Numerical and experimental thermal stress analyses of dissimilar GTA welded joints

Saxena, Abhishek; Saxena, Kuldeep K.; Singh, Bharat; Rajput, S. K.; Yelamasetti, Balram

doi:10.1007/s12008-023-01479-2

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…ML and AI have the potential to transform every stage of materials processing, from design and discovery to characterization and analysis, and finally, to processing optimization and control. Here are some potential applications of ML and AI in materials processing [30]:…”

Section: Machine Learning and Artificial Intelligence In Materials Pr...mentioning

confidence: 99%

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

Srivastava,

Kumar,

Kumar

et al. 2024

E3S Web Conf.

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This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

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Section: Machine Learning and Artificial Intelligence In Materials Pr...mentioning

confidence: 99%

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

Srivastava,

Kumar,

Kumar

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

Numerical and experimental thermal stress analyses of dissimilar GTA welded joints

Cited by 1 publication

References 30 publications

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

Machine Learning and Artificial Intelligence for Advanced Materials Processing: A review on opportunities and challenges

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