The demand for high temperature-resistant superalloys such as Inconel 718 is increasing rapidly, as they possess superior mechanical, chemical, and physical properties. Hence, these materials are h...

Machinability of Inconel 718: A critical review on the impact of cutting temperatures

State of the art of tool texturing in machining

Higher cutting zone temperatures are responsible for poor turning process performance during dry machining of Inconel 718 material. In the present work, a novel hybrid surface texture tool under mi...

Effect of novel hybrid texture tool on turning process performance in MQL machining of Inconel 718 superalloy

The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.

/pdf/eeg-based-detection-of-braking-intention-under-different-car-4j8hah92o7.pdf

EEG-Based Detection of Braking Intention Under Different Car Driving Conditions

As widely reported in the literature, the micro-texturing technique has a significant potential to improve the performance of cutting tools by reducing cutting forces, friction, temperature, and tool wear, and further, by improving tribological behavior at the tool-chip and tool-workpiece interface. However, it is also known that the effects of textures on the cutting tool surface strongly depend on the texture geometry and dimensions, and that the application of indiscriminate surface textures to cutting tools often results in adverse effects on cutting performance, indicating that an improved understanding of surface phenomena is required to further develop textured cutting tools. This paper summarizes recent advances in tool surface texturing in metal cutting fields, and continues to describe how the textured surface of a cutting tool influences the deformation fields of the workpiece material, including both primary and secondary shear zones, by means of direct in-situ observation using particle image velocimetry analysis to understand the behavior in the vicinity of the textured surfaces during the cutting process. The experimental results with the textured cutting tool reveal that the effect of the grooved rake face on the cutting force differs depending on the relative position of the microgroove with respect to the undeformed chip thickness. The friction condition and where the texture is located determine the performance of the surface texture. The link between the interface friction and chip flow mode is discussed, and it is demonstrated that surface textures can alter the chip flow mode from a segmented flow with redundant deformation to a steadier homogeneous flow with lower cutting energy.

Direct observations of tribological behavior in cutting with textured cutting tools

Effects of interface layout on the usability of In-Vehicle Information Systems and driving safety

Tool-chip adhesion impacts on cutting performance significantly, especially in finish cutting process. To promote cutting tools’ anti-adhesion property, the concave micro-grooves texture (MGT) and convex volcano-like texture (VLT) were fabricated separately on lathe tools’ rake faces by laser surface texturing (LST). Various orientations of MGT and different area densities (9% and 48%) and regions (partial and full) of VLT were considered in textured patterns designing. The following orthogonal cutting experiments, machining of aluminum alloy 5038, analyzed tools’ performances including cutting force, cutting stability, chip shape, rake face adhesion and abrasion. It indicated that under dry finish cutting conditions, MGT contributed to cutting stability and low cutting forces, meanwhile friction and normal force reduced by around 15% and 10%, respectively with a weak correlation to the grooves’ orientation. High density VLT tools, on the other hand, presented an obvious anti-adhesion property. A 5 μm reduction of crater wear’s depth can be observed on textured rake faces after long length cutting and textured rake faces presented half size of BUE regions comparing to the flat tool, however, once the texture morphologies were filled or worn, the anti-adhesion effect could be invalid. The bearing ratio curve was employed to analysis tool-chip contact and durability of textured surfaces contributing to a better understanding of anti-adhesion and enhanced durability of the textured tools.

Experimental Investigation of Concave and Convex Micro-Textures for Improving Anti-Adhesion Property of Cutting Tool in Dry Finish Cutting

Physiological signal analysis for fatigue level of experienced and inexperienced drivers.

With the development of society, the smart phone for the elderly has been developed rapidly. To improve the design of the smart phone icons for the elderly, a set of experiments were made to study the usability differences between multi-colored icons and monochromatic icons. 8 pairs of icons where a pair of icons was composed of a multi-colored icon and a monochromatic icon with the same function were prepared. These 16 icons whose height are 9.81 mm were placed in random order in the Xiaomi MI 4 smart phone installed with the android system. 24 retired teachers aged from 60–70 of the university were called as participants. They were asked to find the right icon as required and click on it with one of their fingers. For every subjects, there were 6 finding tasks followed by satisfaction questionnaire surveys. Eye movement data and satisfaction questionnaire surveys data were acquired and analyzed to get the results: monochromatic flat icons were easier to recognize and operate than the ones that designed with multi-colors for old people, even though they may have monotonous forms; age 60–65 or 66–70 was not the reason one made an operation mistake; some older people liked multi-colored flat icons more than monochromatic flat icons for their colorful vision experience.

Color Affects the Usability of Smart Phone Icon for the Elderly

The effects of digital car dashboard interface factors on driving experience were studied. Specifically, representative car dashboard interface design elements were analyzed and five representative samples were selected. Four aspects were explored, including dashboard design style, interface layout, information framework, and hierarchical table. The color proportions and shape division of dashboards were analyzed both longitudinally and transversally. By studying and analyzing the five samples, we find to design digital car dashboards, designers have to obey the rules of simple layout, color precision and experience richness. This study has high practical significance and values.

Interactive Design of Digital Car Dashboard Interfaces

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