Designers often constrain their conceptual design as they sketch the idea to make sure the final concept solution has the expected functionality. Proposed system enables userdefined constraints created as mathematical formulas according to a syntax provided using geometrical features, ready-to-use functions, and mathematical operators. In this way, the users will be able to use computers' generative powers for their specific needs without being limited by built-in constraints. Defined constraints can be used in a sampling method as objectives to explore new design samples with better performance or filter out the undesired designs after the samples are generated. Thanks to its flexibility that lets the users define their constraints, the proposed method will also enhance the usability of generative design studies.

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Machine learning (ML) is a well-established research topic in Industry 4.0 is boosting its adoption. ML is also used for manufacturing cost estimation during design. Such approaches are commonly used to estimate the cost of mass-produced parts. Many consolidated historical data are available for training the regression models. Unfortunately, very often, such a database of data is not available.The paper defines an ML approach for parametric cost estimation of axisymmetric components. The data for training the ML model derives from automatic software for analytically estimating the manufacturing cost. With a proper set of simulations, the tool can generate a large amount of data for training. The paper presents the steps for developing a parametric cost model using ML. The approach is based on CRoss Industry Standard Process for Data Mining method. The proposed method was used to develop one cost model (to estimate the total cost that considered raw material and manufacturing cost). The obtained Relative Error is 23.52% ± 1.37%, coherent with E2516 − 11, Standard Classification for Cost Estimate Classification System.