다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

羟氨苄青霉素引起大疱性类天疱疮样疹

Additive manufacturing (AM), widely known as 3D printing, is a method of manufacturing that forms parts from powder, wire or sheets in a process that proceeds layer by layer. Many techniques (using many different names) have been developed to accomplish this via melting or solid-state joining. In this review, these techniques for producing metal parts are explored, with a focus on the science of metal AM: processing defects, heat transfer, solidification, solid-state precipitation, mechanical properties and post-processing metallurgy. The various metal AM techniques are compared, with analysis of the strengths and limitations of each. Only a few alloys have been developed for commercial production, but recent efforts are presented as a path for the ongoing development of new materials for AM processes.

The metallurgy and processing science of metal additive manufacturing

Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones

Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Laser direct joining of metal and plastic

In laser and electron-beam welding, a deep cavity called a keyhole or beam hole is formed in the weld pool due to the intense recoil pressure of evaporation. The formation of the keyhole leads to a deep penetration weld with a high aspect ratio and this is the most advantageous feature of welding by high-energy-density beams. However, a hole drilled in a liquid is primarily unstable by its nature and the instability of the keyhole also causes the formation of porosity or cavities in the weld metal. In particular, the porosity formation is one of the serious problems in very high-power laser welding, but its mechanism has not been well understood. The authors have conducted systematic studies on observation of keyhole as well as weld pool dynamics and their related phenomena to reveal the mechanism of porosity formation and its suppression methods. The article will describe the real-time observation of keyhole and plume behaviors in the pulsed and continuous-wave laser welding by high-speed optical and x-ray transmission methods, the cavity formation process and its suppression measures.

Dynamics of keyhole and molten pool in laser welding

Recent years have seen increasing attention paid to laser welding monitoring. This paper introduces an innovative approach to perform laser welding process monitoring and welded defect diagnosis. The laboratory-scale sensor can be replaced with industrial-scale sensors after the data-driven model has been established by applying multivariate statistics and machine learning methods. In addition, industrial-scale sensor makes effective diagnosis of welded defect by using pattern recognition. Experimental results show that the feature vector affecting estimation and classification accuracy can be obtained by using wavelet packet decomposition principal component analysis. Image processing technology was applied to quantify geometrical parameters of welding process. The feedforward neural network prediction model and the support vector machine classification model built in this research help to guarantee accurate estimation on welding status and effective identification of welded defect. The method proposed by this paper provides an innovative data-driven-based approach for laser welding process monitoring and defects diagnosis.

WPD-PCA-Based Laser Welding Process Monitoring and Defects Diagnosis by Using FNN and SVM

Elucidation of laser welding phenomena and factors affecting weld penetration and welding defects

This paper proposes a method of seam tracking monitoring during high-power fiber laser welding. A visual sensor system was employed to capture the infrared images of molten pools and the surroundings in the laser welding process. A weld seam position variable was extracted by the image difference and centroid algorithms. The state and measurement equations for weld seam position were established based on an eigenvector derived from the weld seam position variable. A Sage-Husa adaptive Kalman filter (AKF), as an estimator of the noise statistical characteristics, was applied in order to enhance the filtering precision. By embedding an Elman neural network into the AKF, an error estimator was used to compensate for the filtering errors. The results of the welding experiments have demonstrated the effectiveness of the proposed method to improve the accuracy of weld detection.

Seiji Katayama

Papers

Laser direct joining of metal and plastic

Dynamics of keyhole and molten pool in laser welding

WPD-PCA-Based Laser Welding Process Monitoring and Defects Diagnosis by Using FNN and SVM

Elucidation of laser welding phenomena and factors affecting weld penetration and welding defects

Seam Tracking Monitoring Based on Adaptive Kalman Filter Embedded Elman Neural Network During High-Power Fiber Laser Welding