ORIGINAL RESEARCH ARTICLE | Aug. 30, 2020
Properties Evaluation of Injection Moulded Gas and Water Atomised 316L Stainless Steel Powder
Mohd Afian Omar, Istikamah Subuki
Page no 310-315
This paper investigate the characteristics important to injection moulding via rheological behaviour, injection moulding, debinding and sintering process of water and gas atomised 316L stainless steel powder using new locally based binder system; palm stearin. The critical powder loading for injection moulding were 65vol% and 62vol% for gas and water atomised respectively. The gas atomised powder proves easier to mold because of a low interparticle friction and high packing density. In contrast, the water atomised powder has high viscosity of the injection moulding feedstock, high interparticle friction and a low packing density that interfere with injection moulding. Binder debinding was performed using solvent and thermal method. After debinding the samples were sintered at 1360oC using a high temperature vacuum furnace. Results indicate that water atomised powder could be sintered to 95% of theoretical density, while gas atomised powders could be sintered to near full density.
REVIEW ARTICLE | Aug. 29, 2020
Study of Device State Recognition Algorithm Based on Improved YOLOv3
Xiansong Bao, Gu Hao, Zhang Fan
Page no 300-303
In view of the timeliness and accuracy of traditional state recognition algorithms, this paper proposes an improvement measure for foreground segmentation and target recognition. Foreground segmentation is to model the background information in the scene before recognizing the image, to separate the foreground target from the scene, at the same time to reduce the impact of noise, shadow and other environmental changes as much as possible, and then segment the target through a sliding window strategy. Target recognition is improved with reference to the PRN network and anchorboxes mechanism, and a more advanced clustering k-means++ algorithm is applied. The method has low error rate, high signal-to-noise ratio and fast processing speed. Finally, the proposed improved algorithm is applied to device status recognition, which shows the advanced nature of the algorithm.
ORIGINAL RESEARCH ARTICLE | Aug. 29, 2020
Study on the Physico-Mechanical Properties of Okra Fibre at Different Harvesting Time
Md. Anisur Rahman Dayan, Md. Mahmudul Habib, Mohammad Abdullah Kaysar, Md. Moslem Uddin
Page no 304-309
Natural fibers obtained from plants or animals. Okra fiber is one of the source of natural fibres, which comes from okra plant (Abelmoschus esculentus). Okra fiber is eco-friendly, biodegradable, available, and cost effective materials. The properties of okra fiber are changed with different harvesting time. This work studied to determine the physico-mechanical and thermal properties using Stelometer, Fibre fineness analysis system, Photovolt meter, Fourier Transform Infrared Spectroscopy (FTIR), and Thermogravimetric analysis (TGA) at different age of okra fibres. The results showed that strength (gm/tex) 30.78, fineness (μm) 40.50, whiteness or color (%) 48.40 of four-month okra fiber and the six-month okra fiber strength (gm/tex) 31.98, fineness (μm) 56.12, whiteness or color (%) 43.56. FTIR assessment of the okra fiber reveals the presence of functional group. Thermally stability of six-month sample is good compare to the four-month okra fiber. The okra bast fibre is an important unconventional source of fibres, which could be, characterized for their use in blending yarn, reinforcing materials and diversified products.