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The requirement of “zero defects” is rapidly finding its way as a “standard” of quality in numerous quarters. This phrase has great psychological appeal, and is often taken literally at all levels in an organization even though quality motivation may be the intention. It is common to believe that when zero defects are found in the sample, this must be the case for “all the rest” as well. In this paper the technical side of “zero defects” is examined. We look at the statistics of zero defects and show what is implied about lot or process quality when zero defects is the actual sample outcome. The focus is on attribute measurements and includes some special cases where a significant measurement error exists and cases where a Bayesian statistical analysis may be appropriate.

In general for Vehicle-to-Vehicle (V2V) communication, message authentication is performed on every received wireless message by conducting verification for a valid signature, and only messages that have been successfully verified are processed further. In V2V safety communication, there are a large number of vehicles and each vehicle transmits safety messages frequently; therefore the number of received messages per second would be large. Thus authentication of each and every received message, for example based on the IEEE 1609.2 standard, is computationally very expensive and can only be carried out with expensive dedicated cryptographic hardware. An interesting observation is that most of these routine safety messages do not result in driver warnings or control actions since we expect that the safety system would be designed to provide warnings or control actions only when the threat of collision is high.

”U” bolts are fixing elements and they are used to clamp an elastic joint. From the past, they still looking as an old design and unfortunately, suspension engineers are not specialists in fasteners and elastic joints. That is why we will show important assumptions and concepts to design and specifications this clamp element “U” bolt and its influence over leaf-springs. Currently, “U” bolt is used to clamp an elastic or elastic-plastic joint of heavy duty suspension, formed by leaf-spring, axle, spring pad, “U” bolt plate. This kind of suspension is typically used to trucks, buses and trailers. We are wondering, which one important assumption that an engineer must be careful when designs a new suspension changing from old designs to an updated technology. We provide a theoretical analysis and a FEA analysis to compare torque efficacy x leaf-spring reactions and what are effects this relationship can cause in a suspension.

Oil pumpability in passenger car gasoline engines was well-characterized by an ASTM program and by individual researchers in the 1970's and early 1980's. Oil pumpability in diesel engines however, was not investigated to any significant extent until the mid-1980's. This study was initiated to define the performance of several commercial viscosity modifiers in different formulations containing 3 detergent-inhibitor (DI) additive packages and 4 basestock types. The test oils were run at -18°C (0°F) in a Cummins NTC-400 diesel engine. The results, when statistically analyzed, indicated that a new, second generation olefin copolymer (OCP) viscosity modifier had better performance than a first generation OCP and, furthermore, had performance equal to a polymethacrylate (PMA) viscosity modifier. The analysis also showed that one DI/base stock combination had a significant effect on performance.

The term triple safe is used to identify an air brake system with three separate circuits, two of which are service brake systems of virtually equal effectiveness; the third is a spring parking brake system. The paper reviews the spring parking brake chamber capability and describes a triple-safe air brake system and chambers that have similar service and emergency capacities, and parking capabilities, to meet future requirements of trucks and buses.

In the modern and fast growing automotive sector, reliability & durability are two terms of utmost importance along with weight & cost optimization. Therefore it is important to explore new technology which has less weight, low manufacturing cost and better strength. The new technology developed always seek for a quick, cost effective and reliable methodology for its design validation so that any modification can be made by identifying the failures. This paper presents the rig level test methodology to validate and to correlate the CAE derived strain levels, life cycle & failure mode of newly developed light weight stabilizer link for EV Bus suspension

Understanding customer expectations is critical to satisfying customers. Holding customer clinics is one approach to set winning targets for the engineering functional measures to drive customer satisfaction. In these clinics, customers are asked to operate and interact with vehicle systems or subsystems such as doors, lift gates, shifters, and seat adjusters, and then rate their experience. From this customer evaluation data, engineers can create customer loss or preference functions. These functions let engineers set appropriate targets by balancing risks and benefits. Statistical methods such as cumulative customer loss function are regularly applied for such analyses. In this paper, a new approach based on the Taguchi method is proposed and developed. It is referred to as Taguchi Customer Loss Function (TCLF).

IN this paper the authors present some experimental results obtained by using the analysis outlined by Prof. James J. Guest before the Institution of Automobile Engineers, in 1926. To make the experimental work more understandable, they present the essential points of Professor Guest's analysis. Professor Guest begins his analysis of the movements of a car body with the simplest set of conditions and presents a graphical as well as an algebraic solution. He then includes one additional factor after another in his analysis until the principal factors in car suspension are included. After all factors are considered, the essential structure of the simple analysis is retained. The authors' efforts at the experimental determination of the moment of inertia of passenger cars were started in January, 1932, on Sir Charles Dennistoun Burney's “tear-drop” design with which he visited leading American manufacturers.

The SHIFT-MATE is a dashboard mounted computer based device that cues a truck driver to shift more efficiently. Through electronic circuitry, key vehicle parameters are monitored, computed, then via graphic display, instructs the driver when to shift for improved fuel economy. The theory of operation is described in the text.

In automotive electronics on-board diagnostics does the fault diagnosis and reporting. It provides the level of robustness required for the control electronics against various faults. The amount of diagnostic information available via on board diagnostics are depends on the type of vehicle. Pre-supply fuel pump is the component in the common rail hydraulic system. It pumps the fuel from the fuel tank to the inlet valve of the high pressure fuel pump. Electronic control unit synchronizes its operation with high pressure fuel pump. A dedicated driver module in the ECU controls the operation of pre-supply fuel pump. The driver module consist of an ASIC with internal voltage, current monitoring modules for the fault diagnosis and the pre-drivers to control external HS and LS power stages. The software part of the OBD programmed in the internal memory of the ASIC. The “Rds_on” of the power MOSFETs are used for the fault detection purpose.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.

A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

This paper describes the application of statistical techniques known as Design of Experiments (D.O.E.) to efficiently use the results of numerical analysis data in order to improve the configuration of automotive systems. The general framework of these techniques is presented in a format aiming at the design engineer as their end user. Besides, a case study is presented with the purpose of illustrating their practical use. The first step of the case study is to build predictive models for the behaviour of the automotive system being developed by means of the Response Surface Method (RSM), using the proper D.O.E. options. Once these predictive models are available, automatic numerical optimization algorithms are used to improve the responses of interest for given operating conditions. Finally, the automotive systems are robust designed taking into account that the operating conditions vary randomly.

In recent years there has been increasing interest in quantifying the emissions from aircraft in order to generate inventories of emissions for climate models, technology and scenario studies, and inventories of emissions for airline fleets typically presented in environmental reports. The preferred method for calculating aircraft engine emissions of NOx, HC, and CO is the proprietary “P3T3” method. This method relies on proprietary airplane and engine performance models along with proprietary engine emissions characterizations. In response and in order to provide a transparent method for calculating aircraft engine emissions non proprietary fuel flow based methods 1,2,3 have been developed. This paper presents derivation, updates, and clarifications of the fuel flow method methodology known as “Fuel Flow Method 2”.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM).

The extensive use of rotative machines in the diverse branches of the modern world has made the rising undesirable mechanical and acoustic vibration levels to be a problem of special importance for the machines normal operation as for the communities that are each time more affected by the problem. It makes the study of vibration and acoustic phenomena also to be even more important and the applications of its concepts more sophisticated. Several are the concepts used for decreasing vibration levels, like common dampers, hydraulic dampers, active dampers, natural frequencies changes and others. The choice of use of one or another depends greatly on the engineering possibilities (weight, energy, physical space, other components functional interference, vibration levels, etc.) as well as the cost of implementation of each one.