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Machine learning

Advances in Chemicals and Chemistry: Improving Deposition Using Non-Dominant Sorting Genetic Algorithm-II

Strategic Insights

— 1 min read

Researchers at the Department of Mechanical Engineering, Acad Technol, have made significant strides in improving the deposition of electroless nickel-copper-phosphorous coating using non-dominant sorting genetic algorithm-II. The team, led by Jhumpa De, aimed to develop predictive models using machine learning approaches to optimize the coating process. By varying the coating bath parameters, the researchers observed that a trade-off between coating deposited per unit area and surface roughness is necessary. To address this, they employed multivariate polynomial regression models, which showed improved R2 values compared to multi-linear regression models. The optimal deposition condition was determined using non-dominant sorting genetic algorithm-II, resulting in a Pareto optimal front of solutions that provided additional choices for decision-makers.

Key Takeaways:

  • Researchers at the Department of Mechanical Engineering, Acad Technol, have developed predictive models using machine learning approaches to optimize the electroless nickel-copper-phosphorous coating process.
  • The team employed multivariate polynomial regression models, which showed improved R2 values compared to multi-linear regression models.
  • A trade-off between coating deposited per unit area and surface roughness is necessary, and the optimal deposition condition was determined using non-dominant sorting genetic algorithm-II.
  • The research concluded that the coating was deposited in the optimal condition, which converged with the results obtained from the optimization algorithm.
  • Additional authors for the research include Ambikesh Kumar Srivastwa and Tarun Kumar Tiwary.
  • The study was conducted at the Department of Mechanical Engineering, Hooghly, West Bengal, India.

Statistics:

  • The coating was deposited for 30 minutes.
  • The R2 values of the multivariate polynomial regression models were improved compared to multi-linear regression models.
  • The optimal deposition condition was determined using non-dominant sorting genetic algorithm-II.
  • The Pareto optimal front of solutions was obtained using non-dominant sorting genetic algorithm-II.
  • The study was published in the journal Sadhana, 2025;50(3).

Sources:

  • Sadhana, 2025;50(3). Sadhana can be contacted at: Springer India, 7TH Floor, Vijaya Building, 17, Barakhamba Road, New Delhi, 110 001, India.
  • NewsRx. Reports from Department of Mechanical Engineering Describe Recent Advances in Chemicals and Chemistry (Improving the Deposition of Electroless Nickel-copper-phosphorous Coating Using Non Dominant Sorting Genetic Algorithm-ii). Mathematics Week. August 26, 2025; p 2908.

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