Motorteknik og erhverv

This book provides readers with a hands-on guide to system architecture design and platform development strategies for electronic systems. While system architecture design is a sub topic of systems engineering, the author discusses platform development as a matter of strategy, engineering efficiency and quality. This book helps readers by combining system architecture and platform development in a defined way, discussing processes and consequences in the context of the newest developments in technology, processes and management, such as artificial intelligence, agile development, and organizational change.Provides first book to offer a combined perspective on system architecture design and platform development strategy;Offers a systematic, state-of-the-art description of all aspects of system architecture and platform development;Presents system architecture and platform development in the modern context of agile development and artificial intelligence, as it relates to product development in the electronics industry.

This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map-in addition to training better recognition, tracking, and decision models.Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled "e;Teaching and Learning from this Book"e; was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects.This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies.

The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies.In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

X-by-wire Unmanned Ground Vehicles (UGVs) have been attracting increased attention for various civilian or military applications. The x-by-wire techniques (drive-by-wire, steer-by-wire, and brake-by-wire techniques) provide the possibility of achieving novel vehicle design and advanced dynamics control, which can significantly improve the overall performance, maneuverability, and mobility of the UGVs. However, there are few full x-by-wire UGVs prototype models reported in the world. Therefore, there is no book that can fully describe the design, configuration, and dynamics control approach of full x-by-wire UGVs, which makes it difficult for readers to study this hot and interesting topic.In this book, we use a full x-by-wire UGV, developed by our group, as the example. This UGV is completely x-by-wire with four in-wheel motors driven and a four-wheel independent steer steer. In this book, the overall design of the UGV, the design of the key subsystems (battery pack system, in-wheel motor-driven system, independent steer system, remote and autonomous control system), and the dynamics control approach will be introduced in detail, and the experiment's results will be provided to validate the proposed dynamics control approach.

Automobiles have played an important role in the shaping of the human civilization for over a century and continue to play a crucial role today. The design, construction, and performance of automobiles have evolved over the years. For many years, there has been a strong shift toward electrification of automobiles. It started with the by-wire systems where more efficient electro-mechanical subsystems started replacing purely mechanical devices, e.g., anti-lock brakes, drive-by-wire, and cruise control. Over the last decade, driven by a strong push for fuel efficiency, pollution reduction, and environmental stewardship, electric and hybrid electric vehicles have become quite popular. In fact, almost all the automobile manufacturers have adopted strategies and launched vehicle models that are electric and/or hybrid. With this shift in technology, employers have growing needs for new talent in areas such as energy storage and battery technology, power electronics, electric motor drives, embedded control systems, and integration of multi-disciplinary systems. To support these needs, universities are adjusting their programs to train students in these new areas of expertise. For electric and hybrid technology to deliver superior performance and efficiency, all sub-systems have to work seamlessly and in unison every time and all the time. To ensure this level of precision and reliability, modeling and simulation play crucial roles during the design and development cycle of electric and hybrid vehicles. Simscape, a Matlab/Simulink toolbox for modeling physical systems, is an ideally suited platform for developing and deploying models for systems and sub-systems that are critical for hybrid and electric vehicles. This text will focus on guiding the reader in the development of models for all critical areas of hybrid and electric vehicles. There are numerous texts on electric and hybrid vehicles in the market right now. A majority of these texts focus on the relevant technology and the physics and engineering of their operation. In contrast, this text focuses on the application of some of the theories in developing models of physical systems that are at the core of hybrid and electric vehicles. Simscape is the tool of choice for the development of these models. Relevant background and appropriate theory are referenced and summarized in the context of model development with significantly more emphasis on the model development procedure and obtaining usable and accurate results.

Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.

To resolve the urban transportation challenges like congestion, parking, fuel consumption, and pollution, narrow urban vehicles which are small in footprint and light in their gross weight are proposed. Apart from the narrow cabin design, these vehicles are featured by their active tilting system, which automatically tilts the cabin like a motorcycle during the cornering for comfort and safety improvements. Such vehicles have been manufactured and utilized in city commuter programs. However, there is no book that systematically discusses the mechanism, dynamics, and control of narrow tilting vehicles (NTVs).In this book, motivations for building NTVs and various tilting mechanisms designs are reviewed, followed by the study of their dynamics. Finally, control algorithms designed to fully utilize the potential of tilting mechanisms in narrow vehicles are discussed. Special attention is paid to an efficient use of the control energy for rollover mitigation, which greatly enhance the stability of NTVs with optimized operational costs.

The intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system.More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented.As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously.Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy.Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.

The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest.In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

As public attention on energy conservation and emission reduction has increased in recent years, engine idling has become a growing concern due to its low efficiency and high emissions. Service vehicles equipped with auxiliary systems, such as refrigeration, air conditioning, PCs, and electronics, usually have to idle to power them. The number of service vehicles (e.g. public-school-tour buses, delivery-refrigerator trucks, police cars, ambulances, armed vehicles, firefighter vehicles) is increasing significantly with tremendous social development. Therefore, introducing new anti-idling solutions is inevitably vital for controlling energy unsustainability and poor air quality. There are a few books about the idling disadvantages and anti-idling solutions. Most of them are more concerned with different anti-idling technologies and their effects on the society rather than elaborating an anti-idling system design considering different applications and limitations. There is still much room to improve existing anti-idling technologies and products.In this book, we took a service vehicle, refrigerator truck, as an example to demonstrate the whole process of designing, optimizing, controlling, and developing a smart charging system for the anti-idling purpose. The proposed system cannot only electrify the auxiliary systems to achieve anti-idling, but also utilize the concepts of regenerative braking and optimal charging strategy to arrive at an optimum solution. Necessary tools, algorithms, and methods are illustrated and the benefits of the optimal anti-idling solution are evaluated.

Thanks to the potential of reducing fuel consumption and emissions, hybrid electric vehicles (HEVs) have been attracting more and more attention from car manufacturers and researchers. Due to involving two energy sources, i.e., engine and battery, the powertrain in HEVs is a complicated electromechanical coupling system that generates noise and vibration different from that of a traditional vehicle. Accordingly, it is very important to explore the noise and vibration characteristics of HEVs. In this book, a hybrid vehicle with two motors is taken as an example, consisting of a compound planetary gear set (CPGS) as the power-split device, to analyze the noise and vibration characteristics. It is specifically intended for graduates and anyone with an interest in the electrification of full hybrid vehicles.The book begins with the research background and significance of the HEV. The second chapter presents the structural description and working principal of the target hybrid vehicle. Chapter 3 highlights the noise, vibration, and harshness (NVH) tests and corresponding analysis of the hybrid powertrain. Chapter 4 provides transmission system parameters and meshing stiffness calculation. Chapter 5 discusses the mathematical modeling and analyzes torsional vibration (TV) of HEVs. Finally, modeling of the hybrid powertrain with ADAMS is given in Chapter 6.

Vehicle rollover accidents have been a serious safety problem for the last three decades. Although rollovers are a small percentage of all traffic accidents, they do account for a large proportion of severe and fatal injuries. Specifically, some large passenger vehicles, such as large vans, pickup trucks, and sport utility vehicles, are more prone to rollover accidents with a high center of gravity (CG) and narrow track width. Vehicle rollover accidents may be grouped into two categories: tripped and untripped rollovers. A tripped rollover commonly occurs when a vehicle skids and digs its tires into soft soil or hits a tripping mechanism such as a curb with a sufficiently large lateral velocity. On the other hand, the untripped rollover is induced by extreme maneuvers during critical driving situations, such as excessive speed during cornering, obstacle avoidance, and severe lane change maneuver. In these situations, the forces at the tire-road contact point are large enough to cause the vehicle to roll over. Furthermore, vehicle rollover may occur due to external disturbances such as side-wind and steering excitation. Therefore, it is necessary to investigate the dynamic stability and control of tripped and untripped vehicle rollover so as to avoid vehicle rollover accidents.In this book, different dynamic models are used to describe the vehicle rollover under both untripped and special tripped situations. From the vehicle dynamics theory, rollover indices are deduced, and the dynamic stabilities of vehicle rollover are analyzed. In addition, some active control strategies are discussed to improve the anti-rollover performance of the vehicle.

A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the roaddrivervehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.

This book studies the design optimization, state estimation, and advanced control methods for cyber-physical vehicle systems (CPVS) and their applications in real-world automotive systems. First, in Chapter 1, key challenges and state-of-the-art of vehicle design and control in the context of cyber-physical systems are introduced. In Chapter 2, a cyber-physical system (CPS) based framework is proposed for high-level co-design optimization of the plant and controller parameters for CPVS, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estimation method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision controller is designed for a safety-critical CPVS. The detailed control synthesis and experimental validation are presented. The application results presented throughout the book validate the feasibility and effectiveness of the proposed theoretical methods of design, estimation, control, and optimization for cyber-physical vehicle systems.

Ever stringent vehicle safety legislation and consumer expectations inspire the improvement of vehicle dynamic performance, which result in a rising number of control strategies for vehicle dynamics that rely on driving conditions. Road profiles, as the primary excitation source of vehicle systems, play a critical role in vehicle dynamics and also in public transportation. Knowledge of precise road conditions can thus be of great assistance for vehicle companies and government departments to develop proper dynamic control algorithms, and to fix roads in a timely manner and at the minimum cost, respectively. As a result, developing easy-to-use and accurate road estimation methods are of great importance in terms of reducing the cost related to vehicles and road maintenance as well as improving passenger comfort and handling capacity. A few books have already been published on road profile modeling and the influence of road unevenness on vehicle response. However, there is still room to discuss road assessment methods based on vehicle response and how road conditions can be used to improve vehicle dynamics.In this book, we use several generalized vehicle models to demonstrate the concepts, methods, and applications of vehicle response-based road estimation algorithms. In addition, necessary tools, algorithms, and methods are illustrated, and the benefits of the road estimation algorithms are evaluated. Furthermore, several case studies of controllable suspension systems to improve vehicle vertical dynamics are presented.

The number of heavy-duty construction vehicles is increasing significantly with growing urban development causing poor air quality and higher emissions. The electrification of construction vehicles is a way to mitigate the resulting air pollution and emissions. In this book, we consider tracked bulldozers, as an example, to demonstrate the approach and evaluate the benefits of the electrification of construction vehicles. The book is intended for senior undergraduate students, graduate students, and anyone with an interest in the electrification of heavy vehicles.The book begins with an introduction to electrification of heavy-duty construction vehicles. The second chapter is focused on the terramechanics and interactions between track and blades with soil. The third chapter presents the architecture and modeling of a series hybrid bulldozer. Finally, the fourth chapter discusses energy management systems for electrified heavy construction vehicles.

Von immer komplexer werdenden technischen Produkten erwartet man heute nicht nur gesteigerte Leistungsfähigkeit, sondern auch erhöhte Zuverlässigkeit. Dieses Lehrbuch ist sowohl eine Einführung in die Zuverlässigkeitstheorie für Fahrzeug- und Maschinenbauingenieure als auch ein Nachschlage- und Vertiefungswerk für Zuverlässigkeitsspezialisten. Neben den praxisrelevanten statistischen Grundlagen und den qualitativen Zuverlässigkeitsmethoden wie der FMEA und der FTA befasst sich dieses Lehrbuch schwerpunktmäßig mit der Lebensdauerdatenanalyse und den Zuverlässigkeitstestmethoden.Der Stoff ist theoretisch fundiert und zugleich praxisnah aufbereitet. Aufbau und Darstellungsweise machen ein unmittelbares Arbeiten mit dem Buch möglich. Vertieft werden die beschriebenen Theorien, Begriffe und Vorgehensweisen durch Beispiele und Übungen mit Lösungen.Für diese Neuauflage wurde, neben der Erweiterung und Überarbeitung der Kapitel, ein neues Kapitel zum Thema Prognostics & Health Management (PHM) aufgenommen. Darin werden die neuesten Ansätze der Zuverlässigkeitsabsicherung mittels Health-Monitoring und Prognose der verbleibenden Restlebensdauer vorgestellt.

In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. 

The full-color Porsche 911 Carrera (Type 996) Service Manual: 1999-2005 is a comprehensive source of service information and specifications for Porsche 911 (Type 996) Coupe, Targa and Convertible models from 1999 to 2005. The aim throughout this manual has been simplicity and clarity, with practical explanations, step-by-step procedures and useful specifications. Whether you're a professional or a do-it-yourself Porsche owner, this manual will help you understand, care for and repair your Porsche. Engines covered: 1999-2001: 3.4 liter (M96.01, M96.02, M96.04) 2002-2005: 3.6 liter (M96.03) Transmissions covered: G96 (6-speed manual) A96 (5-speed automatic)

Every hot rod needs a good chassis, whether it's a traditional Model T track roadster, a Pro/Street fat-fendered Chevy, or something in between. "In How to Build Hot Rod Chassis," author Timothy Remus works with the pros at the SO-CAL Speed Shop, and other builders, to show you how to build a chassis that'll do the job right.Need to know what kind of spring to use? Don't know how to build a frame that's straight and square? This book shows you how, with detailed step-by-step how-to photo sequences. Chapters include information on: Planning your hot rod chassis. Building a frame. Front suspensions. Rear suspensions. Shocks and springs. Brakes. Hardware and plumbing. Drivetrain. Wheels and tires. There's also a detailed source guide that will help you find the parts you need, plus interviews with some of the country's top hot rod builders and stylists to lend valuable insight into hot rod building techniques and strategies.

Die häufig verwirrend anmutende Vielfalt und Komplexität der Maschinenelemente wird in der vorliegenden Buchreihe systematisch strukturiert und didaktisch so aufbereitet, so dass der Leser einen umfassenden Überblick über dieses zentrale Fach des Maschinenbaus gewinnt. In Fortführung von Band 1 geht es in "Maschinenelemente 2" vor allen um die Elemente, die für die Bewegung einer Maschine erforderlich sind: Das Kapitel "Lager" befasst sich mit Festgleitkörperlagern, Wälzlagern und hydrodynamischen Gleitlagern. Bei den "Welle-Nabe-Verbindungen" geht es darum, Torsionsmomente und Axialkräfte von Wellen auf umgebende Bauteile zu übertragen. Das abschließende Kapitel "Gleichförmig übersetzende Getriebe" führt in Reibradgetriebe, Riemengetriebe und Zahnradgetriebe ein. Zur weiteren Vertiefung schließt jeder dieser Abschnitte mit einem im Schwierigkeitsgrad differenziert abgestimmten Aufgabenteil ab, der Wissen in Können überführen soll, womit auf viele weiterführende Fächer vorbereitet wird. Die Lösungen zu den Aufgaben sind in tabellarischer Form im Anhang des Buches aufgeführt. Darüber hinaus sind die ausführlichen Berechnungen im Internet abrufbar. Ein Verzeichnis der Fachliteratur und der Normen erleichtert weitergehende Anwendungen.

"Maschinenelemente 3" erweitert und vertieft Band 1 und 2 durch die Betrachtung zunehmend komplexerer Maschinenelemente. Die für das Fach typische Konzentration auf das einzelne Element wird durch übergreifende Betrachtungen ergänzt, wodurch das Verständnis des Zusammenspiels der Maschinenelemente in einer vollständigen Maschine erleichtert wird. Der Abschnitt "Verformung und Verspannung" erweitert das ursprünglich eng gefasste Kapitel "Federn" im Hinblick darauf, dass die Verformungsanalyse von so manchem realen Bauteil viele wichtige Aussagen liefert. Der Abschnitt "Reibung, Schlupf, Wirkungsgrad und Verschleiß" stellt den Anschluss an die Tribologie her und macht mit den heute so wichtigen Aspekten der Energie- und Materialeinsparung vertraut. Weiterhin dienen diese Grundlagen dem Verständnis der folgenden Kapitel "Bremsen" und "Kupplungen". Der Abschnitt "Getriebe als Bestandteil des Antriebes" führt auch in das Planentengetriebe ein. Damit wird auf viele weiterführende Fächer vorbereitet, besonders auf die Konstruktionslehre, den Werkzeugmaschinenbau, die Fahrzeugtechnik und die Mechanischen Antriebe. Auch dieser Band enthält zur weiteren Vertiefung nach jedem Abschnitt einen im Schwierigkeitsgrad differenziert abgestimmten Aufgabenteil, der Wissen in Können überführen soll. Die Lösungen zu den Aufgaben sind in tabellarischer Form im Anhang des Buches aufgeführt. Darüber hinaus sind die ausführlichen Berechnungen im Internet abrufbar. Ein Verzeichnis der Fachliteratur und der Normen erleichtert weitergehende Anwendungen.

One of the best ways to encourage women to enter or continue in engineering is to hear about and see examples of other women in the field to whom they can relate. Joan Wills and Karen Ramsey-Idem bring together diverse, talented women across the commercial vehicle industry to share her unique experiences including the habits, motivations, triumphs, defeats, and lessons learned that helped each thrive in her leadership roles. These leaders represent three different generations across U.S., Europe, Africa and Asia. Doing the Hard Work provides insights that have relevance for women at all stages in their careers, whether its young women interested in pursuing a career in the auto industry, those looking for their next strategic move, or those seeking insight and inspiration.