Laser cladding technology serves as a pivotal technique in industrial production, especially in the realms of additive manufacturing, surface enhancement, coating preparation, and the repair of part surfaces. This study investigates the influence of metal powder composition and processing parameters on laser cladding coatings utilizing the Taguchi orthogonal experimental design method. To optimize the laser cladding parameters, multi-response grey relational analysis (GRA) was employed, aiming to improve both the microhardness and the overall quality of the coatings. The optimal parameter combinations identified through GRA were subsequently validated through experimental tests. The results reveal that the microhardness and quality of the coatings are substantially influenced by several critical factors, including the powder feed rate, laser power, high-entropy alloy (HEA) addition rate, scanning speed, and substrate tilt angle. Specifically, the powder feed rate exerts the most significant effect on the microhardness, dilution rate, and average contact angle. In contrast, laser power primarily impacts the mean contact angle difference. The HEA addition rate notably affects the mean contact angle difference, while the scanning speed affects the microhardness and the substrate tilt angle influences the average contact angle. The results of the validation experiment showed a deviation of only 0.95% from the predicted values, underscoring the efficacy of the grey relational analysis (GRA) in optimizing the laser cladding process parameters. The methodology presented in this paper can be applied to determine the ideal processing parameters for multi-response laser cladding processes, encompassing applications such as surface peening and surface repair.

Effects of germination and aeration treatment following segmented moisture conditioning on the γ-aminobutyric acid accumulation in germinated brown rice

The objective of this study was to analyze the content of γ-aminobutyric acid (GABA) in germinated brown rice (GBR) by using near-infrared spectroscopy (NIRS) and the pretreatment method of wavelet de-noising (WD). The prediction accuracy of the NIRS model established by the Daubechies5 wavelet basis function at 3 level denoising treatment is the highest, the correlation coefficient for calibration (rc) is 0.931, the root mean square error of calibration (RMSEC) is 0.4038 mg/100 g, the Bias of calibration is 0.006, the correlation coefficient for prediction (rp) is 0.916, the root mean square error of prediction (RMSEP) is 0.4329 mg/100 g, the Bias of prediction is 0.010, and the ratio of performance to deviation (RPD) is 4.911. Results showed that the predicted and actual values had high correlation. Therefore, these results indicate that the NIRS model treated by WD is feasible to detect GABA content in GBR rapidly and nondestructively. Keywords: near-infrared spectroscopy, wavelet transformation, germinated brown rice, γ-aminobutyric acid, quantitative analysis DOI: 10.25165/j.ijabe.20211403.6178 Citation: Zhang Q, Liu N, Wang S S, Pan L Q. Nondestructive determination of GABA in germinated brown rice with near infrared spectroscopy based on wavelet transform denoising. Int J Agric & Biol Eng, 2021; 14(3): 200–206.

Discrete element simulation of the grain mixing law in brown rice germination device