Neural Network
Assembly Line
Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage
The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.
An ensemble neural network for optimising a CNC milling process
Computer numerical control (CNC) milling is a common method for the efficient mass production of products. Process efficiency and product quality have a strong dependency on the cutting process conditions. Furthermore, optimising a process for material removal rate (MRR) and surface roughness (SR), which are measures of process efficiency and product quality, respectively, is a complex optimisation task due to their contrasting relationships with process parameters. In this work, CNC end milling is performed on aluminium 6061 to investigate the influence of key process input variables (feed per tooth, cutting speed, and depth of cut) on the machined part’s SR. Firstly, a full factorial parametric study is conducted and analysed using Analysis of Variance (ANOVA) before an Ensemble Neural Network (ENN) is trained on the experimental data. To capture the complex nonlinear relationships accurately, each base model of the ENN is a combined genetic algorithm-artificial neural network, whose hyperparameters are optimised using a Bayesian optimisation framework.