Here we look at how engineers and manufacturers can harness the transformative power of digital twin technology?
Delays and cost overruns are the norm in manufacturing, in fact, according to research firm Accenture, they are experienced by 95% of major projects with a budget over US$1bn. Similarly, only 25% of these projects are completed within 10% of the original deadlines, and just 31% come within 10% of the cost baselines.
Accenture attributes the issue to missing or incomplete engineering data, a lack of project transparency, and poor communication amongst teams. Clearly, eliminating these issues is a widespread challenge, but one that a data-centric approach can help resolve.
Twin technology is one way in which plants can adopt such an approach since it provides engineers and managers with access to a centralised, collaborative platform that can help them successfully manage a project life cycle and delivery in a timely manner.
What is digital twin technology?
A digital twin, also known as a digital replica, is a virtual copy of a real-world component in the manufacturing process. The computer model replica uses data inputs from the physical part and mirrors its status, functionality, and/or interaction with other devices.
The technology is currently being used in sectors such as automotives, oil and gas agriculture, aerospace, consumer goods and healthcare.
Physical assets
This technology creates virtual replicas of physical assets allowing manufacturers to analyse and optimise manufacturing processes in a virtual environment. This helps them identify bottlenecks, test alternative strategies, and optimise efficiencies before implementing changes on the shop floor.
Production lines can be assessed for potential failures and maintenance schedules can be created that suit the age or type of machine, thereby reducing downtime and improving productivity.
Twin technology also allows for the exploration of different scenarios, helping manufacturers configure their machinery and collateral in the best possible way. This fine tuning of processes will reduce production errors and waste and potentially lead to significant cost savings.
Digital twins help with Product design and development
Digital twin technology can also revolutionise product design and development processes. Manufacturers can create virtual replicas of these, allowing them to simulate and test various design iterations before investing in physical prototypes. This can lead to rapid prototyping, accelerating the product development lifecycle.
Manufacturers can also analyse product behaviour under different conditions to ensure optimal performance and reliability. Twin technology can help manufacturers discover potential design flaws and validate engineering assumptions based on real-time data helping to enhance the quality of products while reducing costly design errors.
Furthermore, the technology can help improve collaboration between teams involved in product development since designers, engineers and stakeholders can all access and interact with the virtual replica, this helps them grow their understanding of their colleagues by providing cross departmental insight. This streamlined communication helps to align efforts and ensure that the final product meets customer expectations.
Maintenance and performance management
Twin technology also plays a key role in predictive maintenance. Integrating real-time data from sensors and IoT devices can help manufacturers monitor the health and performance of their physical assets in real time, thereby reducing downtime and extending the life of their equipment. Being able to predict or prevent breakdown of equipment enhances operational efficiency and reduces maintenance costs. Automation technology specialist Beckhoff’s twin technology, TwinCAT, is a solution that provides a platform for designing modelling and implementing digital twin models.
As a lead engineer from Beckhoff explains, the technology offers a maintenance approach that allows manufacturers to detect potential failures or anomalies early on, minimising unplanned downtime. By leveraging the data-driven insights provided by digital twins, manufacturers can optimise maintenance schedules, improve asset performance, and extend the lifespan of their equipment. The ability to predict and prevent breakdowns provides significant savings for manufacturers.
Embracing digital twin technology is no longer an option, but a necessity for manufacturers aiming to thrive in the competitive market. By leveraging its power, manufacturers can achieve reduced time-to-market, enhanced product performance, significant cost savings and a culture of continuous improvement and innovation. The transformative capabilities of digital twin technology are reshaping the manufacturing landscape, and empowering them to stay ahead of the curve. Several large companies have adopted digital twin technology in recent times. These include BP at its Azeri Central EAST (ACE) oil and gas platform in Azerbaijan. The company adopted a technology from industrial software specialist Aveva that aggregated multiple information sets into a single connected environment. The team integrated laser scanning to create a 3D model that correlated with the ‘as-built’ engineering data providing users with cloud-based access to accurate information from their web browsers – without the need for data management.
Similarly, Wood, a global EPC leader in consulting and engineering for energy and material markets also uses technology from Aveva to develop digital twin strategies for greenfield and brownfield capital projects. The company created a life cycle representation of assets during the building phase and integrated multiple customer systems of record from its customers, including real-time and contextualised data, asset information management, predictive analytics, and performance benchmarks.
Conclusion
The careful maintenance and update of data combined with the adoption of twin technology such as Aveva’s technology or the TwinCAT solution from Beckhoff promises to alleviate several major bug bears of the process industry, thereby allowing clients to manage their product lifecycle and meet their delivery deadlines on time.
Managing data
1). Look for vendor-agnostic data solutions that can enable seamless integration and collaboration
between providers and partners.
2). Take an iterative approach. Deploy the technology where the digital twin is most valuable and the
scale to other areas of the business.
3) Collect available asset data from across the life cycle and modify those datasets as conditions
change. A great digital twin is underpinned by great contextualised dat