AMA, Agricultural Mechanization in Asia, Africa and Latin America
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.
WOS Indexed (2025)
27 Nov 2025 (Vol - 56 , Issue- 11 )
30 Nov 2025 (Vol - 56 , Issue 11 )
Aim and Scope :
AMA, Agricultural Mechanization in Asia, Africa and Latin America
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:
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering
Effect of Process Parameters on Ratio of Material Removal Rate to Electrode Wear Rate when PMEDM SKD11 Tool Steel
This paper introduces a study on the optimization of powder mixing discharge machining (PMEDM) of cylindrical parts made of SKD11 tool steel. In this study, many main input parameters, including the powder concentration, the powder size, the pulse on time, the pulse off time, the servo current, and the servo voltage were taken into account. Besides, the Taguchi method was used to design and analyze the experimental results. The influence of the input process parameters on the ratio of the material removal speed (MRS) to the electrode wear rate (EWR) was analyzed. Finally, the optimal input parameters to obtain the maximum ratio of MRS to EWR were proposed.
Three-objective Optimization of PMEDM Process for Machining SKD11 Tool Steel
This paper presents the results of a study on three-objective optimization when powder-mixing discharge machining (PMEDM) of SKD11 tool steel cylindrical parts. Three single-objective functions were selected for this problem. They are the minimum surface roughness (SR), the maximum material removal speed (MRS), and the minimum electrode wear rate (EWR). Besides, six main input parameters including the powder concentration, the powder size, the pulse on time, the pulse off time, the servo current, and the servo voltage were inves-tigated. In addition, the Taguchi method and Gray Relational Analysis (GRA) were used to design and analyze the experimental results. Finally, optimal input parameters to achieve the minimum SR, the maximum MRS, and the minimum EWR simultaneously have been suggested.
Multi-Objective Optimization of PMEDM Process for Minimum Surface Roughness and Minimum Electrode Wear Rate when Machining SKD11 Tool Steel
In this paper, a study on multi-objective optimization when powder-mixing discharge machining (PMEDM) of cylindrical parts made of SKD11 steel is conducted to achieve the minimum surface roughness (SR) and the speed minimum electrode wear rate (EWR). To solve this problem, an experiment was conducted with the use of the Taguchi method for design and the gray relational analysis (GRA) method to analyze the results. In addition, six input parameters including powder concentration, powder size, pulse generation time, pulse off time, servo current and voltage were taken to study their effect on SR and EWR. Moreover, optimal input parameters to achieve the minimum SR and minimum EWR simultaneously have been proposed.
Research and Test of Acoustic Signal Monitoring System of Fan Blade Based on BP Neural Network
Aiming at the difficult blade in daily maintenance of large-scale wind turbine, the sound signal of wind turbine blade can be detected by analyzing the non-contact fault of the blade. In this paper, a sound acquisition and transmission device is designed derived on stm32f103c8t6 single chip microcomputer. The device is arranged below the fan blade to collect the fan blade sound and transmit it to the PC through GPRS. The data is restored to the WAV format audio file on the PC. then LabVIEW software pre-processes the sound signal in the audio file and 1 / 6 octave processing to extract the acoustic characteristics of the blade, Finally, the BP neural network is trained and predicted by using the acoustic characteristics of fan blades in different states. The experimental results show that the training of BP neural network can be completed after 27 iterations, and the recognition rate of fault sound blades is more than 88%.
Genetic diversity analysis of recombinant inbred lines of bread wheat (Triticum aestivum L. Em. Thell) for stripe rust resistance using SSR markers
Molecular analysis revealed that out of 70 SSR primer pairs (including 40 Yr specific primers) used, 18 SSRs gave amplification. Seven Yr specific markers (Xgwm130 (Yr7), Xbarc 352 (Yr18), Xgwm 11 (Yr26), Xwmc 44 (Yr29), Xwmc 149 (Yr53), WKS1_I (Yr36) and Xcfb309 (Yr47) were polymorphic on RILs population. The amplified products varied from 120 bp to 350 bp. Cluster analysis at molecular level revealed that cluster I was the largest consisting of 39 RILs. This was followed by cluster X (33 RILs), cluster V (2 8 RILs), cluster VII (19 RILs), cluster IX (19 RILs), cluster VI (18 RILs) and cluster XI (18 RILs), cluster XII (15 RILs), cluster VIII (11 RILs), cluster III (7 RILs) and cluster XIII (5RILs). The scattered diagram on the molecular marker diversity analysis revealed that the fourteen RILs (50, 52, 56, 58, 59, 72, 97, 98, 118, 119, 120, 159, 184 and 193) were diverse. The grouping of recombinant inbred lines was not similar as in case of grouping based on morphological traits. Molecular Clustering as well as NTSYS-PC analysis clearly indicated that the recombinant inbred lines were scattered between the two parental wheat genotypes, WH711 and WH542, with an inclination towards WH542 (yellow rust resistant parent).