Weight Loss By Soil Burial Degradation Of Green Natural Rubber Vulcanizates Modified By Tapioca Starch

The weight loss of soil degraded natural rubber vulcanizates modified by tapioca starch was investigated. The samples were prepared by melt compounding using a Haake internal mixer at different tapioca starch loading of 0, 5, 10, 20, 40, and 60 phr. The samples were exposed to soil burial testing for duration of 7, 14, 21 and 28 days. Then, the weight loss was measured using the difference in weight before and after the testing. The mass reduction was observed to be proportionately increased with the increment of tapioca starch loadings and prolonged soil burial duration. The rate of degradations observed was supported with morphological characteristics of the vulcanizates. This study is highly significant towards the development of green natural rubber composites by incorporation of tapioca starch

Process Optimization of EDM Cutting Process on Tool Steel using Zinc Coated Electrode

In WEDM machining process, surface finish quality depends on intensity and duration of spark plasma. Electrode wire diameter has significant effect on the spark intensity and yet the studies on this matter still less. Therefore, the main objectives of this studies are to compare the different diameters of zinc coated and uncoated brass electrode on H13 tool steel surface roughness. The experiments were conducted on Sodick VZ300L WEDM and work piece material of tool steel AISI H13 block. Electrode of zinc coated brass with diameters of 0.1 mm, 0.2 mm, 0.25 mm and uncoated brass 0.2 mm were used. The surface roughness of cutting was measured using the SUR-FTEST SJ-410 Mitutoyo, surface roughness tester. The results suggest that better surface roughness quality can be achieved through smaller electrode wire diameter. The zinc coated improves flushing ability and sparks intensity resulting in better surface finish of H13 tool steel. New alloys and coating materials shall be experimented to optimized the process further

Deep learning-based single-shot and real-time vehicle detection and ego-lane estimation

Vision-based Forward Collision Warning System (FCWS) is a promising assist feature in a car to alleviate road accidents and make roads safer. In practice, it is exceptionally hard to accurately and efficiently develop an algorithm for FCWS application due to the complexity of steps involved in FCWS. For FCWS application, multiple steps are involved namely vehicle detection, target vehicle verification and time-to-collision (TTC). These involve an elaborated FCWS pipeline using classical computer vision methods which limits the robustness of the overall system and limits the scalability of the algorithm. Deep neural network (DNN) has shown unprecedented performance for the task of vision-based object detection which opens the possibility to be explored as an effective perceptive tool for automotive application. In this paper, a DNN based single-shot vehicle detection and ego-lane estimation architecture is presented. This architecture allows simultaneous detection of vehicles and estimation of ego-lanes in a single-shot. SSD-MobileNetv2 architecture was used as a backbone network to achieve this. Traffic ego-lanes in this paper were defined as semantic regression points. We collected and labelled 59,068 images of ego-lane datasets and trained the feature extractor architecture MobileNetv2 to estimate where the ego-lanes are in an image. Once the feature extractor is trained for ego-lane estimation the meta-architecture single-shot detector (SSD) was then trained to detect vehicles. Our experimental results show that this method achieves real-time performance with test results of 88% total precision on the CULane dataset and 91% on our dataset for ego-lane estimation. Moreover, we achieve a 63.7% mAP for vehicle detection on our dataset. The proposed architecture shows that an elaborate pipeline of multiple steps to develop an algorithm for the FCWS application is eliminated. The proposed method achieves real-time at 60 fps performance on standard PC running on Nvidia GTX1080 proving its potential to run on an embedded device for FCWS