Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (7/7 displayed)

  • 2024Fuzzy logic-driven genetic algorithm strategies for ultrasonic welding of heterogeneous metal sheets1citations
  • 2024Optimizing process parameters to minimize wear-induced material loss in bronze-based hybrid metal matrix composites using the Taguchi method2citations
  • 2024Effect of copper addition on mechanical properties and microstructures of LM25 cast alloys3citations
  • 2024Exploring the strength and durability of hemp fiber reinforced moringa bioresin composites for skateboard applications3citations
  • 2024Opportunities and challenges of bamboo fiber composites in additive manufacturing: A comprehensive review2citations
  • 2024Surface modification of basalt fibers and its effect on the mechanical properties of basalt fiber/epoxy composite laminates4citations
  • 2024Investigation of chemically treated jute/kenaf/glass fiber with TiO2 nano-filler for tensile and impact characteristics19citations

Places of action

Chart of shared publication
Me, Satishkumar P.
1 / 2 shared
Guru, Ajay
1 / 1 shared
Singholi, Ajay K. S.
1 / 1 shared
Amale, Ashvin
1 / 1 shared
Albaijan, Ibrahim
1 / 4 shared
Banagar, Dr. Ashok
1 / 1 shared
T., Dr. Ramesh B.
1 / 1 shared
Sreenivasa, R.
1 / 1 shared
Thanikodi, Sathish
3 / 3 shared
Bongale, Arun Kumar
1 / 1 shared
Al-Sadoon, Mohammad Khalid
1 / 1 shared
Patel, Hiteshkumar
1 / 1 shared
Shah, Mohd Asif
1 / 2 shared
Surani, Kapil
1 / 1 shared
Siddiqui, Md Irfanul Haque
1 / 3 shared
Velkin, Vladimir Ivanovich
1 / 1 shared
Ashraf, Intesaaf
1 / 1 shared
Logesh, K.
1 / 3 shared
Alarfaj, Abdullah A.
1 / 1 shared
Sahayaraj, Felix
1 / 7 shared
Ramesh, M.
1 / 8 shared
Kannan, Sathish
1 / 4 shared
John Rajan, A.
1 / 2 shared
Turmanov, Rakhymzhan
1 / 2 shared
Saravanan, Dr. R.
2 / 3 shared
Balasubramanian, Muthu Selvan
1 / 2 shared
Hasnain, S. M. Mozammil
1 / 4 shared
P., Jaideep J.
1 / 1 shared
Bolar, Gururaj
1 / 1 shared
Sathish, T.
1 / 24 shared
Hiremath, Anupama
1 / 2 shared
Iyer, Tejas Girish
1 / 1 shared
Solairaju, Jothi Arunachalam
1 / 1 shared
Haider, Sajjad
1 / 6 shared
Chart of publication period
2024

Co-Authors (by relevance)

  • Me, Satishkumar P.
  • Guru, Ajay
  • Singholi, Ajay K. S.
  • Amale, Ashvin
  • Albaijan, Ibrahim
  • Banagar, Dr. Ashok
  • T., Dr. Ramesh B.
  • Sreenivasa, R.
  • Thanikodi, Sathish
  • Bongale, Arun Kumar
  • Al-Sadoon, Mohammad Khalid
  • Patel, Hiteshkumar
  • Shah, Mohd Asif
  • Surani, Kapil
  • Siddiqui, Md Irfanul Haque
  • Velkin, Vladimir Ivanovich
  • Ashraf, Intesaaf
  • Logesh, K.
  • Alarfaj, Abdullah A.
  • Sahayaraj, Felix
  • Ramesh, M.
  • Kannan, Sathish
  • John Rajan, A.
  • Turmanov, Rakhymzhan
  • Saravanan, Dr. R.
  • Balasubramanian, Muthu Selvan
  • Hasnain, S. M. Mozammil
  • P., Jaideep J.
  • Bolar, Gururaj
  • Sathish, T.
  • Hiremath, Anupama
  • Iyer, Tejas Girish
  • Solairaju, Jothi Arunachalam
  • Haider, Sajjad
OrganizationsLocationPeople

article

Fuzzy logic-driven genetic algorithm strategies for ultrasonic welding of heterogeneous metal sheets

  • Giri, Dr. Jayant
  • Me, Satishkumar P.
  • Guru, Ajay
  • Singholi, Ajay K. S.
  • Amale, Ashvin
  • Albaijan, Ibrahim
Abstract

<jats:p>There are a lot of problems with the conventional fusion welding process, so ultrasonic welding has been used for about 20 years and has helped a lot of manufacturing industries, including aviation, medicine, and microelectronics. Ultrasonic welding takes less than one second, making it suitable for mass production. Poor weld quality and joint strength are common issues that industries encounter as a result of this process. Actually, the success and quality of the welding are determined by its control parameters. This research examines the impacts of weld time, vibrational amplitude, and weld pressure on the welding of 0.6 mm thick sheets of two different metals, specifically copper and aluminum (AA2024). Responses, including tensile shear stress, weld area, and T-peel stress, are acquired through experiments that follow a full factorial design including four replicas. The highest recorded tensile shear stress was 4.34 MPa, the maximum weld area measured was 63.6 mm2, and the peak T-peel stress reached 1.22 MPa. A second-order non-linear regression model was constructed using all of these data points, which related the responses to the predictors. Due to the importance of quality in the production sector, the process parameters were determined by the combination of genetic algorithm (GA) and fuzzy logic (FL) approaches. The impact of the weld zone temperature on various quality characteristics has been investigated through experiments. It has been noted from the confirmatory test that FL produces superior output outcomes compared to the genetic algorithm, with FL achieving a fuzzy multi-performance index of 0.94 compared to 0.61 for GA. By conducting microstructural analysis, weld quality levels, including “under-weld,” “good weld,” and “over-weld,” were established.</jats:p>

Topics
  • experiment
  • aluminium
  • laser emission spectroscopy
  • strength
  • copper
  • ultrasonic