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

Resource savings by sustainability assessment and energy modelling methods in mechanical machining process: A critical review

The machinability of nickel-based alloys is affected by the amount of friction and heat produced through machining. The usage of metalworking fluids raises concerns about the operator's health and environmental impact. Therefore, minimum quantity lubrication (MQL) is preferred as one of the cooling/lubrication alternatives to achieve enhanced machining performance. The effectiveness of MQL application can be enhanced by using vegetable oil blended with nanoparticles. Considering these facts, the machinability of Inconel 625 is assessed using varying concentrations (0.5%, 1%, 1.5%, and 2%) of graphite and molybdenum disulfide (MoS2) nanoparticles in sunflower oil. The tribological and thermophysical properties of prepared nanofluids in the form of friction coefficient, thermal conductivity, and dynamic viscosity are evaluated. The turning tests are conducted under dry, MQL, and nanofluid-MQL (nMQL) environments, and the findings are compared considering surface roughness, chip morphology, tool wear, cutting temperature, power consumption, and microhardness. The sunflower oil blended with MoS2 resulted in 56%, 42%, and 22% improved surface quality compared to dry, MQL, and nMQL (Graphite) conditions. Also, the efficacy of nMQL with graphite and MoS2 is seen in the form of slower tool wear progression. Also, MQL, nMQL with MoS2, and nMQL (Graphite) resulted in lower cutting temperatures by 18%, 35%, and 25%, respectively, compared to dry turning. The effective performance of nMQL is credited to the better penetration ability of the applied lubricant into machining interfaces. Furthermore, the MQL application with compressed air facilitated chip removal and heat dissipation during machining. Finally, the sustainability assessment through the Pugh matrix showcased the potential of nanofluid-MQL for machinability improvement of Inconel 625. 

Influence of MoS2 and graphite-reinforced nanofluid-MQL on surface roughness, tool wear, cutting temperature and microhardness in machining of Inconel 625

This study investigates the impact of production scheduling decisions aims at improving productive and energy-efficient performances simultaneously in distributed blocking flowshops (EDBFSP). To reach a compromise between the conflicting objectives, a Pareto multi-objective optimization model based on the estimation of distribution algorithm (MOEDA) is proposed. Firstly, an initialization method based on the problem-specific characteristics is designed to create a promising population with quality and diversity; secondly, a probabilistic model based on a Bayesian network is constructed to predict position relationships between jobs. Two neighborhood operators with modified insertion technique are proposed to realize the adjustments of both job sequence and processing speed; thirdly, two operators are developed to execute multi-objective local searches on the elite solutions. Aiming at efficient utilization of the resulted blocking and idle time, an energy-saving method is designed for EDBFSP. In the experimental parts, to gain the best performance, the key parameters of MOEDA have been calibrated. The validation is conducted to assess the performances of the designed initialization method, neighborhood search, local search, and energy-saving strategies. The proposed MOEDA is also compared with mainstream metaheuristics for solving green scheduling problems. The experiment results show that the optimization and search ability of MOEDA have gained prominent advantages over other metaheuristics in both precision and distributivity. 

Grzegorz Królczyk

Papers

Resource savings by sustainability assessment and energy modelling methods in mechanical machining process: A critical review

Influence of MoS2 and graphite-reinforced nanofluid-MQL on surface roughness, tool wear, cutting temperature and microhardness in machining of Inconel 625

Scheduling of energy-efficient distributed blocking flowshop using pareto-based estimation of distribution algorithm

Study on ploughing phenomena in tool flank face – workpiece interface including tool wear effect during ball-end milling

Sustainable Production: Novel Trends in Energy, Environment and Material Systems