Connecting your aerospace production line to the future in three steps

Data is the fuel for smarter manufacturing. This article explores how the aerospace industry can harness the power of data that already exists throughout the manufacturing process to improve production quality and efficiency.

Supporting the aerospace industry with Hexagon solutions

Step 1: Analysing existing data
We’ve all heard about it, but what is big data and how does it impact you in the aerospace manufacturing industry?

Big data is an extremely large set of data that can be structured or unstructured. It is an umbrella term used to describe any data set that is so large or complex that it requires innovative handling for analysis. The insights derived from big data analysis can enhance decision-making. Big data is making a major impact on businesses, not least in aerospace manufacturing.

What does big data mean for aerospace manufacturing?

In manufacturing, big data originates from two general sources. Firstly, unstructured data from external sources such as the internet and media. Secondly, data produced directly from manufacturing processes.

Huge volumes of data are at your fingertips, but it can only be truly put work if it can be properly targeted, collected and analysed.

Instead of ‘what to measure?’ the question often becomes ‘what not to measure?’ Therefore implementing a data-driven process means prioritising your data collection and analysis, and your shop-floor operators must be your first contributors. Both interviewing your operators on recurring issues and analysing existing data to identify some root causes should help your plan for your initial data-driven implementations. In the aerospace industry, which requires immediate problem resolution to maintain quality and productivity, it is essential that big data is structured. Maintaining structure means your data can be effectively and statistically analysed, and automatic reporting and processing can be carried out.

Using big data to advance manufacturing

Structuring big data means you can effectively monitor it. All processes that need viewing and measuring can be displayed in a dashboard, making quality checks more efficient.

For example, in an aero-engine blade manufacturing line producing up to 500 blades per day, a small problem anywhere in the manufacturing process could snowball into quality issues in multiple batches of parts and machines. Analysis of big data offers a feasible solution to this.

When implementing processes to use big data, it’s therefore important to contextualise your data in regards of your future analysis. For instance an aero-engine blade machining step can be launched recording the part ID, its batch belonging, the fixturing ID, the operator user name, the processor firmware version, the ambient temperature and other critical information, in order to quickly track for common parameters resulting in potential defects; as well as implementing such contextual information in a digital twin model.

If in this instance we take the meaning of the digital twin as a virtual model of the real factory, appropriate data collection will then allow the visualisation and monitoring of production in real time; simulating the optimal plant reconfiguration, helping to take preventive or corrective decisions to optimise manufacturing processes, and even allowing the commissioning and troubleshooting of machines remotely.

This means two things. The production managers can monitor the quality of results and capability indices, and thereby maintain stability and consistency. Simultaneously, process engineers are alerted if something is abnormal and correct the fault sooner than if they were relying on manpower or manual processes.

A person using a table device with big data charts coming out of the screen

Big data challenges 

Big data is not without its challenges, especially as it is a relatively new and evolving discipline.

Sometimes the sheer size of a data set means that it can’t easily be analysed in detail. Also, variety of data types can present a major challenge. Gaps can arise when you have multiple different systems with no standardised method for incorporating all the collected data.

Security can also pose a challenge when engaging with big data. Some companies opt for a private cloud approach, which enables you to tailor the architecture to suit your needs and gives you the confidence that your data is stored completely within your organisation, enabling you to allow sharing with your suppliers and customers.

Despite such challenges, big data will inevitably grow – as a phenomenon, as a process and as a business activity. For aerospace manufacturers, it will evolve to be a source of business benefits and opportunities that can’t be missed. Utilising big data is the starting point for achieving a more connected and informed system to drive quality and productivity throughout your processes.

Step 2: Implementing the Internet of Things (IoT) – an opportunity for digital manufacturing

IoT gives rise to a host of manufacturing opportunities that can streamline efficiency and boost productivity.

Readily available real-time data brings instant transparency to the entire production process through performance and condition monitoring. It also gives you much faster and more informed insights into new and potential production issues.

By applying analytics to your production data, it is possible to reveal the optimal parameters for production processes so that the inputs and variables can be set for optimal outputs, meaning less wastage and faster production.

For example, monitoring vibrations and temperatures during blade forging and machining steps can already help to detect abnormal parameters, triggering a technician investigation process before parts go out of tolerance or machines go out of service.

An essential early step would be to transition your paper-based records to electronic. At the same time, seek to increase the amount of metrics you collect, imposing more measurements, analysing more results, and implementing controls or inline measurements. This step of capturing and utilising big data is critical in facilitating your move to greater connectivity.

The next step would be to transition data to the cloud and run the process for a few months to gain familiarity with the procedure and reflect on what you learn. Collate and analyse your data, identifying areas for improvement and possible problems. Use the large data volume to identify patterns and common issues more accurately. Could the tool have been changed? How do the discrepancies relate to other environmental data? Is your product quality constant or erratic? The connectivity of IoT can be a gamechanger for addressing the problems at the root of questions like these.


Making data your focus

Focusing on measuring and analysing progress at every step of the process will be beneficial. With IoT connectivity supporting integration of quality management systems and production systems, increased visibility of data will support new levels of efficiency and accuracy.
In the next decade you’re likely to see a boom in device connectivity and real-time data usage in manufacturing. The key to implementing this in the aerospace industry is to use and measure data optimally, by creating the right platforms and allowing the right decisions to be taken at the right time.

As IoT platforms become more commonplace, your attention should shift to data, as other users begin to interact with the production process in a very different way. As this happens, your focus on data, metrics and insights will drive change and support continuous improvement.

Step 3: Increasing automation

At a high-level, the smart factory is a highly digitised manufacturing enterprise that leverages connected systems that maximise data utilisation for unprecedented productivity and continual optimisation.

Central to the smart factory are automated and connected solutions that enhance transparency, collaboration, flexibility and agility. This includes user interface, robotics, sensors, cyber-physical systems, and cloud-based data handling.

Quality control and visual inspection often create bottlenecks in aerospace manufacturing processes. Therefore allowing technicians to set up their inspections tasks quickly and using automated systems to sort parts and perform overnight inspections make significant cycle time reductions.

Additionally, bringing smaller inspection operations earlier in the process can produce major benefits. Examples in the aerospace industry might include checking for surface defects within blade forging cells, quickly checking the leading and trailing edges at machining steps, or simply switching from tactile measurement to non-contact inspection sensors. Small changes like these offer in-production monitoring capabilities that would help aero engine part manufacturers to anticipate process deviation and take appropriate corrective actions to reduce scrap rates and maintain quality throughout production.

Integrated workflows such as these will help drive automation, making aerospace manufacturing lines more responsive and agile, utilising smart decision-making and predictive control.

ENGINE-AERO-ENGINE-TURBINE-BLADE-COOLING-HOLES-INSPECTION_HEROFor the aerospace industry in particular, a smart factory is not confined within the walls of a single plant. The smart factory concept aims to facilitate the interconnectivity of multiple sites globally across suppliers and customers, ensuring process visibility throughout dispersed manufacturing site and even the supply chain, driving continuous improvement.  

By deploying structured real-time data and big data analytics, smart factories more readily detect trends, causes of issues, and support correctional actions. As a consequence, being fast to market – or first to market – with the highest levels of quality possible, is what a true smart factory delivers.

Hexagon offers a toolset that enables you to do this by removing siloes from the manufacturing stages – from product development to first prototype builds, to production, and end of product life. 

Developing a smart factory

One challenge facing the move towards smart factories is mindset. In order to achieve a successful smart factory, it is necessary to break down the siloes within your factory, allowing for multiple user profiles to select and see data recorded throughout the product lifecycle and take appropriate optimisation actions for their duty. For example, quality should be recognised as the goal throughout all processes from concept to completion, not simply as a final ‘quality check’. With an holistic mindset and digitisation of the process you are one step closer to achieving a smart factory. Ultimately, aerospace manufacturers need to think big, start small, and scale fast.

Learn more about how Hexagon is driving the aerospace manufacturing journey toward the smart factory in this video.


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