Development of comparative investigation method for timing chain wear analysis using oscillating tribometer

This paper presents the development of a test procedure to investigate timing chain components under abrasive stress on a tribometer. Engine developers use cost and time expensive engine dynamometer tests to investigate timing chain life expectancy under different conditions. Tribometer tests are fast and cost effective, but these use standardized specimen material and geometry that greatly differ from the original tribological system of the timing chain. Manufacturing specimens from the original chain material using the original technology is complicated; surface quality and hardness properties cannot be guaranteed. The aim of research was to develop a test method for rapid and cost-effective comparison of engine lubricants, timing chain materials or coatings, as well as to assess the wear resistance of the chain to contaminants. Various uncontaminated and carbon black blended lubricants were compared using standard-based ball-on-disc tribometer tests to tribometer tests using actual timing chain components (bush-on-pin test) of a Diesel engine. Lubricants were ranked in terms of coefficient of friction and wear. Results showed that bush-on-pin tests were comparably suitable for testing lubricants when evaluated against standard ball-on-disc tribometer tests.

Selective trapping of hexagonally warped topological surface states in a triangular quantum corral

The surface of a three-dimensional topological insulator (TI) hosts two-dimensional massless Dirac fermions (DFs), the gapless and spin-helical nature of which leads to their high transmission through surface defects or potential barriers. Here, we report the behaviors of topological surface states (TSS) in a triangular quantum corral (TQC) which, unlike a circular corral, is supposed to be totally transparent for DFs. By real-space mapping of the electronic structure of TQCs, both the trapping and detrapping behaviors of the TSS are observed. The selection rules are found to be governed by the geometry and spin texture of the constant energy contour of TSS upon the strong hexagonal warping in Bi2Te3. Our work indicates the extended nature of TSS and elucidates the selection rules of the trapping of TSS in the presence of a complicated surface state structure, giving insights into the effective engineering of DFs in TIs.

A 3D Object Detection Based on Multi-Modality Sensors of USV

Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.

A Neural Network Based Process Planning System to Infer Tool Path Pattern for Complicated Surface Machining

Die and mold are necessary for the manufacture of present industrial products. In recent years, the requirement of high quality and low cost machining of complicated surfaces has increased. However, it is difficult to generalize process planning that depends on skillful experts and decreases the efficiency of preparation in die and mold machining. To overcome an issue that is difficult to generalize, it is well known that neural networks may have the ability to infer a valid value based on past case data. Therefore, this study aims at developing a neural network based process planning system to infer the required process parameters for complicated surface machining by using past machining information. The result of the conducted case studies demonstrates that the developed process planning system is helpful for determining the tool path pattern for complicated surface machining according to the implicit machining knowhow.

Superhydrophobic Water-Solid Contact Triboelectric Generator by Simple Spray-On Fabrication Method

Energy harvesting is a method of converting energy from ambient environment into useful electrical energy. Due to the increasing number of sensors and personal electronics, energy harvesting technologies from various sources are gaining attention. Among energy-harvesting technologies, triboelectric nanogenerator (TENG) was introduced as a device that can effectively generate electricity from mechanical motions by contact-electrification. Particularly, liquid-solid contact TENGs, which use the liquid itself as a triboelectric material, can overcome the inevitable friction wear between two solid materials. Using a commercial aerosol hydrophobic spray, liquid-solid contact TENGs, with a superhydrophobic surface (contact angle over 160°) can be easily fabricated with only a few coating processes. To optimize the fabrication process, the open-circuit voltage of sprayed superhydrophobic surfaces was measured depending on the number of coating processes. To demonstrate the simple fabrication and applicability of this technique on random 3D surfaces, a liquid-solid contact TENG was fabricated on the brim of a cap (its complicated surface structure is due to the knitted strings). This simple sprayed-on superhydrophobic surface can be a possible solution for liquid-solid contact TENGs to be mass produced and commercialized in the future.

Research on Trajectory Planning of 6-DOF Cutting-robot in Machining Complex Surface

It is important and difficult for the complicated surface processing in mechanical industry. In this paper, an improved algorithm for trajectory planning is proposed in impeller surface processing by using 6-DOF cutting-robot. Taking a single finished path of the impeller blade as an example, the feedrate of the cutter, bow height error, cutter-orientation and position are planned by the B-spline interpolation algorithm, the best cutting trajectory is obtained. On the basis of trajectory planning, the optimal movement scheme of 6-DOF cutting-robot joints is obtained, the 6-DOF cutting-robot feedrate and trajectory smooth transition is achieved and the joints movement adaptive adjustment is completed. Finally, the angles, the angular velocitys of the joints and their interrelated properties are analyzed. The research works indicate that the robot joint angle curves are continuous and stable, which has met the requirements of smooth movement of the robot, and the results show that the trajectory planning is effective and practical.