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Fundamentals of Geometric Dimensioning and Tolerancing 2018: Using Critical Thinking Skills
- Book
- PD0220019-CB00
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.
Caterpillar launches next-gen mini hydraulic excavator, skid steer and compact track loaders
SAE Truck & Off-Highway Engineering: December 2019
- Magazine Article
- 19TOFHP12_12
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,
SMART HONKING
- Technical Paper
- 2019-28-2463
IMPROVE NVH CHARACTERISTICS OF ENGINE OIL PAN BY OPTIMIZATION & LIGHT WEIGHING WITH DEEP LEARNING PROCESS
- Technical Paper
- 2019-28-2552
Electrification System Modeling with Machine/Deep Learning for Virtual Drive Quality Prediction
- Technical Paper
- 2019-28-2418
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
AVSC Best Practice for In-Vehicle Fallback Test Driver Selection, Training, and Oversight Procedures for Automated Vehicles Under Test
- Best Practice
- AVSC00001201911
- Current
ABSTRACT
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
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
Autopilot Strategy Based on Improved DDPG Algorithm
- Technical Paper
- 2019-01-5072
- DOI: https://doi.org/10.4271/2019-01-5072