DISA

You are welcome to attend Manoranjan Kumar’s final seminar (doctoral 90% control). The seminar will include a presentation and a follow-up discussion with Mirka Kans, Associate Professor at the Department of Mechanical Engineering.

When: Wednesday, December 17, 9:00–12:00
Where: Room D1167 and on Zoom

Title: Digital Twins for Construction Equipment

Abstract: The technology organization at Volvo Construction Equipment (VCE) aims to predict and verify the performance of machines like Wheel loaders (WL) and Articulated haulers (AH) to enhance product development, requirements engineering, and customer service etc. Hence, it is needed to virtualize machines and components using the existing sensors on the machines and infrastructure of the central server. Over the years, virtualization has been achieved through the use of digital twins (DTs) across different industries, but realizing it on dynamically complex machines has its own challenges. It is also an important step in this digital transformation journey.

What is the specific research question to be answered?

This PhD thesis describes and investigates how the digital twin (DT) needs to be developed for machines like AH, and more specifically for WL. Further, a variety of actions are needed to incorporate into the framework of the DT. The framework needs to support different machines and their predictive journey, which can be different based on their usage and where it is being used. 

What are the means and methods used by the authors to answer the stated question?

This DT is the virtual replica of the physical machines that feed the twins (simulation model) with data from sensors and edge-based algorithms. The algorithms are built using a machine learning (ML) model. The algorithms that are implemented into machines are often called machine logs or virtual sensors. Further, a high-fidelity simulation supports the different force-driven maneuvers of different machine operators. A new co-simulation framework has been developed that integrates the operators’ model of the wheel loader (WL) and its interaction with the power source model, i.e., the drive train, the hydraulics, and the material. By using the simulations and physical machine data, visualizations are built to illustrate the results, which support various departments in providing customers with predictive services.

What is the answer to the research question?

The edge-based virtual sensors align well with their accuracy in predicting different failures in the machines. Furthermore, the results show that the co-simulation model aligns well with measurement data, validating the model’s accuracy in different types of machine operator driving. The integration of virtual sensors, machine logs, simulation, and results visualization paves the way for a successful DT of the machines.

Why is the answer important and for whom specifically?

The results are useful for engineers in product development, sales, and the aftermarket to create services and develop the machines for future generations.

How does the answer inspire future research?

The successful validation of the framework also paves the way for future research to enhance the virtual simulation techniques for WL and AH performances with different types of machine operators. It also paves the way and inspires to improve ML algorithms on the edge and, therefore, create services under the shadow of DTs.