As manufacturers seek more unified operations with greater visibility, many jump at the chance to gather data only to find themselves buried in meaningless and costly information. Renishaw Central aims to capture the right information and reveal hidden machine data that improves process control and quality.

The cloud is often revered as a sacred watering hole where all data may happily gather and intermingle under the magical governance of AI, which can transform any hopeless datum into a meaningful contrail of predictive knowledge.

But storing mass information doesn’t equate to storing the right information. And sorting through the noise can be costly and unproductive.

Plus, many manufacturers prefer to hold onto now-called legacy systems that still serve their purpose while adding new technology incrementally. Collecting and pooling data from many machines of different eras can be a massive undertaking with no guarantee that the information will be helpful.

“They are very much dreaming of the cloud, but when it comes to a 20-year-old controller, the dream can evaporate and become a nightmare,” said James Hartley, applications and marketing manager of Renishaw’s Machine Tool Products Division.

Deciding where to capture information and how to use it to improve processes can be burdensome. Why would anyone waste time collecting data and creating reports if the same manufacturing problems persist? If data isn’t doing anything useful to improve production times, scrap rates, or quality metrics, then it just wastes more time and money, adding to inefficiencies.

All too common, data flows are visible until they get to the machine shop. Machine shop personnel hold valuable knowledge about what makes each machine work. They know which machines perform the best for specific jobs or need more frequent servicing. This knowledge remains at the shop level, and people learn to adapt to the machines.

But reliance on personnel know-how creates a data vacuum and counters more unified facility control systems. To plant engineers, there seems to be an invisibility cloak over the shop until quality data pops up later. And without visibility, they can’t make real-time data-driven recommendations on the process.

“Many people have the hardware, skills, and technology, but they just don’t have a grip on what’s really going on behind the scenes,” said Guy Brown, development manager of Renishaw Central.

But what if there was a way to extract hidden information from the machines? What if real-time data exchange could provide a holistic view of the machine shop so it’s no longer a data vacuum?

Renishaw Central, an on-premises connectivity and data platform that connects machines and extracts actionable data, aims to do just that — and more.

Digital journeys evolve from their problems

The engineering company Renishaw is known for its metrology, measurement, motion control, and manufacturing technologies. The supplier also develops advanced analytical instruments and machines for healthcare. As a manufacturer of its own products, company engineers deeply understand the end-to-end challenges that OEMs face and how data visibility can improve process control.

In fact, such control is the core of Renishaw Central. The platform connects a range of new and legacy equipment to help operators understand what’s happening at the machine level. The intention is to limit variability and monitor quality in real time and not at the end of the line. With patented Intelligent Process Control (IPC) software, it can also capture near-line quality information from an Equator gauging system, for instance, and send information such as tool-offset updates back to the machine to improve consistency and control.

“If someone manually changes a tool offset or if a tool offset is wrong, the process is inaccurate, and the quality can go haywire. So, we’ve paid just as much attention to the collection of tool-setting data as we have for workpiece setup or inspection,” said Hartley.

While collecting data is one task, acting on it is quite another. In the 1980s, Renishaw printed most measurement results for backup with dot matrix printers. But the information typically sat dormant unless they found a quality problem and referenced it in a post-mortem investigation. Though Renishaw pioneered on-machine process control, even they weren’t actively using the data.

Then, in the 1990s and early 2000s, a shop floor data collection system worked over the DNC system that could push up programs to the machines, take down measurement results, and store the data centrally. The data was still not mined or analyzed for any purpose other than figuring out quality control issues after they occurred.

“We collected a lot of data and never went back to look at it. It was just kept for historical purposes — for traceability reasons — but filed away and not really addressed,” said Brown. “A very small portion was functional and worked with the kitting stations, but 90% of the data just sat there.”

In the 2010s, they implemented an external Industry 4 platform to connect more equipment, but it didn’t collect metrology or measurement data and required user input in many cases. So, the system depended on the quality of manually inputted information.

Now, in the 2020s, they have Renishaw Central to build upon previous efforts and add a real-time data analysis piece — but not just for data analysis’ sake. Renishaw Central extracts and reveals actionable data that would otherwise remain hidden and unshared.

“We started asking, ‘Why do machines stop? Why are things not quite what we expect them to be? What have we missed? What are the unknown unknowns?’” said Brown. “And we wondered how we could use the data day by day to transition from a reactive to a proactive position.”

To test its own product, the company deployed the platform in the U.K. at its low-volume, high-variety manufacturing facilities in Miskin and Stonehouse. The platform connected 67 machines across both facilities, revealing that two error types were responsible for 82% of automation stoppages. After remedial action, automation stoppages decreased by 69%. Additionally, using the IPC set-up feature, set-up times on sliding-head lathes decreased by more than 85%.

A global selection of pilot customers also confirmed that Renishaw Central provided the right data and insights to improve their processes and performance.

Bringing machine data to life

The Renishaw Central platform is machine-agnostic and uses APIs to connect different machines to a unified, on-premises network.

“No one company is going to own Industry 4,” said Hartley. “Therefore, Renishaw is a neutral party, providing all the big controller manufacturers and OEMs with software. We are a licensee for almost all their APIs, which means we can make native connections to those controllers.”

In some cases, data flows one way from machines to Renishaw Central, which collects measurement results, quality metrics, cycle time, status, utilization, warnings, alerts, yield rates, offsets, and other process control information.

What differentiates the platform is its two-way communication capability. For processes the platform can control, there’s a two-way data exchange in which the system analyzes the data received, makes real-time decisions, and then sends updates back to the machines.

“Data is not enough,” said Brown. “The data itself is just values, and those values by themselves can often mean nothing. It’s only when you start to put thresholds around data and add context that it becomes informative and actionable. If you can’t perform an action at the end of it, what’s the point?”

During Renishaw’s pilot program, Brown noticed that the data showed lots of little stoppages, yet when he walked over to the machines, they all seemed to be working fine. He learned that operators adapted to the machines’ behavior and pressed a button to resume the process whenever it stopped. The practice became routine and unnoticed until Renishaw Central revealed each occurrence.

“These somewhat hidden stoppages were accepted, and the solution was to quickly check something and press cycle start,” said Brown. “This is a problem when you’re running lights out. If you step out of the factory at 10:00 pm on a Friday and a machine stops five minutes later, you’ve lost the whole weekend.”

Instead of having engineers lead remediation by evaluating the data and making changes, Renishaw brought in all the people closest to the process as a continuous effort, not a one-off exercise. They started with a workshop-type approach, where engineers, operators, and maintenance personnel looked at the data and initiated interesting conversations. Each team chimed in with their viewpoints to validate and challenge each other’s perspectives. As it turned out, Renishaw’s digital journey sparked a cultural change that now lines its pathway toward even smarter manufacturing.

“It wasn’t one resolution, it was three resolutions, and it needed all three teams to make it work,” said Brown. “They all had to do something different to help resolve this one particular problem.”

To be clear, the data isn’t meant to target people — it’s all about the machines. It’s about the measurement data, tool setting data, in-cycle gauging data, and offset update data combined with alerts generated from the machine to notify when things are out of control or about to be out of control.

Additionally, Renishaw Central isn’t designed as a catch-all for any factory data. However, it does use a Web API that can connect to various data sources and software. For instance, during the resolution workshops, Renishaw realized they needed to aggregate multiple data sources to build dashboards. So, they started using Power BI to create custom reports and review past data.

“The CNC machine provides limited information,” said Hartley. “Let’s say it knows its tool number 10. But to make an intelligent decision further down the line, we want more details — that tool number 10 is a slot drill and comes from manufacturer A, for example. Such analysis can reveal tooling from manufacturer A is better than tooling from manufacturer B. Unconnected machine tools will never give you that kind of information. But software such as Power BI can with data from multiple sources, including Renishaw Central.”

Renishaw Central is best suited for manufacturers already on a mature data journey with in-house IT departments and a networked factory. It provides machine-shop connectivity, consistency, control, and confidence with actionable end-to-end data. The ability to predict, identify, and correct process errors before they happen is the crux of automation for long-term productivity, capability, and efficiency.

Renishaw
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