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Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning.

Hanxiang Li1, Manal E Alkahtani2, Abdul W Basit1

  • 1UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK.

International Journal of Pharmaceutics
|November 4, 2024
PubMed
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This summary is machine-generated.

This study explored eco-friendly pharmaceutical 3D printing (3DP) by analyzing energy use and CO2 emissions in Fused Deposition Modeling. Optimizing parameters like build plate temperature and using machine learning significantly reduces environmental impact.

Area of Science:

  • Pharmaceutical manufacturing
  • Sustainable engineering
  • Additive manufacturing

Background:

  • 3D Printing (3DP) offers personalized medicine but faces environmental concerns regarding carbon emissions.
  • Eco-friendly manufacturing is crucial for the pharmaceutical industry's future.

Purpose of the Study:

  • To investigate the environmental impact, specifically energy consumption and CO2 emissions, of pharmaceutical 3D printing using Fused Deposition Modeling (FDM).
  • To identify key parameters influencing energy use and explore the role of Machine Learning (ML) in optimizing sustainable pharmaceutical manufacturing.

Main Methods:

  • Utilized Design of Experiments (DoE) and Machine Learning (ML) to analyze energy consumption across 136 experimental runs for four common pharmaceutical dosage forms.
  • Identified critical parameters affecting energy use: number of objects, build plate temperature, nozzle temperature, and layer height.
Keywords:
3D printingArtificial intelligenceDigital manufacturingEnergy consumption, Carbon footprintHealthcare 5.0Pharmaceutical manufacturingSustainability

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Main Results:

  • Reduced trial-and-error in R&D and lower build plate temperatures were found to significantly decrease CO2 emissions.
  • The ML pipeline demonstrated superior accuracy in predicting CO2 emissions compared to DoE, validating its potential for sustainable process development.
  • Validated ML models maintained high accuracy across diverse dosage forms and geometric complexities.

Conclusions:

  • Merging sustainability and digitalization, particularly through ML, is key for environmentally conscious pharmaceutical 3D printing, aligning with Industry 5.0 principles.
  • Optimizing printing parameters and reducing waste are essential for a sustainable future in pharmaceutical additive manufacturing.
  • ML shows comparable learning capabilities to DoE, indicating a promising path for its broader adoption in pharmaceutical manufacturing.