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Outlier detection in additive manufacturing using novel machine learning algorithm

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Abstract

The capacity of additive manufacturing (AM) to create products with intricate characteristics has produced its rapid expansion in the manufacturing business and widespread interest from a wide range of other sectors. To ensure that parts made for functional use meet specified standards, especially in terms of quality and dependability, quality management of the AM components has drawn a lot of attention from the industries. Unnecessary porosities have a substantial impact on how well the products manufactured via AM operate mechanically. In order to continue to enhance the standard of the AM components by outlier detection, the quality control procedures are thus necessary. In order to efficiently identify outliers in AM data, this work introduces the binary-weighted sparrow search fine-tuned random forest (BSS-RF) method. The binary-weighted sparrow search optimization (BSSO) method is proposed to optimize the RF’s performance in order to increase the detection accuracy. To remove the noisy data, raw data samples are enhanced using the normalization procedure. The important features are extracted using quadratic discriminant analysis (QDA). Finally, the suggested strategy is used to identify abnormal occurrences. The efficacy of the suggested approach is supported by experimental data. The suggested approach can be used in various industrial sectors, including electrical engineering, mechanical engineering, and medical engineering. We intend to create an interactive outlier identification system that is based on the suggested technique for real-time outlier identification and fault forecasting in the future.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, NCR Campus, Uttar Pradesh, Modinagar, India

    Chiranjit Dutta

  2. Department of Mechanical Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, India

    Nagabhooshanam Nagarajan

  3. Department of Mechanical Engineering, Institute of Engineering and technology, GLA University, Mathura, Uttar Pradesh, India

    Nagabhooshanam Nagarajan

  4. Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Nagabhooshanam Nagarajan

  5. Department of Computer Science & Engineering, GITAM School of Technology, GITAM University, Bengaluru, Karnataka, India

    Zameer Ahmed Adhoni

  6. Senthil College of Education, Puducherry, India

    Sendil Kumar Dhandapany

  7. Center for Multi-Core Architecture Computation (C-MAC), Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Aravindhan Alagarsamy

  8. Department of Information Technology, Aditya Engineering College, Surampalem, Andhra Pradesh, India

    Antharaju Kalyana Chakravarthy

  9. Department of Computer Science & Engineering, Aditya College of Engineering, Surampalem, Andhra Pradesh, India

    Neti Praveen

  10. Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Ganeshan Pushpanathan

  11. Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Sathish Kannan

Authors
  1. Chiranjit Dutta
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  2. Nagabhooshanam Nagarajan
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  3. Zameer Ahmed Adhoni
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Correspondence to Chiranjit Dutta.

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Dutta, C., Nagarajan, N., Adhoni, Z.A. et al. Outlier detection in additive manufacturing using novel machine learning algorithm. Int J Adv Manuf Technol (2023). https://doi.org/10.1007/s00170-023-12798-9

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