Metadata about: Abstract The manufacturing process for single-point diamond turning germanium (Ge) can be complex when it comes to freeform IR optics. The multi-variant problem requires an operator to understand that the machining input parameters and choice of tooling will dictate the efficiency of generating surfaces with the appropriate tolerances. Ge is a brittle material and exhibits surface fracture when diamond turned. However, with the introduction of a negatively raked tool, surface fracture can be suppressed such that plastic flow of the material is possible. This paper focuses on the application and evaluation of machine learning methods to better assist the prediction of surface roughness parameters in Ge and provides a comparison with a well-understood ductile material, copper (Cu). Preliminary results show that both classic machine learning (ML) methods and artificial neural network (ANN) models offer improved predictive capability when compared with analytical prediction of surface roughness for both materials. Significantly, ML and ANN models were able to perform well for both Ge, a brittle material prone to surface fracture, and the more ductile Cu. ANN models offered the best prediction tool overall with minimal error. From a computational perspective, both ML and ANN models were able to achieve good results with smaller datasets than typical for many ML applications—which is beneficial since diamond turning can be costly.

About: Abstract The manufacturing process for single-point diamond turning germanium (Ge) can be complex when it comes to freeform IR optics. The multi-variant problem requires an operator to understand that the machining input parameters and choice of tooling will dictate the efficiency of generating surfaces with the appropriate tolerances. Ge is a brittle material and exhibits surface fracture when diamond turned. However, with the introduction of a negatively raked tool, surface fracture can be suppressed such that plastic flow of the material is possible. This paper focuses on the application and evaluation of machine learning methods to better assist the prediction of surface roughness parameters in Ge and provides a comparison with a well-understood ductile material, copper (Cu). Preliminary results show that both classic machine learning (ML) methods and artificial neural network (ANN) models offer improved predictive capability when compared with analytical prediction of surface roughness for both materials. Significantly, ML and ANN models were able to perform well for both Ge, a brittle material prone to surface fracture, and the more ductile Cu. ANN models offered the best prediction tool overall with minimal error. From a computational perspective, both ML and ANN models were able to achieve good results with smaller datasets than typical for many ML applications—which is beneficial since diamond turning can be costly.

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