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Spindle Assembly02:50

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Spindle assembly occurs through three, often coexisting, pathways – the centrosome-mediated pathway, the chromatin-mediated pathway, and the microtubule-mediated pathway – collectively contributing to form a robust spindle apparatus.
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Machines: Problem Solving II01:30

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Updated: Jun 27, 2026

Laser-induced Forward Transfer of Ag Nanopaste
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Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

Wei Long Ng1, Guo Liang Goh2, Guo Dong Goh3

  • 1Singapore Centre for 3D Printing, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.

Advanced Materials (Deerfield Beach, Fla.)
|March 8, 2024
PubMed
Summary

Machine learning (ML) is revolutionizing additive manufacturing (AM) by uncovering complex patterns for better 3D printing. Integrating ML enhances design, production, and quality control in AM processes.

Keywords:
additive manufacturingbioelectronicsbioprintingconstructioncultivated meatdrugmachine learning

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Area of Science:

  • Materials Science
  • Engineering
  • Computer Science

Background:

  • Machine learning (ML) adoption is increasing in additive manufacturing (AM) to understand complex parameter relationships.
  • ML models analyze large datasets to reveal insights for AM process decision-making.
  • The synergy between ML and AM promises to transform the design and production of 3D-printed parts.

Purpose of the Study:

  • To review challenges and opportunities at the intersection of ML and AM.
  • To analyze the publication landscape of ML in AM research.
  • To explore common ML applications and future trends in AM.

Main Methods:

  • Comprehensive literature review of ML applications in additive manufacturing.
  • Analysis of publication trends and common research areas.
  • Exploration of ML algorithms and their impact on AM processes.

Main Results:

  • ML shows superior performance in various AM applications, including quality control, process and design optimization, microstructure analysis, and material formulation.
  • Increased attention on ML in AM is driven by its effectiveness.
  • The review identifies key application areas and emerging trends.

Conclusions:

  • ML integration significantly enhances 3D printing capabilities across diverse AM research fields.
  • Future outlook emphasizes advanced ML models, novel sensor development, and ML in emerging AM areas.
  • ML is poised to be a key driver of innovation in additive manufacturing.