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.
Quality management professionals across the global aerospace and defense community are convening for one hour – Wednesday, October 27th, starting at 10 am Pacific Daylight Time (PDT) – to discuss the AS9100 international standard. Register to take part in the free AeroTech webinar, hosted by SAE International and Tektronix, designed to help manufacturers, contractors, and subcontractors throughout the global aviation, space, and defense supply chain keep pace with and meet the requirements of AS9100 international quality management system standard.
The function of uniform terminology is to promote understandable and exact communication in the area of vision. A great deal of effort has been expended to make these definitions suit this purpose. It is recognized that this terminology, like other dictionaries, must be revised periodically to reflect current usage and changing needs. The Driver Vision Subcommittee of the Human Factors Engineering Committee, therefore, solicits suggestions for improvements and additions to be considered in future revisions.
This standard establishes the minimum requirements for training, examination, and certification of aerospace coatings application personnel applying liquid organic coatings to interior structural or exterior substrates. It establishes criteria for the certification of personnel requiring appropriate knowledge of the technical principles underlying aircraft surface preparation and coatings application for both protective and decorative purposes. Persons who successfully complete the requirements of this certification standard are considered to be able to successfully and consistently perform a broad spectrum of aerospace coatings application tasks to achieve the desired engineering purposes. This certification is not intended to determine or replace any aerospace coating operation’s proprietary engineering for the depainting, preparation, or subsequent application of organic coatings materials to aircraft surfaces.
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.