Heterogeneous Sensing and Bayesian Optimization for Smart Calibration in Additive Manufacturing Process

Fused Filament Fabrication (FFF) is an extrusion-based additive manufacturing process that utilizes a filament material melted through a hot end extruder to generate a component. Despite the great potential of the process to drastically reduce time-to-produce, cost and material waste for the creation of geometrically complex components, the presence of diverse defects deteriorate the quality of the final build. Defects in FFF (e.g., voids, stringing, and varying track width) are primarily linked to improper calibration of parameters, including feed speed, extrusion speed, extruder temperature, and build plate temperature. Trial and error is the most common practice implemented to manually offset baseline parameters using an array of components generated with varying process parameters. However, fabrication with manual adjustment not only is time consuming, but also leads to a suboptimal solution that jeopardizes the strength and integrity of the generated components. We propose a novel Bayesian Optimization (BO) methodology in conjunction with heterogeneous sensing to determine optimal process parameters with a minimum number of experiments. BO consists of two steps: First, a Gaussian Process as a surrogate model that maps the relationship between controllable parameters (e.g., feed rate/flow rate ratio, extrusion temperature, and layer height) and build quality (i.e., the objective function that is derived from sensing data). Second, an acquisition function is defined from this surrogate to decide where to sample. We design build quality characterization model that formulated as an objective-scoring algorithm that returns the proportion of the effective specimen sensor measurements divided by the desired values. The experimental results on real-world case study shows that the proposed BO is capable of determining the values for parameters in just 7 steps with quality improvement of 0.036 from the best trial quality.