Digitalization in aerospace manufacturing

According to a recent report, mentions of digitalization in the filings of aerospace and defense companies have increased 225% since 2016. Digitalization methodologies include additive manufacturing (AM), materials science, and the Industrial Internet of Things (IIoT) all becoming ubiquitous in the industry’s race to manufacture stronger, lighter, and more efficient aircraft.

Let’s examine digitalization and what operators need to consider when connecting machines. For Sandvik Coromant, this describes connecting machines across a manufacturing facility or multiple manufacturing sites. Data collected from connected machines allows information to be visualized easily so an operator can make decisions based on real-time data. However, collating data alone isn’t enough.

Understanding machine analytics

When machine shops depend on the status of green, yellow, or red Andon lights, they’re not making the best use of the data their machines are generating. What’s a red light telling you? Has the machine crashed? Is engineering doing a test, or is the machine out of work material?

In aerospace, where digitalization is more common, manufacturers will have in-house analytics take historical machine data to assess processes afterward. However, the latest generation of connected machine monitoring systems provides real-time, in-depth analytics capturing and reporting data underlying those red, yellow, and green lights. That data’s based on rules established between the condition-monitoring software provider and machining operation. These rules can follow industry-standard measurements, incorporating factors such as machine utilization, performance, and production quality. These platforms capture and report data determined to be crucial to sustain continuous improvement and lean manufacturing efforts.

Start small, focus on specifics

With many machines generating a lot of data, what data do you focus on to make the best use of powerful condition monitoring software? Apply classic lean principles to start. Identify persistent issues or bottlenecks – waste, unscheduled downtime, quality issues with specific parts or machines – requiring more complete data to implement root cause analysis.

Many shops spend more time in machine setup than machining parts. The right condition monitoring software can track key parameters such as setup times, part loading and unloading time, and time spent on fixture changes, tool changes, or blow-off cycles.

One pain point for aerospace manufacturers is reducing cost-per-part. Without overarching communication between different machines, it’s impossible to monitor which points of production are making cost-per-part reduction difficult. It could be caused by variables including excess waste, product defects, or poor quality management practices. Adequate connectivity can help a manufacturer identify problems and make adaptations.

Today’s machine condition monitoring platforms can track and zero-in on data points that are more accurate, allowing you to make operational changes or business decisions more rapidly and with confidence you’ll achieve optimization goals.

Classify the data

In lean manufacturing continuous improvement processes, the first step is asking shop floor operators to identify persistent issues. Condition monitoring software can collect dynamic input from ongoing operator input to identify patterns. We can also ask digitally connected machines what’s happening, eliminating the need to manually monitor machines.

An important step is classifying data the system captures into categories that drive meaningful business decisions. You could focus on a machine or group of machines to discover how much time was spent cutting a specific part. Further analysis could identify which part caused the most unscheduled machine downtime.

Comparing optimized machines running that part with other machines can enable decisions around how different machines are scheduled, improve maintenance or replace tooling on machines with too much downtime, or determine additional capacity needed.

Choose providers with machining expertise

A range of condition monitoring software platforms are available; however, these general packages may require significant customization to generate actionable, real-time data. That’s why there are advantages to selecting a platform created by a manufacturer having in-depth machining expertise – one that’s already configured to capture, organize, and analyze machining data.

Manufacturing companies such as Sandvik Coromant that supply high-quality condition monitoring software also offer valuable technical support remotely and on-site. They can also continue the partnership, working together on improvement efforts, and offering their expertise in manufacturing knowledge for support through the process.

Sandvik Coromant found customers using CoroPlus Machining Insights platform realized quick returns on their investments, often in less than six months. This payback typically comes from the initial improvement efforts of reducing waste in their processes from the insights the software can offer. This is mainly because the average shop typically, and most often unknowingly, operates at a 30% to 40% utilization rate of its assets.

Dashboards for easier visualization

A key factor when selecting condition monitoring platforms is design and ease of use of the operator dashboards that provide critical visual metrics and alerts on the key indicators established to assess machining performance. Real-time data about machine stop causes, faults, and alarm reasons prevents issues from becoming serious problems. Dashboards also provide insights into opportunities to improve resource planning, asset management, and cost and time prediction.

The best platforms support drag-and-drop programming for easier setup and configurability within the system to help address needs and workflows. They should also support tablet and smartphone displays so operators can receive alerts and respond to issues without having to go to specific machines.

Cloud-based systems

Cloud-based condition monitoring platforms offer advantages compared to systems installed and networked on servers. Recent machine tools have high-speed Ethernet-based interfaces that make it easier to connect tools and production floors to external networks with access to cloud-based applications.

The complex, real-time analysis and reporting provided by these condition monitoring programs use advanced algorithms and significant processing power the cloud can support better than on-premise servers. Also, cloud-based solutions offer the ability, from anywhere in the world, to see the current state of the machines and how those machines have performed since the first day they were connected.

With a cloud-based solution, data is protected behind firewalls, with leading condition monitoring providers offering systems typically complying with strict government security protocols covering sensitive defense and intelligence applications. A cloud-based application is managed and kept up to date by the platform provider so machining company information technology (IT) departments aren’t required to maintain expertise in managing and updating the application.

A further advantage is cloud-based condition monitoring programs can be rapidly implemented with the same system supporting multiple locations worldwide. They also can allow for remote support capabilities, enabling technicians to collaborate with end users on a specific machine issue and implement the solution over the network.

Embracing the edge

Looking to the future, it’s likely connected machining will evolve into a hybrid environment using cloud-based analytics with edge computing. Edge computing describes data capture, processing, and analysis taking place on a device – on the edge of the process – in real-time. Unlike cloud-based methods, which typically collate data from several machines at a centralized store, edge computing is distributed, bringing computation and machine data storage closer to data sources. This can improve response time and save bandwidth.

In aerospace manufacturing, conducting analytics at an individual device can save data processing costs and resources. However, edge computing isn’t an alternative to cloud-based methods – each is making the other’s job easier. Computing environments associated with IIoT take various forms, from an industrial PC remote server to a gateway or back-office infrastructure. These tools are essential to support edge computing because they’re distributed away from the core, or the cloud. They can perform tasks that don’t necessitate analytics at the edge.

In aerospace manufacturing, edge analytics might be used in boring large metal components, where one small mistake can be incredibly costly. In this case, Sandvik Coromant’s CoroPlus edge computing offering deploys intelligent tools and sensors onto one piece of equipment, and a machine-integrated version of Sandvik Coromant’s Silent Tools Plus, with CoroPlus Connected, uses data generated at the cutting zone to identify potential problems. Automated cutting actions can then be applied to avoid costly mistakes.

This methodology ensures decentralized problems are identified immediately – at the edge – while data from wider machining processes are correlated to be analyzed separately.

What’s next for digitalization?

While innovations in materials science, AM, and robotics continue to advance the sector, it’s crucial that manufacturers don’t lose sight of the tools binding these technologies together. At Sandvik Coromant, we believe the most crucial technologies in aerospace’s IIoT toolkit are the ones that provide insight to human operators in the form of easy-to-use machining data.

Sandvik Coromant

About the author: Cory Carpenter is digital machining specialist for Sandvik Coromant.