AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering. Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Dressing process has significant effects on the grinding operations when considering the profile accuracy, the topography, and the grinding wheel wear. This study aims to optimize the internal grinding process of hardened SKD11 steel to find the optimum set of dressing parameters that can maximize material removal rate. The input parameters used are Coarse dressing depth, Number of coarse dressing, Fine dressing depth, Number of Fine dressing, Non-feeding dressing, and Dressing feed speed. Taguchi method is utilized to plan experiments for validating predictions. It is found that the number of coarse dressing has the most influential value of 65.83% on the MRR. Moreover, the consistency between the predictions and experiments is confirmed by the Anderson- Darling checking method. It can be drawn that the proposed method can be further applied in other studies.
The aim of this study is to obtain the optimum set of dressing parameters which can generate minimum surface roughness when carrying grinding process of SKD11 hardened steel by CBN grinding wheel. The selected input parameters are dressing depth, dressing feed rate and dressing spindle. The optimization process is validated by experiments planned to be tested by using Taguchi method. It is found that the dressing depth has the largest effect on the surface roughness, and the influential percentage is 74.37%. Followed are the influences of the dressing feed rate and the dressing spindle speed with 13.07% and 6.28%, respectively. Additionally, it is revealed that the differences between the experiments and the predictions are minor when showing the deviation of 8.06%. The experimental results showed that it is possible to employ Taguchi method to study experiments and predictions in case of grinding SKD11 hardened steel by CBN grinding wheel.
In the grinding process, the dressing plays a vital role in ensuring the topography and performance of the grinding wheel. This study examines the effect of some critical parameters of dressing on material removal rate in grinding SKD 11 alloy steel using CBN wheels. The Taguchi method is used to organize experiments as well as provide statistical analysis. The dressing parameters chosen are the dressing depth, the dressing spindle speed, and the dressing feed rate. The CBN grinding wheel was dressed in a condition with three levels of the dressing depth (from 0.01 to 0.03 mm), three levels of the dressing spindle speed (from 500 to 2000 rpm), and three levels of the dressing feed rate (from 100 to 800 mm/min). The optimum conditions of the dressing have been achieved for the maximum material removal rates. Along with that, an analysis of variance (ANOVA) was performed to assess the appropriateness of the empirical model. The results show that the dressing spindle speed is the parameter that has the greatest influence on MRR, followed by the dressing depth.
This research is to determine the influence of the process parameters when machining SKD11 steel and proposes the optimized technical conditions to minimum surface roughness (Ra) in WEDM (Wire Electrical Discharge Machining) process. Process parameters such as cutting voltage (VM), pulse on time (Ton), pulse off time (Toff), servor voltage (SV), wire feed (WF) and cutting speed (SPD) were investigated as input variables. To find out the optimized paramaters, Taguchi method was used to design experimental plans. With this method, 18 experimental runs were conducted with six above process parameters. The experimental results show that surface roughness (Ra) is minimum with optimized parameter set as follows: SPD = 4 m/min, Ton = 6 s; Toff = 16 s; SV = 34 V; WF = 10 mm/min and VM = 6 V. The deviation value between calculation and experiment is 9.29 %.
Leaf mold is a common disease on tomato leaves, which seriously affects the quality and yield of tomatoes. In order to use hyperspectral technology to achieve early detection of leaf mold, 200 samples were first collected and the hyperspectral data of all leaf samples in the band of 927 to 1684 nm were obtained. According to the lesion area, all leaf samples were divided into 4 grades, and then the four pretreatment effects were compared, and the Savitzky-Golay convolution smoothing method was selected as the pretreatment method. Competitive adaptive reweighted sampling (CARS), iteratively retains informative variables (IRIV) and a combination of the two algorithms are used to select feature variables to establish tomato leaf mold hyperspectral support vector machine (SVM), CARS-SVM, IRIV-SVM, CARS-IRIV-SVM detection model. The results show that the detection accuracy of the four models for level 1 samples are all higher than 80.9%, and the recognition effect is good. The overall prediction accuracy rates of the four models are 79.41%, 86.76%, 85.29% and 92.65%. The CARS-IRIV-SVM model has the best results in identifying the characteristics of tomato leaf mold. The evaluation index of the model is Rp2=0.9103, RMSEP=0.138 and RPD=2.51, the prediction accuracy and overall accuracy of each level of the prediction set are 100%, 95.24%, 88.88%, 90.91% and 92.65%. The built model has high detection accuracy, indicating that the CARS-IRIV-SVM model based on hyperspectral technology is feasible for the classification and recognition of tomato leaf mold.