Prediction of line heating deformation on sheet metal based on an ISSA-ELM model.

A prediction method based on an improved salp swarm algorithm (ISSA) and extreme learning machine (ELM) was proposed to improve line heating and forming. First, a three-dimensional transient numerical simulation of line heating and forming was carried out by applying a finite element simulation, and the influence of machining parameters on deformation was studied. Second, a prediction model for the ELM network was established based on simulation data, and the deformation of hull plate was predicted by the training network. Additionally, swarm intelligence optimization, particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the salp swarm algorithm (SSA) were studied while considering the shortcomings of the ELM, and the ISSA was proposed. Input weights and hidden layer biases of the ELM model were optimized to increase the stability of prediction results from the PSO, SOA, SSA and ISSA approaches. Finally, it was shown that the prediction effect of the ISSA-ELM model was superior by comparing and analyzing the prediction effect of each prediction model for line heating and forming.

Preparation and Evaluation of PDMS/Carbon Soot Particles Superhydrophobic Biomimetic Composite Coating with Self-Cleaning and Durability.

In this paper, a superhydrophobic biomimetic composite coating was fabricated on brass by electrochemical etching, brushing PDMS adhesive layer, and depositing carbon soot particles. Due to the microstructure and the optimized ratio of PDMS, the contact angle of the superhydrophobic coating is up to 164° and the sliding angle is only 5°. The results of optical microscopy and morphometric laser confocal microscopy show that the prepared coating surface has a rough hierarchical structure. A high-speed digital camera recorded the droplet bouncing process on the surface of the superhydrophobic coating. The self-cleaning property of the coatings was evaluated by applying chalk dust particles as simulated solid contaminants and different kinds of liquids (including grape juice, beer, cola, and blue ink) as liquid contaminants. The coating remained superhydrophobic after physical and chemical damage tests. This work presents a strategy for fabricating superhydrophobic biomimetic composite coatings with significant self-cleaning properties, durability, and shows great potential for practical engineering applications.

Study on Preparation and Grinding Performance of Vitrified Bond CBN Grinding Wheel with Controllable Porosity.

Vitrified bond cubic boron nitride (CBN) grinding wheel specimens with controllable porosity were prepared by regulating the pore former dextrin content and varying the forming pressure, and the performance of the grinding camshaft was studied. The porosity of the specimens increases with the increase in dextrin content, and decreases first and then increases with the increase in the forming pressure. The grinding experiments show that the dextrin content is negatively correlated with the grinding force and grinding temperature, while the grinding force and grinding temperature of the specimens increase and then decrease with the increase in the forming pressure. When we observe and measure the grinding surface of the specimen and workpiece, we see that the surface roughness of the specimen after grinding is smaller than that before grinding. In addition, the greater the porosity of the specimen, the rougher the surface of the workpiece after grinding.

Research and Optimization of Process Parameters for Internal Thread Forming Based on Numerical Simulation and Experimental Analysis.

In order to improve the forming quality of extruded thread, finite element analysis and experimental research are combined to reduce the two keys that affect thread quality in the machining process-extrusion torque and extrusion temperature. The effects of different processing parameters on the extrusion torque and temperature are obtained by numerical simulation, including the bottom hole diameter of the workpiece, the machine tool speed, and the lubrication medium. For the purpose of reducing extrusion torque and temperature, the process parameters for internal thread forming are further optimized by orthogonal design. It is determined that when machining the M22 × 2 internal thread on the connecting rod of the marine diesel engine made of 42CrMo4 steel, the bottom hole diameter of the workpiece should be 21.20 mm, the speed of the machine tool should be 40 RPM, and the lubricating medium should be PDMS polydimethylsiloxane coolant. Compared to before optimization, the maximum extrusion torque and the maximum extrusion temperature are reduced by 19.27% and 15.07%, respectively. On the premise of ensuring the thread connection strength, the height of the thread tooth is reduced by 0.052 mm, and the surface condition of the thread is improved. The surface microhardness at the root, top, and side of the thread increases by about 5 HV0.2, and the depth of the hardened layer increases by 0.05 mm. The results show that the quality of the optimized thread is higher.

Thriving artificial underwater drag-reduction materials inspired from aquatic animals: progresses and challenges.

In the past decades, drag-reduction surfaces have attracted more and more attention due to their potentiality and wide applications in various fields such as traffic, energy transportation, agriculture, textile industry, and military. However, there are still some drag-reduction materials that need to be deeply explored. Fortunately, natural creatures always have the best properties after long-term evolution; aquatic organisms have diversified surface microstructures and drag-reducing materials, which provide design templates for the development of thriving artificial underwater drag-reduction materials. Aquatic animals are tamed by the current while fighting against the water, and thus have excellent drag reduction that is unparalleled in water. Inspired by biological principles, using aquatic animals as a bionic object to develop and reduce frictional resistance in fluids has attracted more attention in the past few years. More and more aquatic animals bring new inspiration for drag-reduction surfaces and a tremendous amount of research effort has been put into the study of surface drag-reduction, with an aim to seek the surface structure with the best drag-reduction effect and explore the drag-reduction mechanism. This present paper reviews the research on drag-reduction surfaces inspired by aquatic animals, including sharks, dolphins, and other aquatic animals. Aquatic animals as bionic objects are described in detail, with a discussion on the drag-reduction mechanism and drag-reduction effect to understand the development of underwater drag-reduction fully. In bionic manufacturing, the effective combination of various preparation methods is summarized. Moreover, bionic surfaces are briefly explained in terms of traffic, energy sources, sports, and agriculture. In the end, both existing problems in bionic research and future research prospects are proposed. This paper may provide a better and more comprehensive understanding of the current research status of aquatic animals-inspired drag reduction.

Investigation of the Turbulent Boundary Layer Structure over a Sparsely Spaced Biomimetic Spine-Covered Protrusion Surface.

Multiperspective particle image velocimetry was used to investigate the turbulent boundary layer structure over biomimetic spine-covered protrusion (BSCP) samples inspired by dorsal skin of pufferfish. The comparison of BSCP samples of two sparse "k-type" arrangements (aligned and staggered) with roughness height k + = 5-7 (nearly hydraulically smooth) and smooth case were manufactured in bulk Reynolds number Re b = 37,091, 44,510. The negative value of the roughness function ΔU + shows a downward shift of the mean velocity profile of BSCP samples, which shows a drag reduction effect. The results of turbulent statistics present strong fluctuation over the aligned case in the streamwise direction, while little influence is observed in the wall-normal and spanwise direction, which promotes turbulence stability. The same phenomenon was found based on the probability density function of fluctuation velocity that the suppression of turbulent flow is better over the staggered case. It is obvious that the shear stress induced is governed by the streamwise fluctuations. Furthermore, the Q-criterion and the λci-criterion improved with vorticity ω were introduced for vortex identification, which indicates less prograde vortex population and weaker swirling strength over BSCP samples than over the smooth one. Finally, the spatial coherent structure appeared similar and more orderly over the staggered case in the streamwise and wall-normal direction based on the analysis of two-point correlations R uu. These results provide further guidance to reveal the mechanism of drag reduction on the BSCP surface.

Prediction of Welding Deformation and Residual Stress of a Thin Plate by Improved Support Vector Regression.

Thin plates are widely utilized in aircraft, shipbuilding, and automotive industries to meet the requirements of lightweight components. Especially in modern shipbuilding, the thin plate structures not only meet the economic requirements of shipbuilding but also meet the strength and rigidity requirements of the ship. However, a thin plate is less stable and prone to destabilizing deformation in the welding process, which seriously affects the accuracy and performance of the thin plate welding structure. Therefore, it is beneficial to predict welding deformation and residual stress before welding. In this paper, a thin plate welding deformation and residual stress prediction model based on particle swarm optimization (PSO) and grid search(GS) improved support vector regression (PSO-GS-SVR) is established. The welding speed, welding current, welding voltage, and plate thickness are taken as input parameters of the improved support vector regression model, while longitudinal and transverse deformation and residual stress are taken as corresponding outputs. To improve the prediction accuracy of the support vector regression model, particle swarm optimization and grid search are used to optimize the parameters. The results show that the improved support regression model can accurately evaluate the deformation and residual stress of butt welding and has important engineering guiding significance.

Numerical Analysis of Drag Reduction Characteristics of Biomimetic Puffer Skin: Effect of Spinal Height and Tilt Angle.

Based on the migratory phenomenon of the puffer and the cone-shaped structures on its skin, the effects of spinal height and tilt angle on the drag reduction characteristics is presented by numerical simulation in this paper. The results show that the trend of total drag reduction efficiency changes from slow growth to a remarkable decline, while the viscous drag reduction efficiency changes from an obvious increase to steady growth. The total and viscous drag reduction efficiencies are 19.5% and 31.8%, respectively. In addition, with the increase in tilt angle, the total drag reduction efficiency decreases gradually; the viscous drag reduction efficiency first increases and then decreases, finally tending to be stable; and the total and viscous drag reduction efficiency reaches 20.7% and 26.7%, respectively. The flow field results indicate that the pressure drag mainly originates at the front row of the spines and that the total pressure drag can be effectively controlled by reducing the former pressure drag. With the increase in low-speed fluid and the reduction in the near-wall fluid velocity gradient, the viscous drag can be weakened. Nevertheless, the drag reduction effect is achieved only when the decrement of viscous drag is greater than the increment of pressure drag. This work can serve as a theoretical basis for optimizing the structure and distribution parameters of spines on bionic non-smooth surfaces.

Reliability optimization of process parameters for marine diesel engine block hole system machining using improved PSO.

The processing quality of the block hole system affects the working performance of the marine diesel engine block directly. Choosing an appropriate combination of process parameters is a prerequisite to improving the accuracy of the block hole system. Uncertain fluctuations of process parameters during the machining process would affect the process reliability of the block hole system, resulting in an ultra-poor accuracy. For this reason, the RBF method is used to establish the relationship between the verticality of the cylinder hole and process parameters, including cutting speed, depth of cut, and feed rate. The minimum cylinder hole verticality is taken as the goal and the process reliability constraints of the cylinder hole are set based on Monte Carlo, a reliability optimization model of processing parameters for cylinder hole is established in this paper. Meanwhile, an improved particle swarm algorithm was designed to solve the model, and eventually, the global optimal combination of process parameters for the cylinder hole processing of the diesel engine block in the reliability stable region was obtained.

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample.

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

[GJB2 235delC single allelic mutation modulates the phenotype associated with the mitochondrial A1555G mutation].

OBJECTIVE: To investigate a non-syndromic deafness family in which potential interaction between the GJB2 gene and a mitochondrial gene appeared to be the cause of hearing impairment. METHODS: Audiological examination was performed by pure-tone audiometry (PTA). Blood samples from 8 members of the pedigree were obtained. DNA was extracted from the leukocytes. The coding region of the GJB2 gene and mitochondrial DNA target fragments were amplified by polymerase chain reaction (PCR). The PCR products were analyzed by sequencing. RESULTS: Direct sequencing showed that the proband had both a heterozygous mutation of 235delC in the GJB2 gene and a mitochondrial 1555 A to G mutation. The proband had profound hearing loss. The maternal relatives had sensorineural hearing loss in the higher frequencies or no hearing loss. CONCLUSION: The GJB2 mutations may be an aggravating factor in the phenotypic expression of the non-syndromic hearing loss associated with the A1555G mitochondrial mutation.

Research on the drag reduction property of puffer (Takifugu flavidus) spinal nonsmooth structure surface.

Puffers show good drag reduction performance during migration. It is worth noting that spines which are different from ordinary fish scales are densely distributed on the puffer skin. Here, the special morphological structure of puffer spines was observed using microscopy techniques, accurate contour models were established based on image processing techniques and curve fitting, then feature sizes were obtained. Based on the results, the nonsmooth surface was established by orthogonal test to simulate the flow field. In addition, the influence of spinal structure on boundary layer flow field and the drag reduction property of nonsmooth surface were further analyzed. The nonsmooth surface formed by spinal structure elements can effectively reduce the wall shear stress and Reynolds stress, and there was a special "climbing vortex" phenomenon, so as to reduce the surface viscous friction resistance and achieve drag reduction. Compared with the smooth surface, the drag reduction rate of the nonsmooth surface was 12.94% when the inflow velocity was 5 m/s, which revealed and verified the drag reduction performance of the spines of puffer skin. The results lay a foundation for further research and optimization of drag reduction ability of nonsmooth surface of bionic spines. HIGHLIGHTS: The contour of the spinous process was accurately reflected by the Fourier function. The spines of puffer skin have good drag reduction effect. There was a special "climbing vortex" phenomenon to explain the drag reduction property.

Quality Prediction and Control of Assembly and Welding Process for Ship Group Product Based on Digital Twin.

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

The Influence of Tool Rake Surface Geometry on the Hard Turning Process of AISI52100 Hardened Steel.

The hard turning process has been widely used in the field of hard material precision machining because of its high efficiency, low processing residual stress, and low environmental pollution. Due to its undesirably processing quality, it is still not a substitute for traditional grinding, so many studies have reported that the process has been optimized. However, there has been little research on the geometry optimization of hard cutting tools, which have a great influence on the traditional machining process. In this paper, two tools with different rake face shapes are designed. The finite element analysis method is used to compare their performance with a conventional plane tool while turning hardened steel. The results show that the cutting performance of the designed tool T1 and T2 (chip morphology, cutting force, and cutting temperature) and the quality of the machined surface are improved compared with the tool. The cutting force decreased by 12.72% and 14.74%, the cutting temperature decreased by 7.56% and 9.01%, respectively, and the surface residual stress decreased by 26.56% and 28.66%.

Related factors of outcomes of pharyngeal foreign bodies in children.

OBJECTIVE: This study aimed to identify factors related to outcomes of the pharyngeal foreign bodies in children and to improve the management protocol of this disease. METHODS: The medical records of 131 children with pharyngeal foreign bodies hospitalized in the hospital were retrospectively reviewed. RESULTS: Significant differences were observed between the two groups (dislodgement and removal group) with respect to location of pharyngeal foreign bodies and age, while sex, time of pharyngeal foreign bodies, and nature of pharyngeal foreign bodies had no significant differences. Moreover, results suggested that location of pharyngeal foreign bodies and nature of pharyngeal foreign bodies were risk factors correlated with complications. CONCLUSION: Pharyngeal foreign body in children has a high rate of dislodgement (>50%). Foreign bodies in the oropharynx were more likely to dislodge compared with the foreign bodies in the laryngopharynx. Younger children were more likely to dislodge compared with older children. Although the risk of complications was very low, attention needs to be paid to the potential risks: local infection, deep abscess, and migration of foreign bodies. Because the possibility of complications caused by bone fragments and foreign bodies in the laryngopharynx increase obviously, hence, it is suggested to remove these kinds of foreign bodies as soon as possible to prevent complications.