Advances in computational algorithms and the significant increase in computational resources currently available has led to the new and exciting field of Artificial Intelligence, and Machine Learning in particular. This has reduced the need to perform a large number of experiments in order to obtain the optimized process parameters. For example, consider a scenario, where a coating formulation and process is dependent on more than a dozen parameters. Using Machine Learning (ML) algorithms, such as Gaussian Processes, the situation can be expressed as:
ŷ = ƒ(x1,x2,.…….,xn)
Where, ŷ is the characteristic of the coating we need to optimize (e.g., hardness) and xi are different composition and process parameters (e.g., type and amount of monomer of oligomer, curing time, coating thickness, application rate), and f is a function of xi. The ML algorithms can determine the shape of f with a limited number of experiments (called training set) that can then be used to predict the value of the desired characteristic ŷ, with given inputs. Machine Learning can also be used to determine which parameters are more important (and need to be observed more closely during manufacturing) and which parameters are less important.
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The Machine Learning algorithm is applicable to all classes of coatings: waterborne, solventborne, solvent-free, thermally cured, and UV cured coatings. The technology can also be applied to Battery Optimization. Contact us today using the form below to explore how the ML approach can be adapted for your specific need in a cost effective manner.