In the current work, the effects of steam and boiling water blanching on the drying characteristics, water distribution, microstructure, and contents of bioactive substances of Gastrodia elata (G. elata) were explored. Results showed that the degree of steaming and blanching was related to the core temperature of G. elata. The steaming and blanching pretreatment increased the drying time of the samples by more than 50%. The low-field nuclear magnetic resonance (LF-NMR) of treated samples showed that the relaxation time corresponded to water molecule states (bound, immobilized, and free) and G. elata became shorter, which indicated a reduction in free moisture and increased resistance of water diffusion in the solid structure during drying. Hydrolysis of polysaccharides and gelatinization of starch granules was observed in the microstructure of treated samples, which was consistent with changes in water status and drying rates. Steaming and blanching increased gastrodin and crude polysaccharide contents and decreased p-hydroxybenzyl alcohol content. These findings will contribute to a better understanding of the effect of steaming and blanching on the drying behavior and quality attributes of G. elata.

This study aimed to optimize the postharvest blanching and drying process of blueberries using high-humidity air impingement (HHAIB) and hot-air-assisted infrared (HAIR) heating. A novel pilot-scale hot-air-assisted carbon-fiber infrared (IR) blanching/drying system was developed. Fresh blueberries with an average diameter of 10~15 mm were first blanched with high-humidity air at 110 °C and 12 m/s velocity for different durations (30, 60, 90, and 120 s); subsequently, the preblanched blueberries were dried at different IR heating temperatures (50, 60, 70, 80, and 90 °C) and air velocities (0.01, 0.5, 1.5, and 2.5 m/s), following a factorial design. The drying time (DT), specific energy consumption (SEC), ascorbic acid content (VC), and rehydration capacity (RC) were determined as response variables. A three-layer feed-forward artificial neural network (ANN) model with a backpropagation algorithm was constructed to simulate the influence of blanching time, IR heating temperature, and air velocity on the four response variables by training on the experimental data. Objective functions for DT, SEC, VC, and RC that were developed by the ANN model were used for the simultaneous minimization of DT and SEC and maximization of VC and RC using a nondominated sorting genetic algorithm (NSGA II) to find the Pareto-optimal solutions. The optimal conditions were found to be 93 s of blanching, 89 °C IR heating, and a 1.2 m/s air velocity, which resulted in a drying time of 366.7 min, an SEC of 1.43 MJ/kg, a VC of 4.19 mg/100g, and an RC of 3.35. The predicted values from the ANN model agreed well with the experimental data under optimized conditions, with a low relative deviation value of 1.43–3.08%. The findings from this study provide guidance to improve the processing efficiency, product quality, and sustainability of blueberry postharvest processes. The ANN-assisted optimization approach developed in this study also sets a foundation for the smart control of processing systems of blueberries and similar commodities.

The objective of this study was to develop an efficient drying technology for poria cubes in order to improve product quality. Poria cubes were dried using different methods, including air impingement drying, infrared-assisted air impingement drying, vacuum drying, two-stage vacuum drying, and infrared-assisted air impingement drying. The results were compared with those from hot air drying. For the two-stage drying, the tested conditions were the first stage of vacuum drying with temperatures between 65–85 °C and a switching moisture ratio of 70–90%. The second stage infrared-assisted air impingement drying also had temperatures 65–85 °C. The drying kinetics (effective moisture diffusivity (D_eff), Biot number (Bi), and mass transfer coefficient (k) were studied via the product qualities (broken ratio, firmness, microstructure, and water-soluble polysaccharide content) and specific energy consumption (SEC) of the drying processes. The results showed that two-stage drying led to the lowest drying time and energy consumption, and also obtained the best qualities. Box–Behnken experimental design with response surface methodology (RSM) was used to optimize the two-stage operating conditions as 82 °C under vacuum drying until a moisture content of 81% and a temperature of 69 °C with infrared-assisted air impingement drying was achieved. These findings suggested that two-stage vacuum and infrared-assisted air impingement drying is a promising method for producing high quality and energy efficient dried poria cubes.

Biological bone screws play an important role in fixing fractures and bone defects. The machining of helical grooves on xenogenic materials is a key part of fabricating biological bone screws. The fabrication of helical grooves on bovine bone using micro-abrasive air jets was investigated in this paper. The helical groove shapes were classified and their formation mechanisms were studied. Analyses of the material removal mechanism and the effect of process parameters on the groove shapes were carried out. The results show that the helical grooves could be effectively machined using micro-abrasive air jets with a spring mask. The shapes of the helical grooves could be classified as U-, V-, and W-shaped. Cracks that propagated along the cement line may have led to the formation of a slot. Meanwhile, cracks that propagated in the interstitial lamella may have led to the formation of ridges. The slots and ridges resulted in the appearance of stripes on the groove bottom. The cracks propagated along the axial direction of the osteon at the same time as it propagated into the osteon, leading to the formation of dimples on the groove sidewall. The experimental method proposed in this study can be regarded as a suitable method to fabricate helical grooves on bones.

With the increased requirement of miniaturization structures on hard and brittle substrates, micro abrasive air jet turning technology has become a promising machining technology for manufacturing miniaturization structures. This paper presents an investigation on multi-pass radial-mode micro abrasive air jet turning of fused silica. The effect of machining parameters on the depth of cut and materials removal rate was analyzed. The waviness and striation formation mechanisms were studied. It was found that increasing the number of passes significantly increases the depth of cut and materials removal rate. When the surface speed is reduced, the depth of cut and materials removal rate significantly increases. The waviness may be attributed to the intersection and overlap of the adjoint jet footprint. The crossed striations have been found, which may lead to the elimination of the striation. A predictive model of the material removal rate was developed using the dimensional analysis method. The model was assessed and shown to be able to give adequate predictions.

Abrasive air jet machining is a burgeoning non-traditional machining technology particularly suitable for machining brittle non-metallic materials and metals with high hardness. It is very challenging to select the optimal process parameters to achieve desirable machining performance metrics, such as maximizing material removal rate and minimizing machining width while controlling machining depth. In this study, we aimed to achieve multi-objective process parameter optimization for abrasive air jet machining of silicon based on the artificial bee colony algorithm. A series of experiments was carried out to investigate the effect of process parameters, including air pressure, standoff distance, and nozzle traverse speed, on material removal rate, machining width, and machining depth. Mathematical models for machining performance metrics were developed by regression analysis, and a multi-objective optimization model was further formulated. The artificial bee colony algorithm was proposed to solve the optimization problem, and a set of Pareto-optimal solutions was found. The results indicate that the artificial bee colony algorithm is an effective method for multi-objective process parameter optimization in abrasive air jet machining.