Digital Twin-Driven Condition-Based Maintenance

The world is witnessing an all-level digitalization that guides the industry and business to a restructuration in order to adapt to the new requirements of the surrounding environment. That change also concerns the labour of the technical professionals and their formation. As a consequence of this deep consciousness-raising, this chapter will investigate and develop simulation models based on the current digitalization. The aim of this chapter is the exposition of a real case development of “digital twin” models framed as part of the condition-based maintenance paradigm to improve real-time assets operation and maintenance. This model contributes by providing real-time results that could turn into a basis for the industrial management decisions and place them in the Industry 4.0 paradigm environment.

Cognitive ability as a mediator of GxSES interactions along the educational life course

A growing body of research asks whether the opportunity to realize the genetic endowment for education varies by parental socio-economic status (GxSES). While the behavioral genetic Scarr-Rowe hypothesis (SRH) suggests stronger, the sociological compensatory advantage hypothesis (CAH) predicts weaker genetic effects for individuals with a higher social origin. Using data from the German TwinLife survey, I estimate biometric twin models to test for a GxSES along the educational life course and whether it can be explained by a moderation of the effect of genes associated with cognitive ability. While for secondary school track no GxSES can be found, there is a GxSES for tertiary enrolment in line with the CAH that is mainly accounted for by social origin differences in the realization of genes associated with cognitive ability. Taken together, the results show a more pronounced GxSES pattern in the later educational life course.

Sensor Selection and Placement for Track Switch Condition Monitoring through Validated Structural Digital Twin Models of Train–Track Interactions

Railway track switches experience high failure rates, which can be reduced by monitoring their structural health. The results obtained from a validated Finite Element (FE) model for train–track switch interaction have been introduced to support sensor selection and placement. For the FE models with nominal and damaged rail profiles, virtual strain sensor measurements have been obtained after converting the true strains to engineering strains. Comparisons for the strains before and after the introduction of the fault have demonstrated greater amplitude for the strains after fault introduction. The highest difference in strain amplitude is in the vertical direction, followed by the longitudinal and lateral directions.

Digital Twin Based Interactive Mechatronics Lab Development for Remote Lab Offering and Evaluation

Mechatronics and control education is supported by laboratory intensive assignments that allow students acquire software and hardware skills to solve real world problems. However, COVID-19 force many schools to switch into remote learning complicating the instruction of practical assignments. This paper presents a novel proposal for interactive remote teaching of the laboratory component of the course ME-142: Mechatronics at the University of California, Merced using Digital Twins (DT) and the flipped classroom methodology. Each lab experience is composed by a set of on-demand supporting materials with the foundations of mechatronics simulation using MATLAB/Simulink to enhance and adapt the learning experience of the students. Once the students acquire advanced simulation skills, a set of Digital Twin models are provided to the students in order to begin their interaction with virtual representations of real systems for identification, analysis, controller design and validation, which are available online for remote access. By the end of the course, students were able not only to gain valuable experience with mechatronic systems but also interact and build advanced modelling techniques as Digital Twin, contributing to compensate the lack of remote hardware interaction.

IN-SITU CALIBRATED DIGITAL PROCESS TWIN MODELS FOR RESOURCE EFFICIENT MANUFACTURING

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of insitu process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

Digital Twin-Enabled Online Battlefield Learning with Random Finite Sets

The digital twin is becoming the most promising emerging technology in the field of unmanned combat and has the potential to innovate future combat styles. Online battlefield learning is one of the key technologies for supporting the successful application of digital twin in unmanned combat. Since there is an urgent need for effective algorithms for online learning the battlefield states in real time, a new random finite set- (RFS-) based algorithm is proposed in the presence of detection uncertainty including clutters, missed detection, and noises. The system architecture and operational mode for implementing the digital twin-enabled online battlefield learning are provided. The unmanned ground vehicle (UGV) is employed as the experimental subject for systematically describing the proposed algorithm. The system architecture for implementing the digital twin-enabled online battlefield learning is firstly given, and its operational mode is also described in detail. The RFS-based digital twin models including the battlefield state model, UGV motion model, and sensor model are designed. The Bayesian inference is adopted, and the probability hypothesis density (PHD) filter is modified to implement the online learning process. At last, a group of experiments are conducted to verify the performance and effectiveness of the proposed algorithm. The research work in this paper will provide a good demonstration of the application of digital twin in unmanned combat.