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In many industries, the development is aimed towards Industry 4.0, which is accompanied by a movement from large to small quantities of individually adapted products in a multitude of variants. In this scenario, it is essential to be able to provide the price for these small batches fast and without additional costs to the customer. This is a big challenge in technical applications in which this price calculation is in general performed by local experts. From the age of expert systems, one knows how hard it is to achieve a formalised model-based on expert knowledge. So it makes sense to use today’s machine learning techniques. Unfortunately, the small batches combined with typically small and midsize production enterprises (SMEs) lead to smaller databases to rely on. This comes along with data which is often based on 3D data or other sources that lead in the first step to a lot of features. In this paper, we present an approach for such use cases that combines the advantages of model-based approaches with modern machine learning techniques, as well as a discussion on feature generation from CAD data and reduction to a low-dimensional representation of the customer requests.

Case Study on Model-based Application of Machine Learning using Small CAD Databases for Cost Estimation