TT2021-017 Artificial intelligence for multi-parameter optimisation of manufacturing procedures

Current global optimisation techniques involve three main approaches, (i) gradient based approaches, (ii) stochastic approaches and (iii) model-based approaches. When using a gradient-based approach, a large number of evaluations is required to determine which direction to go in. This is costly, time-consuming, and one can often get stuck in local minimum traps. Stochastic approaches scale poorly with each additional dimension/parameter. Model-based approaches require a large amount of prior knowledge, which may not be available for a particular problem, e.g. a new manufacturing process.

By leveraging novel machine learning techniques and heuristics, the novel approach from The Australian National University (ANU) enables efficient mapping/learning of the system to promptly generate candidate solutions, which optimises the system/process in question. This provides insights that are not normally available via existing methodologies/ approaches applied by human operators. The number of evaluations required is significantly reduced (machine learning requires less information to get to a solution), this approach scales very well with the addition of more dimensions/parameters and is able to avoid local minimum traps.

Potential benefits

  • Efficient: Requires less information to get to a solution (including multiple parameter problems)
  • Versatile: Can solve multi-parameter/high-dimension problems with one program
  • Valuable decision making: Models generated can be utilised multiple times, to provide insight and deduce relevant system parameters
  • User-friendly: Requires little to no specialisation or tuning
  • Automated: Requires little to no human intervention/manpower
  • Cheaper: Optimisation of your system/multiple processes will make your generation/manufacturing processes cheaper to produce

Potential applications

  • Advanced/automated manufacturing
  • Photonics (communications and manufacturing)
  • High precision sensing
  • Quantum technology (hardware through to sensing)
  • Gravimetric analysis
  • Hydrogen storage - optimisation of capture process
  • Solar and wind - manufacturing of components
  • Semiconductor - manufacturing of components


ANU is seeking industry and customer feedback for this technology (to ascertain which initial application(s) will suit this technology best), as well as engagement with industry partners/customers to work collaboratively with us to further develop the idea and optimise the algorithm for their specific application and/or manufacturing requirements.

Related publication: Multiparameter optimisation of a magneto‑optical trap using deep learning, Nature Communication, DOI

Key research team

  • Aaron Tranter, Department of Quantum Science, Research School of Physics
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