Session: CT-19-01 AI, Data Engineering and Data Analysis-1

Paper Number: 154467

154467 - A Case for Applications of Machine-Vision-Aided Industrial Robotics in Pipeline Bolting 

Abstract: 

This paper addresses the case for a practical application of AI by incorporating it into the process of installing and removing bolts from piping joints. The major cases identified are reducing human error and bridging the construction labor gap. This paper then discusses the application of AI by describing the 3 guiding design objectives identified by industry, safety, speed, and quality, and the various industries where this application is immediately relevant.

The current process for industrial bolting is still heavily reliant on human labor, and thus prone to human error. As industrial assets age, they become less forgiving of human error and thus more prone to risk of incident. There is a direct correlation between the quality of bolting labor and botling reliability. Bolting labor quality needs to improve to mitigate risk of industrial incidents. New research and development as funded by Nexterity's lead investor, Roadrunner Venture Studios, has identified machine vision as a key component of improving bolting reliability by automating select conditions of bolting labor. 

In parallel, there is a continued increase in demand for bolting labor projected across industrial construction. It will be difficult for construction crews to meet industry needs at the current staffing levels. Nexterity's research and development of machine-vision enabled industrial robotics to improve the safety, speed, and quality of bolting labor is also a step in bridging the construction labor gap.

After surveying over 75 industry leaders, it became apparent that bolting labor is analyzed by 3 major factors: safety, speed, and quality. These factors are significant in piping alignment, worker safety, flange seal integrity, equipment reliability, and worker efficiency. Not only is the quality of bolting labor important for the duration of the work, but the impact of the labor is in effect over the course of the equipment runtime until the next turnaround cycle (often 4+ years) long after the bolting crew has left the jobsite. To ensure that the highest quality bolting labor is delivered to piping joints, Nexterity's research and development has led to new designs of a hands-free bolting robot. This new approach has been intentionally designed following industry guidance along the same 3 criteria: safety, speed, and quality.

Nexterity's research and product development is initially targeted towards industrial infrastructure in the following areas: facility maintenance, capital projects, facility retrofits, and new facility construction. This positions Nexterity as a team player across all types of energy infrastructure and in a variety of relevant industries: oil & gas, chemical/petrochemical, pharmaceuticals, food & beverage, water/wastewater/water treatment/sewage, agriculture, cosmetics, irrigation, nuclear, mining, and green/sustainable manufacturing. There is a strong case for improving bolting reliability and bridging the construction labor gap across all of these industries. 

Presenting Author: Lindsey Elliott Nexterity, Inc.

Presenting Author Biography: Ms. Lindsey Elliott attended Texas Christian University on a full-ride merit-based scholarship where she graduated with Engineering Honors, a BS in Engineering with Mechanical Emphasis, and a BA in Mathematics. While at TCU, Lindsey led a R&D project as part of her larger senior design team's collaboration with Lockheed Martin Missile & Fire Control. Following graduation, Lindsey worked as a Cost Engineer with ExxonMobil Global Projects and then was essential at ExxonMobil's refinery in Baton Rouge, LA through COVID while rolling out new digital tech solutions to a 1500+ person workforce. Lindsey then went to work as an AI program manager for Qualcomm R&D and a Manufacturing Technology Engineer for Sabic. Through these experiences, she identified many opportunities to bring strategic embedded-AI solutions into industrial construction. She is passionate about building these solutions with strong ethics and delivering them in a way they will be well-received by the overexerted construction workforce they will support.

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