Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1826508

Resolution of Tolerance Variations

Trent, Bruce Collins·Sandoval, Donald Leon·Velappan, Manikantan·Mitchell, Bradley Leon
View original source ↗Read source document ↗Export metadata ↓

Abstract

The connector/adapter assembly (CAA) is one component of a higher-level assembly. A simplified representation of the CAA is illustrated in Figure 1. The location of each CAA is required to meet a specific positional requirement. Several CAAs have been measured and found to be out of tolerance. The connector and the adapter are welded to each other such that the cylindrical axes of the two components are coaxial. The positional tolerance condition requires that all points on the outer surface of the CAA must be within a defined maximum diameter, as illustrated in Figure 1. Two different methods have been developed to measure the CAA positions from coordinate measuring machine (CMM) data. One method (RevD) extrapolates the data from two circles approximately midway from the weld region to each end. The second method (RevF) measures points that are much closer to the actual ends.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Trent, Bruce Collins, Sandoval, Donald Leon, Velappan, Manikantan, Mitchell, Bradley Leon. 2021-11-29. Resolution of Tolerance Variations. https://doi.org/10.2172/1826508

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗