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

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

A comprehensive analysis of literature pertaining to surface texture metrology for metal additive manufacturing has been performed. This review paper structures the results of this analysis into sections that address specific areas of interest: industrial domain; additive manufacturing processes and materials; types of surface investigated; surface measurement technology and surface texture characterisation. Each section reports on how frequently specific techniques, processes or materials have been utilised and discusses how and why they are employed. Based on these results, possible optimisation of methods and reporting is suggested and the areas that may have significant potential for future research are highlighted.

Surface texture metrology for metal additive manufacturing: a review

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals

http://www.cell.com/article/S2405844022033412/pdf

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

Przedstawiono wybrane rezultaty dotyczące stereometrycznej charakterystyki stanu czynnych powierzchni elastycznych tarcz ściernych z ziarnami typu TrizactTM po procesie szlifowania stali NC6. Pomiary realizowano z wykorzystaniem zaawansowanej techniki mikroskopowej – mikroskopii różnicowania ogniskowego – oraz specjalistycznego oprogramowania komputerowego. SŁOWA KLUCZOWE: metody mikroskopowe, analiza obrazu, szlifowanie, elastyczne tarcze ścierne, stal NC6

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Stereometric characteristics of condition of active surface of the abrasive discs with Trizact™ grains after the grinding process of steel NC6 by the use of focus-variation microscopy

The paper presents a project of a prototype stand for making inseparable crimped joints through crimping operation, which is commonly used in the construction of heat exchangers. The joint is formed by tools in form of crimping jaws, which are considered as a punch stamp for pressing two materials into each other. In effect these two materials are mutually “crimped”. This elaboration contains a case study of joints being made by the workstand. Furthermore, the paper presents an analysis of features of the joints by examining them by destructive testing: Micrography, Tensile strength test, and non-destructive testing: Leakage test. The elements to be joined have been made of aluminium 6060, which is well matched to indirectly or directly make joints for all kinds of coolers or condensers.

/pdf/testing-of-crimp-connections-made-on-a-prototype-stand-jnf3iwf9nn.pdf

Testing of crimp connections made on a prototype stand

Machine vision based damage detection for conveyor belt safety using Fusion knowledge distillation

As professionals working in the field of conditionmonitoring and fault diagnosis, we know that reliable recognition of fault type and assessment of fault severity are essential for decision making in condition-based maintenance of rotating machinery. In engineering practice, the mechanical systems of rotating machinery are often subject to concurrent faults on the same component or different components, which make the examination of both the fault types and severities more challenging. Popular intelligent algorithms such as artificial neural networks (ANNs) are proven effective in identifying different fault patterns while “physical meanings” of the identification process are often missed due to blackbox of intelligent algorithms. Alternatives such asmultimodal decomposition approaches enable decoupling the hybrid faults into submodes. Each submode describes a single fault in the hybrid faults. As a result, the “physicalmeanings” of the identification process can be revealed using the multimodal decomposition approaches. This special issue looks at latest multimodal decomposition approaches for multifault detection, diagnosis, and prognosis on rotating machinery. The article by K. Chen et al. (Wuhan University of Technology, China) is a good place to begin this special issue as the authors introduced the variational mode decomposition (VMD) as the multimodal decomposition approach to detect multiple faults in rotor systems. The decomposed vibration signals usingVMDcan be used to extract effective features for multifault detection. The authors evaluated the performance of the proposed method using experimental data. In another article, G. An and H. Li from Mechanical Engineering College in China developed a multimodal decomposition approach based on fundamental component extraction (FCE) algorithm for multifault detection of rotor systems. The failures in stator and rotor can be effectively identified by the proposed FCE method. In another two articles, H. Li et al. (State Key Laboratory of Mechanical Transmission, China) presented an image tensor extraction method for rotor fault diagnosis and K. Chen et al. (Wuhan University of Technology, China) introduced an integrated approach of ensemble empirical mode decomposition and deep briefs network to diagnose gear multiple faults. The authors conducted experimental testing to evaluate the performance of the proposed approaches. Y. Li et al. is a good place to conclude this special issue as the authors proposed a new method based on variational mode decomposition and Gath-Geva clustering time series segmentation to extract the degradative feature of rolling element bearings and predict the bearing failures.The effectiveness of the proposed bearing degradation prediction method was verified by two case studies.

/pdf/multifault-detection-diagnosis-and-prognosis-for-rotating-2zi1vsh7k6.pdf

Grzegorz Królczyk

Papers

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

Stereometric characteristics of condition of active surface of the abrasive discs with Trizact™ grains after the grinding process of steel NC6 by the use of focus-variation microscopy

Testing of crimp connections made on a prototype stand

Machine vision based damage detection for conveyor belt safety using Fusion knowledge distillation

Multifault Detection, Diagnosis, and Prognosis for Rotating Machinery