The Fundamentals of Geometric Dimensioning and Tolerancing 2018 Using Critical Thinking Skills by Alex Krulikowski reflects the technical content found in the latest release of the ASME Y14.5-2018 Standard. This book includes several key features that aid in the understanding of geometric tolerancing. Each of the textbook's 26 chapters focuses on a major topic that must be mastered to be fluent in the fundamentals of GD&T. Each topic includes a goal that is defined and supported by a set of performance objectives that include real-world examples, verification principles and methods, and chapter summaries. There are more than 260 performance objectives that describe specific, observable, measurable actions that the student must accomplish to demonstrate mastery of each goal. Learning is reinforced by completing three types of exercise problems, along with critical thinking questions that promote application of GD&T on the job.
Shift fork is a key shifting element in manual and dual clutch transmission for smooth operations of gear shifting. One of the main criteria for robust design of shift fork is stiffness symmetry. Stiffness symmetry ensures straight movement of sleeve onto hub and thus helps in achieving good shift quality. Stiffness symmetry also ensures equal load distribution across two or three pads of shift fork while in operation. In this paper, we intend to demonstrate finite element simulation driven design process to improve stiffness symmetry of shift fork. Various parameters affecting stiffness symmetry are analyzed through design of experiment and selected best range for optimum design of shift fork. Output of this study will be useful for improving any design of shift fork to meet different targets of stiffness symmetry for all automobile suppliers and manufactures.
Supervised learning, unsupervised learning & reinforcement learning are the three basic learning techniques for training machine learning and artificial intelligence models. Deep learning models can be supervised or unsupervised. In auto industry, the deep learning applications use the supervised learning technique. Models trained with the unsupervised learning technique produce generalized results. It requires a huge set of tagged/labeled datasets to train these supervised deep learning networks. Self-supervised learning is a technique where the AI model learns the features from the training data, without tags or labels and tags the data by itself. This tagged/labelled data can be further used to train other AI models. This saves the cost of tagging the data. Tagging or labeling is a time-consuming activity, which also needs human effort to do the job.
As per WHO 2018 report, pedestrian fatalities account for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance highly depends on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface.
In order to meet the challenges of future CAFE regulations & pollutant emission, vehicle fuel efficiency must be improved upon without compromising vehicle performance. Optimization of engine breathing & its impact on vehicle level fuel economy, performance needs balance between conflicting requirements of vehicle Fuel Economy, performance & drivability. In this study a Port Fuel Injection, naturally aspirated small passenger car gasoline engine was selected which was being used in a typical small passenger car. Simulation approach was used to investigate vehicle fuel economy and performance, where-in 1D CFD Engine model was used to investigate and optimize Valve train events (Intake and exhaust valve open and close timings) for best fuel economy. Engine Simulation software is physics based and uses a phenomenological approach 0-D turbulent combustion model to calculate engine performance parameters. Engine simulation model was calibrated within 95% accuracy of test data.
Climate change is a global phenomenon now and countries across the globe are working towards reducing emissions by bringing in stricter legislations on emissions and CO2. India is also facing huge challenges on pollutions in large cities. Reports suggest that 7 of the 10 most polluted cities of the world lie in India. The growing public opinion towards cleaner air and reduced greenhouse gaseous emissions has sensitized the matter and has led to drafting of strict emission legislations in India during the past few years. The leap frogging from BS 4 to BS 6 in 2020 by skipping BS 5 norms showed the intent of the GOI towards emission reduction. The BS 6 legislation is not limiting to meeting norms with legislative emission cycle but will also focus from year 2023 onto real driving emissions on actual roads. GOI is also proposing to implement fleet CO2 emission norms (CAFÉ) by 2022 to regulate the CO2 emissions.
The current paper focuses on the compact HVAC component development for electric passenger vehicles running in countries where the external ambient conditions are harsh. Various previous studies have shown that the energy required for HVAC system alone is about 12-15 percent of the overall vehicle energy demands. Due to very high thermal loads, the Electric Vehicles operating in such countries will obviously fall under the higher HVAC energy consumption band. In addition to the energy demand, the cooling requirements like shorter pull-down time adds further challenges to the HVAC design. Another major challenge being faced by the EV manufacturers is the concerns due to range which has resulted in compact vehicles having less space for HVAC and other subsystem components. The current paper proposes an approach for replacing the conventional air-cooled condenser by liquid-cooled condenser. A liquid-cooled condenser will be much more compact than a conventional condenser.
SAE J4001 provides instruction for evaluating levels of compliance to SAE J4000. Component text (Sections 4 to 9) from SAE J4000 is included for convenience during the evaluation process. Applicable definitions and references are contained in SAE J4000. SAE J4000 tests lean implementation within a manufacturing organization and includes those areas of direct overlap with the organization’s suppliers and customers. If applied to each consecutive organizational link, an enterprise level evaluation can be made. SAE J4001 relates the following approximate topic percentages to the implementation process as a whole: SAE J4001 is to be applied on a specific component basis. Each of the 52 components tests part of, one, or multiples of the specific requirements of lean implementation. Implementation throughout an organization may be measured by evaluating all of the components.
As the move to decrease physical prototyping increases the need to virtually prototype vehicles become more critical. Assessing NVH vehicle targets and making critical component level decisions is becoming a larger part of the NVH engineer’s job. To make decisions earlier in the process when prototypes are not available companies need to leverage more both their historical and simulation results. Today this is possible by utilizing a hybrid modelling approach in an NVH Simulator using measured on road, CAE, and test bench data. By starting with measured on road data from a previous generation or comparable vehicle, engineers can build virtual prototypes by using a hybrid modeling approach incorporating CAE and/or test bench data to create the desired NVH characteristics. This enables the creation of a virtual drivable model to assess subjectively the vehicles acoustic targets virtually before a prototype vehicle is available.
In this paper, we present the process we propose to evaluate the effect of the laminated glass interlayer material on the Noise, Vibration and Harshness (NVH) performance of vehicles. The process starts from lab measurements to evaluate the glass damping and sound transmission loss and demonstrate the benefit of using optimized interlayer material. The results are then used to develop filters to adapt a vehicle model created in a driving simulator for NVH. With this process, it is not necessary to physically install the windshield in the vehicle to be able to listen and experience the benefits of switching to an optimized interlayer material. We use results from a real application case to verify the validity of the process and we share in our conclusions the future direction for this work to make the process completely virtual by using CAE simulation data.
Full vehicle modal identification is a major challenge for both experimental and simulated modal results. A global modal is usually masked by nearby local modes, so that even well-experienced engineers have difficulty to identify vehicle modes efficiently. Besides different vehicle configurations e.g. SUV, MPV and hatchback can make the challenge even greater, since the same modal for them will present different characteristics. This paper proposes a deep neural network for vehicle modal identification. This method takes advantage of the deep learning method which has achieved outstanding performance in language translation, computer vision and image processing. It also has the potentiality to improve modal identification efficiency. In general commercial neural network applications, a large number of data is available for training to achieve a robust output.
Abstract The ability of senior citizens as well as other members of the general population to engage in an effective manner with technology is of increasing importance as new and innovative technologies become available. While recognizing the challenges that technologies can have on different populations, the ability to interact successfully with new technologies will, for seniors, have important consequences that can affect their quality of life and those of their families in numerous and important ways. This study, building upon previous research, examines the major dimensions of decision-making regarding attitudes toward autonomous vehicle technologies (ATVs) and their use. The study utilized data from a study of senior citizens in the Dallas-Fort Worth (DFW) area and compared the results with a sample of graduate students from a local university.