Integrating Taguchi Method and Gray Relational Analysis for Auto Locks by Using Multiobjective Design in Computer-Aided Engineering.

In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs-original cooling, U-shaped cooling, and conformal cooling-were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution.

Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning.

BACKGROUND: To diagnose key pathologies of age-related macular degeneration (AMD) and diabetic macular edema (DME) quickly and accurately, researchers attempted to develop effective artificial intelligence methods by using medical images. RESULTS: A convolutional neural network (CNN) with transfer learning capability is proposed and appropriate hyperparameters are selected for classifying optical coherence tomography (OCT) images of AMD and DME. To perform transfer learning, a pre-trained CNN model is used as the starting point for a new CNN model for solving related problems. The hyperparameters (parameters that have set values before the learning process begins) in this study were algorithm hyperparameters that affect learning speed and quality. During training, different CNN-based models require different algorithm hyperparameters (e.g., optimizer, learning rate, and mini-batch size). Experiments showed that, after transfer learning, the CNN models (8-layer Alexnet, 22-layer Googlenet, 16-layer VGG, 19-layer VGG, 18-layer Resnet, 50-layer Resnet, and a 101-layer Resnet) successfully classified OCT images of AMD and DME. CONCLUSIONS: The experimental results further showed that, after transfer learning, the VGG19, Resnet101, and Resnet50 models with appropriate algorithm hyperparameters had excellent capability and performance in classifying OCT images of AMD and DME.

Application of Intelligent Modeling Method to Optimize the Multiple Quality Characteristics of the Injection Molding Process of Automobile Lock Parts.

This study focuses on applying intelligent modeling methods to different injection molding process parameters, to analyze the influence of temperature distribution and warpage on the actual development of auto locks. It explores the auto locks using computer-aided engineering (CAE) simulation performance analysis and the optimization of process parameters by combining multiple quality characteristics (warpage and average temperature). In this experimental design, combinations were explored for each single objective optimization process parameter, using the Taguchi robust design process, with the L18 (21 × 37) orthogonal table. The control factors were injection time, material temperature, mold temperature, injection pressure, packing pressure, packing time, cooling liquid, and cooling temperature. The warpage and temperature distribution were analysed as performance indices. Then, signal-to-noise ratios (S/N ratios) were calculated. Gray correlation analysis, with normalization of the S/N ratio, was used to obtain the gray correlation coefficient, which was substituted into the fuzzy theory to obtain the multiple performance characteristic index. The maximum multiple performance characteristic index was used to find multiple quality characteristic-optimized process parameters. The optimal injection molding process parameters with single objective are a warpage of 0.783 mm and an average temperature of 235.23 °C. The optimal parameters with multi-objective are a warpage of 0.753 mm and an average temperature of 238.71 °C. The optimal parameters were then used to explore the different cooling designs (original cooling, square cooling, and conformal cooling), considering the effect of the plastics temperature distribution and warpage. The results showed that, based on the design of the different cooling systems, conformal cooling obtained an optimal warpage of 0.661 mm and a temperature of 237.62 °C. Furthermore, the conformal cooling system is smaller than the original cooling system; it reduces the warpage by 12.2%, and the average temperature by 0.46%.

Suspected malignant hyperthermia during sevoflurane anesthesia.

Malignant hyperthermia is a rare anesthetic-related disorder. We present a case with unusual presentation. A boy aged 3 years and 9 months who was scheduled for Hotz's operation presented normally before the operation. Anesthesia was induced by atropine, thiopental and sevoflurane. Trachea intubation was facilitated by succinylcholine. Jaw stiffness was first noted although trachea was intubated without difficulty. The following tachycardia, hypercapnia and hyperthermia led to the diagnosis of malignant hyperthermia. Symptoms were relieved dramatically after the discontinuation of sevoflurane. Molecular genetic testing identified a novel ryanodine receptor (RYR1) mutation in exon 39, which confirmed malignant hyperthermia susceptibility in this patient.

Clinical comparisons between GlideScope video laryngoscope and Trachlight in simulated cervical spine instability.

STUDY OBJECTIVE: To compare the time taken for tracheal intubation, hemodynamic changes, and perioperative morbidities between the GlideScope (GS) video laryngoscope and the Trachlight (TL) with manual inline stabilization. DESIGN: Prospective, controlled, randomized study. SETTING: Operating room. PATIENTS: 60 ASA physical status I and II patients scheduled for elective surgery with general anesthesia. INTERVENTIONS: Patients were randomly assigned to the GS group or TL group (n = 30 for each group). MEASUREMENTS: Noninvasive blood pressure and heart rate at preinduction; preintubation and one, three, and 5 minutes after successful intubation; grade of face mask ventilation; number of intubation attempts; intubation time; apnea duration; mucosal trauma; lip or dental injury; and presence of hypoxia, were all recorded. MAIN RESULTS: The intubation attempts and perioperative safety data were comparable between the two groups. Intubation time and apnea duration were significantly shorter in the TL group than the GS group. All variables one minute after intubation were greater than baseline values except systolic blood pressure (SBP) in TL group. Both systolic blood pressure (SBP) and the degree of change of SBP from the baseline value one minute after intubation in TL group were significantly less than those of the GS group. CONCLUSIONS: Trachlight offers a faster intubation and a milder hemodynamic response than GS.