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In this study, rice protein was used as raw material to explore the effects of γ-irradiation treatment doses (0, 0.5, 1, 2, 3, 5 kGy) on the physicochemical properties of rice protein (particle size, zeta potential, secondary structure, scanning electron microscope microstructure), surface hydrophobicity (H0), thermal stability), functional properties (solubility, water and oil retention, emulsification) and sensory quality. The results show that when the γ-irradiation dose is 2 kGy, the average particle size of rice protein is the smallest, the absolute value of the potential is the highest 33.58 mV, the content of β-sheets in the secondary structure is at least 31.16 ± 0.16, and the content of random curl is at most 14.56 ± 0.06, the surface of the microstructure is rough and the degree of pore depression is the deepest, the highest H0 is 160.45 ± 2.98, the minimum denaturation temperature (Td) and enthalpy (△H) are 70.49 ± 0.05 °C and 1.30 ± 0.01 J/g, which shows that γ-irradiation treatment can be significant affect the physicochemical properties of rice protein. When the irradiation dose is 2 kGy, the highest solubility of rice protein is 69.18 ± 1.07%, and the highest water and oil holding capacity are 5.89 ± 0.08 g/g and 3.45 ± 0.04 g/g, respectively. The highest emulsification activity and emulsification stability are 45.65 ± 1.26 m2/g and 208.33 ± 4.79 min, which shows that γ-irradiation treatment can improve the functional properties of protein. When the irradiation dose was less than 5 kGy, the sensory quality of rice protein was not significantly affected. The research results provide a theoretical basis for the deep processing and value-added utilization of rice protein by γ-irradiation technology.

In more recent times, research on various aspects of the Wire Arc Additive Manufacturing (WAAM) process has been conducted, and efforts into monitoring the WAAM process for defect identification have increased. Rapid and reliable monitoring of the WAAM process is a key development for the technology as a whole, as it will enable components produced by the process to be qualified to relevant standards and hence be deemed fit for use in applications such as those found in the aerospace or naval sectors. Intelligent algorithms provide inbuilt advantages in processing and analysing data, especially for the large data sets generated during the long manufacturing cycles. Interdisciplinary engineering (IDE) furnishes a concept integrating computer science and industrial system manufacturing engineering together to treat large amounts of process monitoring data. In this work, a WAAM process monitoring and defect detection system integrating intelligent algorithms is presented. The system monitors welding arc current and voltage signals produced by the WAAM process and makes use of a support vector machine (SVM) learning method to identify disturbances to the welding signal which indicate the presence of potential defects. The incremental machine learning models developed in this work are trained via statistical feature analysis of the welding signals and a novel quality metric that improves detection rates is also presented. The incremental learning approach provides an efficient means of detecting welding-based defects, as it does not require large quantities of data to be trained to an operational level (addressing a major drawback of other machine learning methods). A case study is presented to validate the developed system, results show that it was able to detect a set of defects with a success rate greater than 90% F1-score.