A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

Clinical significance of contact lens related changes of ocular surface tissue observed on optical coherence images

PurposeTo investigate the relationship between the real contact lens imprint into the conjunctival tissue, observed by optical coherence tomography (OCT) and conjunctival staining and contact lens wearing comfort.Methods17 participants (mean age = 26.6 SD ± 3.6 years; 7 females) were fitted with three different contact lenses base curves of the same silicone hydrogel custom lens type (Visell 50; Hecht Contactlinsen, Au, Germany) in a randomised order. One lens was optimally fitted according to the manufacturer's recommendation, one fitted 0.4 mm flatter and one fitted 0.4 mm steeper. After 4 h of lens wear the contact lens edge in the area of the conjunctiva was imaged nasally and temporally using OCT (Optovue iVue SD-OCT). To correct the artefact due to optical distortion with OCT, the imprint of all worn lenses was measured on a glass plate afterwards. Conjunctival staining in the limbal region after 4 h of lens wear was classified using the CCLRU Grading Scale. Comfort scoring was based on visual analog scales from 0 (very poor) to 100 (excellent).ResultsThe mean conjunctival imprint of all contact lens edges was 32.0 ± 8.1 μm before and 7.3 ± 6.5 μm after distortion correction of the OCT images. The distortion corrected conjunctival imprint with the 0.4 mm steeper lens (11.5 ± 6.2 μm) was statistically significantly greater compared to the optimally fitted lens (6.5 ± 5.9 μm) (One-way ANOVA followed Tukey-test; p = 0.017) and greater compared to the 0.4 mm flatter lens (3.9 ± 5.3 μm) (p < 0.001). There was no statistically significant difference between the optimally fitted lens and the 0.4 mm flatter lens (p = 0.209). The nasally measured imprint (11.4 ± 9.0 μm) was significantly greater than the temporally measured (3.3 ± 7.6 μm) (p < 0.001). There was no statistically significant correlation between the amount of conjunctival imprint and the graded conjunctival staining (p = 0.346) or the wearer’s comfort (p = 0.735).ConclusionsContact lens edges imaged by OCT exhibited displacement artefacts. The observed conjunctival imprints are a combination of real conjunctival compression and artefacts. A deeper imprint of the contact lens into the conjunctiva caused by a steeper base curve was not related to clinically significant staining or changes in comfort after 4 h of lens wear. The observed differences between nasal and temporal imprint are likely to be caused by variations of conjunctival thickness and the shape of the underlying sclera.     

Compression properties of gas diffusion layers and its constitutive model under cyclic loading

The compressive deformation of gas diffusion layer (GDL), which is highly nonlinear and related to the loading history, affects the performance of PEM fuel cell stacks. However, linear elastic models are widely used. In this study, a new nonlinear constitutive model is proposed to describe the compression properties. Macroscopic studies reveal that GDL has different mechanical properties during the first and repeated compression stages. Besides, the tangent modulus has a significant linear relationship with stress. The constitutive model can be rebuilt using the micro-mechanical theory of fiber assemblies by considering the bending of carbon fibers. Furthermore, a prediction method is proposed to describe cyclic compression behavior. The prediction results fit well with the test results with an average and maximum relative error of less than 5.30% and 18.13%, respectively. These conclusions are beneficial to the design of GDL with specific mechanical properties and the real-time analysis of PEM fuel cell.     

Novel cheese with vegetal rennet and microbial transglutaminase: Effect of storage on consumer acceptability,sensory and instrumental properties

The aim of this study was to evaluate the consumer acceptance and preference of a novel cheese coagulated with vegetal rennet and microbial transglutaminase (V + TG) as an alternative for lacto‐vegetarian consumers. The evolution of the sensory and instrumental parameters throughout the storage time was studied. Cheese with animal rennnet and microbial transglutaminase (A + TG), vegetal rennet alone (V) and animal rennet alone (A) were also studied as controls. The consumer test showed that the V + TG cheese was preferred and showed high texture and taste acceptance scores. This cheese could be successful for its sensory acceptance and as an alternative for the lacto‐vegetarian market.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

圆形截面柱的振动台试验及分析

进行了圆柱振动台试验,对模型的加速度反应数据进行获取。试验表明,随着地震的加速度峰值的不断增加,圆柱顶部加速度极值不断增加。柱顶的加速度响应有着增大的趋势,且极值比输入的加速度的极值有一定的放大。     

Effect of obstacle density on the travel time of the visually impaired people

Because China has put more emphasis on people with disabilities, their living conditions and protection have received increasing attention. In an emergency, the visually impaired people are limited in their ability to evacuate, especially along egress paths with obstacles. This paper studied the relationship between the travel time of visually impaired people and obstacle density in their path. Eight sighted people and 32 people with visual impairments were chosen to carry out the walking experiment. The results of the experiment showed that the travel time of sighted people was shorter than that of visually impaired people under the same obstacle and non‐obstacle situations. Based on the analysis of variance of travel time, it was clear from the results that the travel time of the visually impaired people rapidly increased when there were obstacles. When the obstacle density was kept to within a certain range, there was no obvious increase in travel time. Once the density exceeded this range, there was another obvious increase. Based on this result, it can be seen that keeping the obstacle density to within a certain range could be helpful for the visually impaired people during an evacuation when the egress paths cannot be made obstacle‐free.     

ChronoCorrelator: Enriching Events with Time Series

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.