Awake, REM, and NREM stages. Preprocessing techniques have been employed to clean and normalize the signals. The system, trained and validated using standardized data sets like PhysioNet, exhibits robustness and generalization in dealing with different patterns of sleep. It can also be used to analyze new EEG signals in real-time, detect abnormal sleep patterns, and predict the occurrence of sleep disorders. This intelligent system can greatly improve the efficiency of diagnosis and reduce the need to rely on manual diagnosis. It can also prove to be a cost-effective solution.

DOI: https://doi.org/10.5281/zenodo.20132129

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