From May issue, NZ Manufacturer magazine  www.nzmanufacturer.co.nz

 

-Adam Sarman, Senior Partner, Dsifer

In an industry in which flexibility, agility and innovation are no longer competitive advantages, but tickets to the game, the ability to make responsive, agile decisions, predict future requirements and innovate without impacting operations are critical.

In this dynamic industry context, digital twin technology has emerged as an important but underutilised technology.

Digital twins are software models that represent the attributes and operating behaviour of physical assets, product designs and/or processes. They support better decision making by simulating how these applications behave given certain inputs.

A digital twin’s ability to enable progressive learning and capture tacit knowledge provides a key, differentiating benefit: it stores and structures information in a way engineers and operators can understand.

Digital twins, are reshaping how products are designed, manufactured, and optimised across all process and design-drive industries.

Three key applications of digital twin technologies are process optimisation through simulation, product design simulation, factory/operation design and predictive maintenance.

Process Optimisation: Enhancing Efficiency and Accuracy

Process simulation lies at the heart of manufacturing and supply-chain optimisation, and digital twins provide an unparalleled platform for achieving this.

By creating virtual replicas of production processes, manufacturers can simulate and analyse operations in real-time, enabling them to identify bottlenecks, optimise workflows, and improve efficiency.

For instance, in automotive manufacturing, digital twins are used to simulate assembly lines, allowing manufacturers to test different layouts and production scenarios before implementation.

Moreover, by integrating IoT sensors and data analytics, manufacturers can collect real-time data from equipment and processes, feeding it into the digital twin for predictive maintenance and continuous improvement.

Product Design: Accelerating Innovation and Iteration

Digital twins play a crucial role in product design by enabling engineers to create virtual prototypes and simulate their performance under various conditions.

This allows for rapid iteration and refinement of designs before physical prototypes are built, saving time and resources while ensuring optimal product performance.

In industries like aerospace, digital twins are used to simulate the behaviour of aircraft components, such as wings or engine parts, under different operating conditions and loads.

Engineers can analyse the data generated by these simulations to optimise designs for strength, durability, and fuel efficiency. This iterative approach to product design not only speeds up the development process but also results in more robust and cost-effective products.

Factory/Operation Design: Optimising Layouts for Efficiency

The layout of a manufacturing facility can significantly impact productivity, workflow, and resource utilisation. Digital twins offer a powerful tool for optimising factory layouts by providing virtual replicas that can be analysed and optimised for maximum efficiency.

Manufacturers can use digital twins to simulate different factory configurations, equipment placements, and material flow paths to identify the most efficient layout. This includes considerations such as minimising material handling distances, reducing congestion, and optimising workstation layouts for ergonomic efficiency.

By optimising factory layouts with digital twins, manufacturers can improve throughput, reduce lead times, and lower operating costs.

OEE Optimisation through Predictive Maintenance

Downtime due to equipment failure can be costly for manufacturers. Digital twin technology offers a solution by enabling predictive maintenance. By creating virtual representations of machinery and equipment, manufacturers can monitor their performance in real-time and predict when maintenance is needed before failures occur.

This proactive approach minimises unplanned downtime, reduces maintenance costs, and extends the lifespan of assets. For example, in the aerospace industry, digital twins are used to monitor aircraft engines, allowing airlines to schedule maintenance based on actual usage and performance data.

Digital twin technology offers tremendous potential for manufacturing organisations to optimise processes, improve efficiency, and drive innovation. However, successful implementation requires careful planning and execution.

Here are the steps a manufacturing organisation should follow to implement digital twin technology effectively:

1. Define Objectives and Scope

Start by clearly defining the objectives of implementing digital twin technology. Determine which areas of your manufacturing operations will benefit most from digital twins, whether it’s product design, production optimisation, predictive maintenance, or supply chain management.

Define the scope of the project to ensure it aligns with your organisation’s goals and resources.

2. Assess Readiness

Evaluate your organisation’s readiness for digital twin implementation. This includes assessing existing infrastructure, data management capabilities, and workforce skills. Identify any gaps that need to be addressed to ensure a smooth implementation process.

3. Choose the Right Platform

Select a digital twin platform that best fits your organisation’s needs and objectives. Consider factors such as scalability, compatibility with existing systems, ease of integration, and support for advanced analytics.

4. Collect and Prepare Data

Gather the necessary data to create digital twins of your manufacturing processes, equipment, and products. This includes sensor data, CAD models, historical performance data, and other relevant information.

Ensure the data is accurate, complete, and properly formatted for use in the digital twin environment.

5. Build and Deploy Digital Twins

Create virtual replicas of your manufacturing assets and processes using the collected data. This involves modelling the physical characteristics, behaviour, and interactions of the assets within the digital twin environment.

Deploy the digital twins to simulate real-world scenarios and test different configurations and optimisations.

6. Monitor and Optimise

Continuously monitor the performance of your digital twins and use the insights gained to optimise processes and improve efficiency. This may involve adjusting parameters, fine-tuning algorithms, or implementing predictive maintenance strategies based on real-time data analysis.

7. Train and Engage Employees

Ensure that your workforce is trained and equipped to leverage digital twin technology effectively. Provide training on how to use and interpret data from digital twins, and foster a culture of innovation and continuous improvement within your organisation.

Conclusion: Paving the Way for Manufacturing Excellence

In conclusion, digital twin technology is transforming the manufacturing landscape by revolutionising the way process, product and assets are optimised through real-time data.

By harnessing the power of virtual replicas, manufacturers can achieve higher levels of efficiency, innovation, and competitiveness.

. As this technology continues to evolve, it will undoubtedly play an increasingly vital role in shaping the future of manufacturing across industries worldwide.

Embracing digital twins is not just about staying ahead of the curve; it’s about unlocking the full potential of manufacturing in the digital age.