To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is e...To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.展开更多
Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector mac...Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.展开更多
基金This project is supported by National Natural Science Foundation of China (No. 50575139)
文摘To aim at prototype parts fabricated with fused deposition modeling （FDM） process, the problems how to improve and enhance their surface micro-precision are studied. The producing mechanism of surface roughness is explained with three aspects concretely including the principle error of rapid prototyping （RP） process, the inherent characteristics of FDM process, and some mi- cro-scratches on the surface of the extruded fiber. Based on the micro-characters of section shape of the FDM prototype, a physical model reflecting the outer shape characters is abstracted. With the physical simplified and deduced, the evaluating equations of surface roughness are acquired. According to the FDM sample parts with special design for experimental measurement, the real surface roughness values of different inclined planes are obtained. And the measuring values of surface roughness are compared with the calculation values. Furthermore, the causes of surface roughness deviation between measuring values and calculation values are respectively analyzed and studied. With the references of analytic conclusions, the measuring values of the experimental part surface are revised, and the revised values nearly accord with the calculation values. Based on the influencing principles of FDM process parameters and special post processing of FDM prototype parts, some concrete measures are proposed to reduce the surface roughness of FDM parts, and the applying effects are better.
文摘Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.
