Studying the Effect of Short Carbon Fiber on Fused Filament Fabrication Parts Roughness via Machine Learning.

Along with the characteristic staircase effect, short carbon fibers, added to reinforce Fused Filament Fabrication parts, can significantly worsen the resulting surface finishing. Concerning this topic, the present work intends to improve the existing knowledge by analyzing 2400 measurements of arithmetic mean roughness Ra corresponding to different combinations of six process parameters: the content by weight of short carbon fibers in polyethylene terephthalate glycol (PETG) filaments f, layer height h, surface build angle θ, number of walls w, printing speed s, and extruder diameter d. The collected measurements were represented by dispersion and main effect plots. These representations indicate that the most critical parameters are θ, f, and h. Besides, up to a carbon fiber content of 12%, roughness is mainly affected by the staircase effect. Hence, it would be likely to obtain reinforced parts with similar roughness to unreinforced ones. Different machine learning methods were also tested to extract more information. The prediction model of Ra using the Random Forest algorithm showed a correlation coefficient equal to 0.94 and a mean absolute error equal to 2.026 µm. In contrast, the J48 algorithm identified a combination of parameters (h = 0.1 mm, d = 0.6 mm, and s = 30 mm/s) that, independent of the build angle, provides a Ra < 25 µm when using a 20% carbon fiber PETG filament. An example part was printed and measured to check the models. As a result, the J48 algorithm correctly classified surfaces with low roughness (Ra < 25 µm), and the Random Forest algorithm predicted the Ra value with an average relative error of less than 8%.

Build Time Estimation for Fused Filament Fabrication via Average Printing Speed.

Build time is a key issue in additive manufacturing, but even nowadays, its accurate estimation is challenging. This work proposes a build time estimation method for fused filament fabrication (FFF) based on an average printing speed model. It captures the printer kinematics by fitting printing speed measurements for different interpolation segment lengths and changes of direction along the printing path. Unlike analytical approaches, printer users do not need to know the printer kinematics parameters such as maximum speed and acceleration or how the printer movement is programmed to obtain an accurate estimation. To build the proposed model, few measurements are needed. Two approaches are proposed: a fitting procedure via linear and power approximations, and a Coons patch. The procedure was applied to three desktop FFF printers, and different infill patterns and part shapes were tested. The proposed method provides a robust and accurate estimation with a maximum relative error below 8.5%.

Alternative Calibration of Cup Anemometers: A Way to Reduce the Uncertainty of Wind Power Density Estimation.

This study presents a procedure to reduce the uncertainty of wind power density estimations, which is useful to improve the energy production predictions of wind farms. Power density is usually determined from the wind speed measured by a cup anemometer and the air density value (conventional procedure). An alternative procedure based on wind speed and dynamic pressure estimations provided by a cup anemometer is proposed. The dynamic pressure is obtained by means of a calibration curve that relates the anemometer rotation frequency and the dynamic pressure measured by a Pitot tube. The quadratic regression, used to define the calibration curve, and its uncertainty are both detailed. A comparison between the alternative procedure and the conventional one points out the advantage of the proposed alternative since results show a high reduction of the indirect measurement uncertainty of wind power density.

Force Prediction for Incremental Forming of Polymer Sheets.

Incremental sheet forming (ISF) is gaining attention as a low cost prototyping and small batch production solution to obtain 3D components. In ISF, the forming force is key to define an adequate setup, avoiding damage and reducing wear, as well as to determine the energy consumption and the final shape of the part. Although there are several analytical, experimental and numerical approaches to estimate the axial forming force for metal sheets, further efforts must be done to extend the study to polymers. This work presents two procedures for predicting axial force in Single Point Incremental Forming (SPIF) of polymer sheets. Particularly, a numerical model based on the Finite Element Model (FEM), which considers a hyperelastic-plastic constitutive equation, and a simple semi-analytical model that extends the known specific energy concept used in machining. A set of experimental tests was used to validate the numerical model, and to determine the specific energy for two polymer sheets of polycarbonate (PC) and polyvinyl chloride (PVC). The approaches provide results in good agreement with additional real examples. Moreover, the numerical model is useful for accurately predicting temperature and thickness.