فهرست نشریات علمی دسترسی آزاد ایران

The City Ruins in Jining in Inner Mongolia were an important transportation and trade hub in the Yuan Dynasty. Archaeological excavations have unearthed a large number of black-glazed oil-spot porcelain pieces, providing significant materials for studying ancient Chinese ceramic firing techniques. Using technologies such as super depth-of-field microscopy, X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS), and Raman spectroscopy, this paper conducts a scientific archaeological analysis on representative samples. The results show that the chemical composition of the body of the glazed oil-spot porcelain unearthed from Jining site possesses remarkable characteristics of northern Chinese porcelain, which has rich aluminum and poor silicon. Analysis of the micro-structure and phase of the crystals on the glaze surface reveals an iron oxide crystalline phase transformation in the order of γ-Fe2O3→ε-Fe2O3→α-Fe2O3. The formation mechanism of the glaze surface is identified as anorthite crystallization --intergranular liquid phase separation -iron oxide crystallization. The glaze's chemical composition features rich Fe2O3 (6.268%), rich CaO (5.527%), and poor Al2O3 (14.945%), with an average RO content of 0.76. Analysis of the micro-structure and phase of the crystals on the glaze surface reveals an iron oxide crystalline phase transformation in the order ofγ-Fe2O3→ε-Fe2O3→α-Fe2O3. The formation mechanism of the glaze surface is identified as anorthite crystallization --intergranular liquid phase separation -iron oxide crystallization. This paper utilized an Artificial Neural Network (ANN) to better understand the conditions and interrelationships among parameters. These practical networks have proven successful in diverse functions and complex problems, even with training data errors. The study showed neural networks to investigate enamel phase transformation, and formation by manipulating chemical compositions, with acceptable error confirmed through linear regression. The ANN model was able to predict the enamel microstructure, phase transformation, and formation mechanism with an error of less than 1% compared to the target values.