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.
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:Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science
This study aimed to determine some physical and mechanical properties of the common varieties of pineapple leaves in Malaysia such as MD2 (V1), Josapine (V2) and Nanas Madu Kaca A11 (V3). These properties are important to help designers to further apply in chopping/cutting or threshing machine. The physical properties for each variety were measured for 10 different leaves which were collected from different plants. The results indicated that the average length and width of MD2, Josapine and Nanas Madu Kaca were within similar range of 818- 686 mm and 56-58 mm, respectively. The average moisture content was measured for the varieties prior measuring the mechanical properties. The tensile strength for V1, V2 and V3 at different moisture content of 86%, 76% and 83% was observed to be 12.23, 9.16 and 6.45 MPa, respectively. Tensile strength for pineapple leaves is generally decreased with increasing moisture content. For penetration test, the point of hardness (in mm) was measured at the top, middle and bottom leaves. For V1, V2 and V3 the hardness penetration was 2161,2803,2004 mm; 2814, 2364, 3057 mm; 3924, 6630, 3061 mm for each top, middle and bottom, respectively. Whereas for the compression test, the hardness penetration for each V1, V2 and V3 were 1450,1142,14753 mm; 6589,896 and 1734 mm; 16009, 880, 13570 mm for top, middle, bottom, respectively. With respect to the findings of the present research study, it is concluded that different varieties of pineapple leaves exhibit different mechanical properties. Further investigation is required prior application for the cutting ability of the blade in the cutting and chopping machine.
The aim of current work is to investigate the physical and mechanical properties of sterilized oil palm fruit (Dura variety) and its component (kernel and nut) prior to mesocarp and nut separator machine development. The fresh fruit bunch (FFB) samples were sterilized at 100 °C for 140 minutes at 1 atm by using a continuous sterilizer and oil palm fruits were stripped from the bunch manually. Physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), gravimetric properties, and frictional properties were measured. Standard methods were applied in determining the physical properties of the sample. The mechanical properties evaluated were rupture force, deformation at rupture point, hardness and energy. The mechanical properties parameters were determined using Universal Testing Machine. The overall findings show that the average length, width, and thickness of the palm fruitlet were the highest compared to nut and kernel which were 37.28, 25.06 and 21.64 mm, respectively. The shape of the kernel is closer to a sphere compared to the whole fruit and nut as the sphericity value obtained was 79.25. True densities of whole fruits, nuts and kernels were 1079.77, 1140.86 and 1284.76 kg/m3, bulk densities were 767.37, 536.77 and 515.72 kg/m3 whereas the porosity were 20.27, 51.29 and 52.39, respectively. Rupture force, deformation and hardness of sterilized whole fruit, nut and kernel were 878.23, 712.51 and 339.07 N, and 4.86, 3.70 and 2.40 mm, 180.71, 192.57 and 141.27 N.mm, respectively. Overall, the collected data are beneficial for the design of machinery related to oil palm fruit processing such as mesocarp and nut separator machine.
The convolutional neural network (CNN) has won unprecedented triumph in super-resolution (SR) reconstruction via making full use of the advantages of fast parallel computing, end-to-end high training accuracy. However, the model CNN based can only extract fixed-size local feature information, and has limited ability to obtain global information. Moreover, scale fusion mechanism was rarely employed in model training in the past, and diverse degradation models with different scales need to be trained and tested separately. We put forward a new self-attention based multi-scale learning network (SAMSSR), which can address the above problems. On the one hand, extract the global similarity in degraded images in virtue of self-attention mechanism, and the similarity is acted as the feature weights to redistribute the feature channel information, so as to enhance the important information and suppress the non- important information, finally improve the reconstruction quality and simplify the training process. On the other hand, taking advantage of intrinsic similarity and correlation of SR results with different scales reconstructed by model and deeply merging them, we ultimately achieved multi-scale SR outputs, consequently realized a single end-to-end multi-scale SR reconstruction. Extensive experiments demonstrated that SAMSSR obtained competitive promotion in accuracy and visual perception compared with state- of-the-art methods.
According to the consideration of safe use, the design of tractor driving axle housing is too conservative, with its strength failing to demonstrate. Aiming at the lightweight design of the axle housing, this paper proposes a design method to ensure the reliability and robustness of the axle housing on the basis of realizing the lightweight. Firstly, based on the analysis of the natural frequency of the axle housing, the finite element model is verified, and the resonance phenomenon is analyzed. Secondly, the optimal area of axle housing is determined by the results of variable density topology optimization and the fatigue accumulation damage theory. In the optimized area, the shell thickness variables are selected by sensitivity analysis. Thirdly, based on the radial basis function (RBF) approximate model, a six-sigma robust optimization algorithm is adopted to optimize the axle housing with mass, equivalent stress, displacement and safety factor as the objective function. The simulation results show that the design variables, strength, stiffness and safety factor of the optimized drive axle housing reach 8σ level, and the mass of the axle housing decreases by 9.7%. Moreover, compared with the deterministic optimization design based on genetic algorithm, the safety factor, strength and stiffness of the bridge housing through the proposed method can be improved relatively while achieving the goal of lightweight, and meanwhile it provides a technical reference for the structural lightweight design of vehicles and other machineries.
The grades of oil palm fresh fruit bunches (FFB) influences both processing and market values as this oil palm sector is highly contributed to Malaysia’s Gross Domestic Product (GDP). Traditional techniques used during the screening process must be enhanced in order to minimize the quantity of low-quality oil palm that is delivered to the mills for further processing. This study presents an innovative method to grading the oil palm, based on a fruit-based maturity sensor with charging technique. The sensor's sensitivity is evaluated by examining the relationship between the sensor voltage and the moisture content of the oil palm fruit, which is done with the use of several set of electrodes in this research. When a four-set of electrodes in fruit battery was utilized instead of a single set of electrodes, the sensitivity of the sensor improved by triple times for the range of 50 to 80 per cent moisture content, compared to the single-set of electrodes. The accuracy of fruit maturity grade is the common difficulty encountered throughout the receiving process, was substantially enhanced as a result of the suggested method.