Intellegens
Software : Information Technology : MLOps
Our mission is to be the leading machine learning solution for real-world, sparse and noisy data problems in industrial R&D and manufacturing processes. Our advanced deep learning technology originated from the work of Dr Gareth Conduit and collaborators at the Cavendish Laboratory, University of Cambridge. At Intellegens, we have further developed this work to build a unique Artificial Intelligence (AI) toolset that can train deep neural networks from sparse or noisy data, where other machine learning approaches fail. These capabilities are available through the Alchemite™ software. Our Cambridge-based development team continues to extend and improve Alchemite™ based on close collaboration with our customers and partners.
Assembly Line
The right tool for the right job – ML and Design of Experiments
Typical statistical DOE software assumes that the response of experimental outputs to inputs is linear, or at best quadratic. ML makes no such assumption. Its models learn from the data provided even when that data contains complex, non-linear relationships. So ML can model difficult multi-component systems where cross-correlations would not be accounted for by other DOE approaches.
Standard DOE methods usually require you to vary only a limited number of inputs at any one time in your experimental design. With ML, you don’t have to identify which inputs are most important (thus potentially building bias into your design). You can ask the ML to explore all of the inputs simultaneously and it will find those that are most significant.
Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification
A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. Current high-performance engineering alloys commonly suffer from issues when processed using additive manufacturing methods. These include cracking, porosity, elemental segregation, and anisotropy. The computational method reported here enables the identification of new alloy compositions that have the highest likelihood of simultaneously satisfying a range of target properties, including criteria specific to additive manufacturing. The efficacy of this method is demonstrated with the design of a new alloy more amenable to laser-blown-powder direct-energy-deposition. The method may be readily extended to the optimization of other alloy types and process methods.
Machine learning predictions of superalloy microstructure
Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys. Additionally, the model predicts the phase composition with uncertainties unlike the traditional CALPHAD method.
Accelerating the Design of Automotive Catalyst Products Using Machine Learning
The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.