運用類神經網路最佳化圓球壓痕測試於生醫材料黏彈性測量之研究

Abstract

材料性質的測定不論是對於發展理論以及實務應用來說都是非常重要的事情，準確的量測材料性質才可以推導出正確的理論以及在實務上做正確的評估及應用。在材料黏彈性質測定領域中，使用圓球壓痕測試來進行測定是很常見的方法，然而，使用圓球壓痕測試只能測到材料的局部黏彈性質，為相對的性質，而非材料真正的性質。 為了準確得到材料真正的黏彈性質，本研究探討使用類神經網路對生醫材料進行圓球壓痕測試而得到的黏彈性質測量結果進行最佳化之方法，並設計不同的神經網路架構進行學習，輸出最佳的材料黏彈性之預測結果，同時降低模型預測之誤差。 本研究主要透過有限元素模擬軟體進行圓球壓痕測試，將應力鬆弛試驗過程中的負載之數據輸出，並藉由赫茲接觸力學將其轉換成應力，接著再使用Generalized Maxwell Solid Model (3rd order) 對數據進行曲線擬合取得實驗的黏彈性質之測量值，將測量值以及有限元素模擬軟體中材料設定之實際值輸入至神經網路進行學習，並進行材料黏彈性之預測。 由研究結果顯示，運用類神經網路對圓球壓痕測試於生醫材料黏彈性測量結果進行最佳化是可行的。最終，本研究訓練出兩種神經網路模型用於最佳化圓球壓痕測試於生醫材料黏彈性之測量，在與本研究之有限元素模擬架設條件相同下，透過此兩種神經網路模型可以準確地預測材料之黏彈性質。

Material properties characterization is very important for the development of theory and practical applications. Only by accurately characterize material properties can we derive correct theories and make correct evaluations and applications in practice. In the field of material viscoelastic properties characterization, it is very common to use the spherical indentation test to measure. However, the spherical indentation test can only measure the local viscoelastic properties of the material, which are relative properties, not the true nature of the material. In order to accurately obtain the true viscoelastic properties of materials, this study explores a method to optimize the viscoelastic properties measurement results obtained from spherical indentation testing of biomedical materials using neural networks, and designs two different neural networks. The framework learns to output the best prediction result of material viscoelasticity, and at the same time reduces the error of model prediction. In this study, the spherical indentation test is mainly carried out through finite element simulation software, and the load data during the stress relaxation test is output, and converted into stress by Hertzian contact mechanics, and then the Generalized Maxwell Solid Model (3rd order) is used to curve fitting the data to obtain the experimental viscoelastic measurement value, input the measurement value and the actual value of the material setting in the finite element simulation software into the neural network for learning, and predict the viscoelasticity of the material. The results of the study show that it is feasible to optimize the viscoelasticity measurement results of biomedical materials using the spherical indentation test by neural network. Finally, this study trains two neural network models to optimize the measurement of viscoelasticity of biomedical materials by spherical indentation test. Both model can accurately predict the viscoelastic properties of materials.