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Experimental research on laser additive remanufacturing technology of heavy-duty sprocket was carried out. The influences of laser power, scanning speed and powder feeding rate on cladding height, cladding area, melting area and dilution rate were compared and analyzed. The prediction models of the combination of process parameters with the geometric characteristics of cladding layer and dilution were established. A multi-objective process parameter optimization model with the maximum cladding height and cladding area maximum, the minimum melting area and dilution rate as objective functions was established, and the model was optimized and solved based on MOPSO algorithm. The laser additive remanufacturing repairing experiment of damaged sprocket was carried out by using the optimal parameters combination, and the microstructure and mechanical properties of the repaired region were analyzed. The results show that the scanning speed and powder feeding rate are the main factors influencing the geometric characteristics and dilution of the cladding area, and the models have good prediction accuracy. The optimal process parameters (1150 W, 950 mm/min, 3.8 rad/min) obtained by MOPSO algorithm is adopted to repair the damaged sprocket. The repaired area without cracks and pores, the cladding layer shows good metallurgical bonding with the substrate and the microhardness is twice that of the substrate. The experimental results prove that the laser additive remanufacturing technology is feasible to repair the damaged heavy-duty sprocket and has a strong engineering application prospect.

Texture exists in lots of the products, such as wood, beef and compression tea. These abundant and stochastic texture patterns are significantly different between any two products. Unlike the traditional digital ID tracking, in this paper, we propose a novel approach for product traceability, which directly uses the natural texture of the product itself as the unique identifier. A texture identification based traceability system for Pu'er compression tea is developed to demonstrate the feasibility of the proposed solution. With tea-brick images collected from manufactures and individual users, a large-scale dataset has been formed to evaluate the performance of tea-brick texture verification and searching algorithm. The texture similarity approach with local feature extraction and matching achieves the verification accuracy of 99.6% and the top-1 searching accuracy of 98.9%, respectively.

Food ingredient adulteration, especially the adulteration of milk and dairy products, is one of the important issues of food safety. A rapid, simple, sensitive, specific, and low-cost DNA detection method was established to screen bovine milk adulterated in three different kinds of special milk (camel, horse, and goat milk). This method can detection both bovine milk and special milk in a single duplex polymerase chain reaction (duplex PCR). In this study, specific primers of duplex PCR were designed based on 16S-RNA genes from camel and bovine mitochondria and D-LOOP genes from horse and goat mitochondria. The designed primers had good specificity and could accurately amplify the target fragments (711 bp, 584 bp, 241 bp, and 184 bp). The duplex PCR was applied to the binary mixtures of raw milk fixed percentage and processed dairy products (freeze-dried, pasteurized, and ultra-high temperature (UHT) sterilized with the same mixtures and commercial samples). The limit of detection (LOD) of special milk adulterated with bovine milk was 0.1% in raw milk mixture. Pasteurized and UHT sterilized raised the LOD to 0.2% and 0.5%, but freeze-dried did not raise the LOD. Duplex PCR maintained preferable selectivity and sensitivity in the detection of samples with different processed, and it is expected to be used in market detection.

The aim of this study is to reduce the influence of machine tools spindle thermal error on machining accuracy in load machining state. The temperature and thermal error measuring system and experiments of the 3-axis vertical milling machine tool spindle in idling and load machining state were established and carried out. The differences in temperature and thermal error between the idling and load machining states were analyzed. Upon combining the fuzzy clustering and gray correlation algorithm, the temperature-sensitive points were optimized. The thermal error prediction models of machine tool spindle system in load machining state with the optimal specific cutting energy were established based on the adaptive chaotic particle swarm optimization algorithm, and the model prediction effects were evaluated. The results showed that the spindle system temperature and thermal error in load machining were higher than the idle state. Two temperature-sensitive points were selected that not only reduced the redundancy of temperature measuring points but also ensured the model prediction accuracy. The thermal error models prediction accuracy was above 90%, and the root mean square error and residual error were better than PSO and regression. The experimental results showed that the thermal error prediction models have a high prediction accuracy and engineering application value.

The thermal error of machine tool spindle system has become an important factor affecting machining accuracy. Measurement experiments of a 3-axis vertical milling machine spindle system were conducted to assess temperature fields and thermal errors. The fuzzy clustering and gray correlation algorithms were adopted to cluster the temperature measuring points and identify the temperature-sensitive points. Based on the adaptive chaos particle swarm optimization algorithm, thermal error models were established in the axial and radial directions for the spindle system, and the compensation effects were evaluated by the workpiece machining accuracy. The results showed that the number of temperature measuring points was reduced from 12 to 6. The residual range of measured and predicted thermal error values in the axial direction was 6.17–4.19 μm, and the modeling accuracy was 95.53%. The radial residual ranges were − 2.75–3.05 μm and − 2.10–2.15 μm, and the modeling accuracies were 90.74% and 91.10%, respectively. The model compensation effect was demonstrated remarkably in the verification experiments. The thermal error models showed high prediction precision, could improve machining accuracy and had strong engineering application value.