## 2 Digital Twin Software Solutions
### General Applications
Another example: [PERSON] et al. [30] explore a digital twin driven by inverse analysis and long short-term memory (LSTM)-based neural networks and apply it to detect aortic abdominal aneurysms using convolutional neural networks (CNN), achieving promising accuracy rates. Frameworks like this can simulate the effects of different treatment options, predict patient responses, and assist medical professionals in making informed decisions. Digital twins also find utility in medical device development, allowing for virtual testing and optimization before physical prototypes are built [31]. This reduces costs, accelerates the development process, and improves the safety and effectiveness of medical devices. Transportation and logistics industries leverage digital twin software solutions to optimize their operations and improve asset management. Digital twins can be used to create virtual replicas of vehicles, warehouses, and transportation networks, enabling real-time monitoring, route optimization, and predictive maintenance [32; 33]. By harnessing data from sensors, global positioning satellites (GPS) systems, and other sources, organizations can gain insights into vehicle performance, fuel efficiency, and maintenance needs, leading to cost savings and improved effectiveness. [PERSON] et al. [34] developed and tested an application framework of the production of a turbofan engine blade using DT-driven smart manufacturing. Their framework is composed of four different layers: the physical layer, the model layer, the information processing layer, and the system layer. These interconnected layers synchronize their respective data to optimize manufacturing parameters. To demonstrate how DT operations are able to address multi-objective optimization problems, the researchers tested their framework with a four-axis CNC machine. The machine was set to transmit data using force sensors, accelerometers, and acoustic sensors during a machining process. Before any milling was completed, the researchers modeled the CNC to replicate it in the virtual space. The digital CNC was virtual commissioning via computer simulation of the production process, followed by a virtual inspection of the digital fan blade. Afterwards, the now virtually tested process is transferred to the physical space, where the physical process data is collected. Their results showed a process time reduction of 26.3% and an increase in machining precision of 23.4%, though the limited sample data does not significantly represent the entire milling process of the CNC. This case study demonstrates the importance of DT for process establishment as well as system health monitoring and fault detection. [PERSON] et al. [35] created a digital twin to predict the crack propagation of an induced rack in a helicopter gearbox planetary gear. They used fractography after crack propagation to estimate the state of crack propagation over time. They theorized adding four vibration sensors to another planetary gear and fed that data to a DT, which could analyze it in real-time using synchronous averaging and provide alarms and notifications based on a calculated health factor. The team tested this concept against the HUMS datasets that they used as a benchmark. This research represents many other digital twins research cycles. It highlights some insights into DT research, including the difficulty of implementing solutions in real-time, and benchmarking against currently accepted solutions.