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<p>Additive manufacturing (AM), also known as 3D printing, is revolutionizing production processes across various industries. This technology is used to manufacture a wide range of complex geometries and structures from 3D model data. This advanced manufacturing technique offered many benefits, such as mass customization, freedom of design, the ability to fabricate complex structures, waste minimization, fast prototyping, on-demand and decentralized manufacturing, etc. Due to these attractive properties of AM, this technology finds wide application in distinct industrial sectors, such as biomedical, aerospace, electronics, automobile, dental, architectural, energy, education and training, building and structure. However, the performance of AM parts mainly depends upon the processing parameters, so it is crucial to tune the processing parameters of AM process, which is a difficult task. To overcome this, machine learning (ML) plays a significant role as it can offer practical ways to process optimization, quality control, energy management, and complex system modeling. In addition, the deployment of internet of things (IoT) in AM is useful for optimized and lean manufacturing especially during global pandemic situation like COVID-19. Hence, this article provides a comprehensive overview of distinct AM, ML and IoT techniques along with merits and demerits. Thereafter, the relation between AM techniques, their microstructure and properties are explored. Subsequently, the role and applications of IoT (distinct types of sensors) and ML techniques in the area of additive manufacturing are discussed. Finally, the advantages, potential, challenges and opportunities for sustainable AM platform through ML and IoT are explored.</p>

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A Comprehensive Study on Additive Manufacturing Techniques, Machine Learning Integration, and Internet of Things-Driven Sustainability Opportunities

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Santosh Kumar,
  • Rakesh Kumar

摘要

Additive manufacturing (AM), also known as 3D printing, is revolutionizing production processes across various industries. This technology is used to manufacture a wide range of complex geometries and structures from 3D model data. This advanced manufacturing technique offered many benefits, such as mass customization, freedom of design, the ability to fabricate complex structures, waste minimization, fast prototyping, on-demand and decentralized manufacturing, etc. Due to these attractive properties of AM, this technology finds wide application in distinct industrial sectors, such as biomedical, aerospace, electronics, automobile, dental, architectural, energy, education and training, building and structure. However, the performance of AM parts mainly depends upon the processing parameters, so it is crucial to tune the processing parameters of AM process, which is a difficult task. To overcome this, machine learning (ML) plays a significant role as it can offer practical ways to process optimization, quality control, energy management, and complex system modeling. In addition, the deployment of internet of things (IoT) in AM is useful for optimized and lean manufacturing especially during global pandemic situation like COVID-19. Hence, this article provides a comprehensive overview of distinct AM, ML and IoT techniques along with merits and demerits. Thereafter, the relation between AM techniques, their microstructure and properties are explored. Subsequently, the role and applications of IoT (distinct types of sensors) and ML techniques in the area of additive manufacturing are discussed. Finally, the advantages, potential, challenges and opportunities for sustainable AM platform through ML and IoT are explored.