From Days to Minutes: How Smart Manufacturing Accelerates High-Quality Steel Pipe Production

Businesses live and die by the quality of their products. So, reducing or eliminating defects, errors and inefficiencies is mission critical.

At Zekelman Industries, new smart manufacturing technologies are transforming quality assurance (QA) in steel pipe and tube manufacturing to boost product quality and also ensure that customers obtain products even sooner. “Quality assurance is advancing rapidly and we’re taking a leadership position in our industry,” says Gene Metro, quality assurance manager, Zekelman Industries.

At Zekelman’s Wheatland Tube division, the QA process incorporates connected sensors, laser technology and specialized production software that automates complex tasks across a variety of systems and stages in the production process. This gives teams access to instant readings and real-time data to monitor events and operational processes more closely than ever before.

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In manufacturing, effective QA reduces scrap and rework, lowers energy costs, and squeezes out other inefficiencies.

At Wheatland’s facilities in the Pennsylvania and Ohio Valley region, the focus is on eliminating time-consuming, manual processes that more often lead to errors and product imperfections. Smart automation — in some cases driven by artificial intelligence (AI) — is accelerating that progress. “One piece of technology on its own can be semi-effective, but multiple technologies, linked together, can aid and really multiply your effectiveness,” Metro explains.

The heart of this more advanced framework for quality assurance starts with a programmable logic controller (PLC), which ties together various smart manufacturing technologies, including robotic systems, assembly line equipment and sensors. The PLC resides on the shop floor and delivers data that support a more advanced manufacturing framework.

“In the past, everything — from cutting metal and manufacturing pipe and other components to inspecting finished products — required manual intervention and was human-based,” Metro says. “Now we can track everything in a centralized way. We can now combine and link numerous quality assurance processes.”

The goal is to augment human capabilities — not replace them. So while automation and AI are transforming factory jobs, they are also driving demand for new roles, such as PLCs, that work alongside the technology.

QA Charges Ahead

Wheatland and other divisions at Zekelman incorporate a range of quality assurance technologies, including:

• AI-based laser systems automatically measure the pipe length to ensure that it is the precise size required.
• Smart sensors track pipes as they move along the pipe mill. This translates into better, faster and more optimized production, with fewer errors and delays.
• Robust and consistent process controls surround galvanization and pickling processes. Robotics and other machinery ensure that cleaning and galvanization processes and other critical tasks are performed in a highly controlled process.

“Thanks to increased automation of the QA process, we’re able to quickly adjust our processes to account for slight variability that will always exist in raw materials and our equipment,” Metro says. “And we’re talking minutes, not hours or days.” And it’s not only customer satisfaction levels that have increased. New smart manufacturing technologies in QA also support Zekelman’s drive for constant improvement in safety of its facilities, while providing real-time visibility and data to better track the entire production process.

In the future, smart manufacturing technologies will only further improve product quality and efficiency in the sector. As Metro reiterates, “Quality is the foundation for successful products in the industries we serve. Technology allows us to take performance to a higher level.”

At Zekelman, we’re committed to manufacturing excellence. We embrace innovation and automation to augment human capabilities, create new skilled jobs and build up communities. Learn how we do things differently.