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Fundamentals of Geometric Dimensioning and Tolerancing 2018: Using Critical Thinking Skills
- Book
- PD0220019-CB00
To be published on
2019-12-31
by SAE International in United States
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. It's the most practical and
easy-to-use GD&T text on the market.
**SPECIAL: Pre-Order your copy by December 31, 2019 and receive
10% off the $140 retail price.
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Caterpillar launches next-gen mini hydraulic excavator, skid steer and compact track loaders
SAE Truck & Off-Highway Engineering: December 2019
- Magazine Article
- 19TOFHP12_12
Published
2019-12-01
by SAE International in United States
Covering about 2.5 million ft2 and including roughly 2,800 exhibitors, the triennial ConExpo-Con/Agg event boasts an evenlarger footprint for 2020 with the addition of the Festival Grounds. As one of the major exhibitors at North America's largest construction tradeshow, taking place March 10-14 in Las Vegas,
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SMART HONKING
- Technical Paper
- 2019-28-2463
Published
2019-11-21
by SAE International in United States
Smart Honking Keywords-Safety, Connectivity, GPS M. Priyanka, Mahindra&Mahindra, India Sai Himaja Nadimpalli, Mahindra&Mahindra,India Keywords-Honking , Infotainment , GPS Research and/or Engineering Questions/Objective: In India unnecessary vehicular honking is the main reason for noise pollution. The problem is worst at traffic signals where drivers start honking without waiting for the signal to turn green or for traffic to move. Drivers show no respect to the law that prohibits the use of horn at traffic signals and other silent zones such as areas near hospitals, schools, religious places and residential areas. Vehicular honking in cities has reached at an alarming level and contributes approximately 70% of the noise pollution in our environment.The unwanted sound can affect human health and behavior, causing annoyance, depression, hypertension, stress, hearing loss, memory loss and panic attacks. Most of the drivers try to release their frustration and tension by blowing horns, possibly due to lack of awareness regarding the negative effects of noise but most likely it is because of the lack of civic sense.. Limitations: There is a provision of sign…
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IMPROVE NVH CHARACTERISTICS OF ENGINE OIL PAN BY OPTIMIZATION & LIGHT WEIGHING WITH DEEP LEARNING PROCESS
- Technical Paper
- 2019-28-2552
Published
2019-11-21
by SAE International in United States
Recent Years “NVH” is gaining lots of attention as the perception of vehicle quality by a consumer is closely aligned to NVH Characteristics. Demand on Vehicle Light weighting to compliance the environmental norms with powerful engines challenging the “Vehicle NVH”, powertrain induced noise will be continued to be a primary factor for all IC engine vehicles. Component level NVH refinement is necessary to control the overall NVH characteristics of vehicle with lighter Vehicle goal. Current Paper works starts with physical testing the Engine oil pan of the most popular vehicle and build an equivalent simulation model by reverse engineering the design and match similar performance trend in simulation model. After building baseline simulation model, conduct shape, topology, gauge and material optimization to improve weight and performance of Oilpan. In addition to the Simulation DVPS to study the complete NVH characteristics oil pan models, a deep Learning model developed with power of GPUs to disrupt oil pan design methodology as well as optimizing the weight, Performance and cost . Every design engineer would like to optimize…
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Electrification System Modeling with Machine/Deep Learning for Virtual Drive Quality Prediction
- Technical Paper
- 2019-28-2418
Published
2019-11-21
by SAE International in United States
A virtual 'model' is generally a mathematical surrogate of a physical system and when well correlated, serves as a basis for understanding the physical system in part or in entirety. Drive Quality (DQ) defines a driver's 'experience' of a blend of controlled responses to an applied input. The 'experience' encompasses physical, biological and bio- chemical perception of vehicular motion by the human body. In the automotive domain, many physical modeling tools are used to model the sub-components and its integration at the system level. Physical Modeling requires high domain expertise and is not only time consuming but is also very 'compute-resource' intensive. In the path to achieving 'vDQP (Virtual Drive Quality Prediction)' goal, one of the requirements is to establish 'well-correlated' virtual environments of high fidelity with respect to standard test maneuvers. This helps in advancing many developmental activities from a Analysis, Controls and Calibration standpoint. Recently, machine/deep learning have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear real world systems. This paper investigates the effectiveness…
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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling
SAE International Journal of Transportation Safety
- Journal Article
- 09-07-02-0009
Published
2019-11-14
by SAE International in United States
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events. The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.
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AVSC Best Practice for In-Vehicle Fallback Test Driver Selection, Training, and Oversight Procedures for Automated Vehicles Under Test
- Best Practice
- AVSC00001201911
- Current
Published
2019-11-08
by SAE Industry Technologies Consortia in United States
ABSTRACT
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Weighted Distance Metrics for Data Association Problem in Multi-Sensor Fusion
- Technical Paper
- 2019-01-5022
- DOI: https://doi.org/10.4271/2019-01-5022
Published
2019-11-04
by SAE International in United States
Traffic accidents are the world's leading threat to human safety. The majority of traffic accidents are due to human error. Advanced Driver Assist Systems (ADAS) can reduce human error, therefore has the potential to effectively improve the safety of road traffic. The perception module in an ADAS understands the surrounding environment of the subject vehicle and therefore is the prerequisite for planning and control. Due to the limitation of computational constrain of Electronic Control Units, ADAS system commonly uses object-leveled multi-sensor fusion, in which raw data is processed to detect objects at the sensory level. In multi-sensor fusion, the task of assigning new observations to the existing tracks, known as Data Association problem, requires distance metrics to present the similarity between tracks. In the literature, metrics, such as standardized Euclidean distance and Mahalanobis distance has been used. Though accounting for the scale and correlation of the data, the existing metrics cannot account for the importance of each feature in predicting their dissimilarity. As a result, weighting factors are added to the distance metrics and they…
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Study on Robust Motion Planning Method for Automatic Parking Assist System Based on Neural Network and Tree Search
- Technical Paper
- 2019-01-5059
- DOI: https://doi.org/10.4271/2019-01-5059
Published
2019-11-04
by SAE International in United States
Automatic Parking Assist System (APAS) is an important part of Advanced Driver Assistance System (ADAS). It frees drivers from the burden of maneuvering a vehicle into a narrow parking space. This paper deals with the motion planning, a key issue of APAS, for vehicles in automatic parking. Planning module should guarantee the robustness to various initial postures and ensure that the vehicle is parked symmetrically in the center of the parking slot. However, current planning methods can’t meet both requirements well. To meet the aforementioned requirements, a method combining neural network and Monte-Carlo Tree Search (MCTS) is adopted in this work. From a driver’s perspective, different initial postures imply different parking strategies. In order to achieve the robustness to diverse initial postures, a natural idea is to train a model that can learn various strategies. As artificial neural network has outstanding potential in representing and learning knowledge, a neural network is utilized to provide prior knowledge, which is trained through supervised learning by a novel method that imitates human learning style. However, the training accuracy…
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Autopilot Strategy Based on Improved DDPG Algorithm
- Technical Paper
- 2019-01-5072
- DOI: https://doi.org/10.4271/2019-01-5072
Published
2019-11-04
by SAE International in United States
Deep Deterministic Policy Gradient (DDPG) is one of the Deep Reinforcement Learning algorithms. Because of the well perform in continuous motion control, DDPG algorithm is applied in the field of self-driving. Regarding the problems of the instability of DDPG algorithm during training and low training efficiency and slow convergence rate. An improved DDPG algorithm based on segmented experience replay is presented. On the basis of the DDPG algorithm, the segmented experience replay select the training experience by the importance according to the training progress to improve the training efficiency and stability of the training model. The algorithm was tested in an open source 3D car racing simulator called TORCS. The simulation results demonstrate the training stability is significantly improved compared with the DDPG algorithm and the DQN algorithm, and the average return is about 46% higher than the DDPG algorithm and about 55% higher than the DQN algorithm.