Finding optimal machine configurations or material compositions for a specific application is a complex and resource intensive task. Especially in fields like additive manufacturing, CNC-machining and other manufacturing methods, where the number of machine parameters is high, such an approach leads to elevated costs and longer go-to-market time whenever an implementation of a new production process or material innovation is required. In areas like chemical production, biochemistry, and material science, increase in the complexity of material mixtures results in rising costs and longer development time for new material compositions.
In many domains, e.g. additive manufacturing, a change in process parameters can alter material properties. That opens new innovation opportunities, at the same time requiring a higher level of complexity. This complexity makes the use of advanced software solutions a necessity.
Out of these reasons we at Exponential Technologies Ltd. have developed a unique optimization software, called xT smart_DoE. Our software, similar to a classical Design of Experiments (DoE) software, guides the user through the experiments to accelerate the development process. However, unlike classical DoE software, our solution doesn’t require statistical expertise from the user. Additionally, the efficiency of our algorithms allows to find satisfying solution in less trials than classical DoE and other AI algorithms. This saves time and money in the R&D process.
We developed our solution specifically using small data algorithms that are very versatile and resilient. The resulting process is called active learning. This allows us to adjust our software for many applications without any reprogramming. This makes our solution much more flexible compared to other AI solutions on the market.
xT smart_DoE has the potential to be further automated by using APIs to directly communicate with the machine and by automating the feedback (e.g. using optical tomography).