Application of Machine Learning Methods to the Open-Loop Control of a Freeform Fabrication System

Freeform fabrication of complete functional devices requires the fabrication system to achieve well-controlled deposition of many materials with widely varying material properties. In a research setting, material preparation processes are not highly refined, causing batch property variation, and cost and time may prohibit accurate quantification of the relevant material properties, such as viscosity, elasticity, etc. for each batch. Closed-loop control based on the deposited material road is problematic due to the difficulty in non-contact measurement of the road geometry, so a labor-intensive calibration and open-loop control method is typically used. In the present work, k-Nearest Neighbor and Support Vector Machine (SVM) machine learning algorithms are applied to the problem of generating open-loop control parameters which produce desired deposited material road geometry from a description of a given material and tool configuration comprising a set of qualitative and quantitative attributes. Training data for the algorithms is generated in the course of ordinary use of the SFF system as the results of manual calibration of control parameters. Given the large instance space and the small training data set compiled thus far, the performance is quite promising, although still insufficient to allow complete automation of the calibration process. The SVM-based approach produces tolerable results when tested with materials not in the training data set. When control parameters produced by the learning algorithms are used as a starting point for manual calibration, significant operator time savings and material waste reduction may be achieved.