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This review explores how artificial intelligence (AI) enhances nanomanufacturing and digital manufacturing (DM) for Industry 4.0 applications. AI optimizes nanomaterial synthesis, processes, and characterization, driving innovation in diverse technological fields.

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Technology
  • Artificial Intelligence

Background:

  • Nanomanufacturing and digital manufacturing (DM) are key technologies in Industry 4.0.
  • These technologies enable material processing across various length scales.
  • Their evolution is crucial for advancements in medicine, robotics, and electronics.

Purpose of the Study:

  • To review the evolution of nanomaterials and nanomanufacturing in the digital age.
  • To discuss the integration of artificial intelligence (AI) tools in these processes.
  • To highlight applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics.

Main Methods:

  • Review of literature on nanomanufacturing and AI integration.
  • Discussion of machine learning and deep learning algorithms for nanoscale analysis.
  • Elaboration on AI applications in nanomaterial synthesis, process optimization, and characterization.

Main Results:

  • AI tools, including machine learning and deep learning, are increasingly used for nanoscale image analysis, nanomaterial design, and quality assurance.
  • Challenges exist in achieving robust predictions with current AI models.
  • AI integration offers significant potential for material synthesis, process innovation, and nanosystem development.

Conclusions:

  • AI is pivotal for advancing nanomanufacturing and digital manufacturing.
  • Future prospects include sophisticated AI algorithms like reinforcement learning and explainable AI (XAI).
  • Big data analytics and advanced AI will drive innovation in material synthesis, manufacturing, and nanosystem integration.