Rolls-Royce
Canvas Category OEM : Aerospace
Rolls-Royce pioneers cutting-edge technologies that deliver clean, safe and competitive solutions to meet our planet’s vital power needs.
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Rolls-Royce CEO Erginbilgic on Growth, Boeing and Supply
Rolls-Royce announces two additions to FIRST Network of authorized global support providers
Rolls-Royce announces the addition of two companies to its FIRST Network™ of authorized service providers. The FIRST Network is a global listing of authorized facilities providing affordable, reliable support solutions for operators of Rolls-Royce M250 and RR300 engines.
Essential Turbines Inc., joins the First Network as an Authorized Maintenance Repair and Overhaul Center (AMROC). MASCO Services, based in Grapevine, Texas, joins the First Network as an Authorized Repair Facility (ARF). MASCO’s primary focus will be repairs to the RR300 Starter Generator.
Heavy Machinery Meets AI
The process starts with developing what we call fusion strategies, which join what manufacturers do best—creating physical products—with what digital businesses do best: using AI to mine enormous, interconnected data sets for critical insights. Industrial firms will have to figure out how to connect hardware and software, steel and silicon, and humans and machines. In this article we’ll describe four kinds of fusion strategies and how to execute them. They all require reimagining analog products and services as digitally enabled offerings and learning to create new value from the data generated by combined physical and digital assets. Just as crucially, industrial firms will need to partner with other companies to create ecosystems with an unwavering focus on solving customers’ problems.
Deere is one of the companies leading the way in the industrial sector. Until recently, incumbent manufacturers of construction and mining equipment and other heavy machinery didn’t use the most advanced software in their products. That’s no longer true. Today, using generative AI and machine learning, they extract insights and trends from structured and unstructured data—including text, high-resolution 3D images, voice interactions, video, and sound—and create complex designs in seconds.
Another option is to offer performance contracts to guarantee that fusion products will be proactively maintained and updated, ensuring minimal downtime. Rolls-Royce is one company that does this. It guarantees uptime of close to 100% for its commercial airline customers by using AI to monitor and maintain the engines in planes. When something fails on one of its engines, Rolls-Royce knows about it in advance or finds out in real time, which allows it to troubleshoot the problem much more quickly.
Why former collective farm is at the heart of Rolls-Royce’s future
In early June, Rolls-Royce celebrated the 30th anniversary of its site in Dahlewitz near Berlin. What began as a joint venture with BMW in the early 1990s and an investment of 30 million Deutschmarks – then about $18 million – has grown into a key part of the company’s industrial footprint that will in 2023 build upwards of 250 engines for business jet applications.
It is the first time in 54 years and the development of the RB211 that Rolls-Royce has tested a new engine architecture. “It’s a big change because you’re calibrated on how a three-shaft engine works and you have to build up your knowledge of how an new engine will behave differently,” Burr says. Incorporating a suite of new technologies – including carbon-titanium fan blades, Advance3 core, a new combustor, a high-power gearbox – the UltraFan should deliver a 10% fuel-burn improvement over manufacturer’s newest engine, the Trent XWB, or 25% over earlier Trent models.
Rolls-Royce has selected a planetary arrangement for the component, against the star-gear layout seen in Pratt & Whitney’s geared-turbofan narrowbody engines. This, says Burr, enables the higher bypass ratios necessary for future engine designs.
How Rolls-Royce Scales Smart Factory Tech Worldwide
Rolls-Royce began working with PTC to conduct multiple pilots at varying levels of complexity, hoping to discover technologies that could scale to up to a dozen different plants. Deciding which pilots to scale ultimately came down to the opinions of the early adopters.
Successfully implementing the technology at scale comes down to:
- Vendors, business leadership and technology leadership aligning at the highest levels
- Extended technology teams successfully explaining new implementations to management
- Diverse left-brain/right-brain thinking to understand tech deployments holistically and fully engage user communities
Jayasekara says Rolls-Royce is ’ruthless’ when assessing the value of a smart factory technology, like ease and potential breadth of deployment. If the company has hundreds of a certain type of machine used across multiple plants, a smart factory upgrade can improve its performance, that obviously stands a greater chance of attracting funding compared to upgrades for a lesser-used machine.
Rolls-Royce achieves agile delivery by rolling PTC advisors, Rolls-Royce experts and managed service teams into “sprint teams“ that can change the scope of a deployment as they better understand the technology, rather than running smart factory deployments like traditional IT deployments with inflexible milestones.
Databricks Announces Lakehouse for Manufacturing, Empowering the World's Leading Manufacturers to Realize the Full Value of Their Data
Databricks, the lakehouse company, today announced the Databricks Lakehouse for Manufacturing, the first open, enterprise-scale lakehouse platform tailored to manufacturers that unifies data and AI and delivers record-breaking performance for any analytics use case. The sheer volume of tools, systems and architectures required to run a modern manufacturing environment makes secure data sharing and collaboration a challenge at scale, with over 70 percent of data projects stalling at the proof of concept (PoC) stage. Available today, Databricks’ Lakehouse for Manufacturing breaks down these silos and is uniquely designed for manufacturers to access all of their data and make decisions in real-time. Databricks’ Lakehouse for Manufacturing has been adopted by industry-leading organizations like DuPont, Honeywell, Rolls-Royce, Shell and Tata Steel.
The Lakehouse for Manufacturing includes access to packaged use case accelerators that are designed to jumpstart the analytics process and offer a blueprint to help organizations tackle critical, high-value industry challenges.
👷 How smart manufacturing can alter safety standards
An essential component of smart manufacturing is the ability to automate hazardous activities. The evolution of automation in manufacturing has been a game-changer, and it’s why workplace injuries have steadily decreased over the years.
An example of this is at Rolls-Royce, where the adoption of cutting-edge Industry 4.0 technologies is helping to enhance safety and ensure a safer working environment for employees. With the use of 3D visualisation software, employees can have a better understanding of their workplace, including potential hazards. Meanwhile, machine learning technology is assisting in monitoring personal protective equipment (PPE) compliance, and the deployment of robotic arms is taking over tasks that were once considered dangerous, such as furnace operations, thus reducing the need for manual labour.
Rolls-Royce Civil Aerospace keeps its Engines Running on Databricks Lakehouse
COBRA: COntinuum roBot for Remote Applications
Rolls-Royce Finds New-Engine Benefits in Old Test Data
The goal, according to Peter Wehle, head of innovation, research and testing at RRD, is to use this information to reduce new-engine weight and mass, while maintaining structural integrity.
Both parties are hopeful that using ML and AI will significantly reduce the number of sensors needed to obtain present and future data, thereby saving RRD millions of euros annually. According to Mahalingam, the software lets engineers choose the data they want from a data silo, select the algorithms they want to employ and decide whether or not they want to use a neural network to train an ML model.
Wehle notes that the disruptive tool is based on the interaction between a communication endpoint of the engine simulation and neighboring points. It carefully analyzes the effects of loads on physical structures.