An Intelligent Decision Support Approach for Industrial Digital Twin

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Trust model experimental validation to improve the digital twin recommendation system

Publication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, Paulo

In the manufacturing domain, the digital twin has become an emerging concept for decision-making through the integration of what-if simulation capabilities. In such systems, the processing of the entire space of alternative solutions is very time-consuming; recommendation systems are used to solve this; however, these suffer from several problems, namely data sparsity and cold-start. The application of trust-based models can mitigate these problems, particularly the cold-start problems, by providing valuable background for the recommendation system. This paper presents the implementation and experimental validation of a trust-based model for improving the digital twin based what-if simulation recommendation system, addressing the cold-start problems. The proposed trust model was applied in an assembly line case study to recommend the best configurations for the optimal number of AGVs (Autonomous Guided Vehicles). The results show that applying the trust-based model with similarity metrics improved the mitigation of the cold-start problem.

2022Conference paper

Restricted access

Recommendation system using reinforcement learning for what-If simulation in digital twin

Publication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, Paulo

The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.

2021Conference paper

Restricted access

Quo vadis industry 4.0? Position, trends, and challenges

Publication . Leitão, Paulo; Pires, Flávia; Karnouskos, Stamatis; Colombo, Armando W.

Industry 4.0 vision and its mandated digital transformation are radically reshaping the way business is carried out and the way overall industrial processes and collaborations are operating. In this work, the objective is to analyze the current level of adoption of Industry 4.0, via the footprint available in industrial and academic research works. The analysis performed reveals insights on how Industry 4.0 has impacted and is still influencing research and innovation in industrial systems, services, and business approaches. It also reveals pertinent trends on key enabling features, technologies and challenges associated with this 4th industrial revolution, mainly focusing on the pathways for wider industrial adoption of Industry 4.0-compliant technologies and solutions.

2020Journal article

Open access

Digital twin experiments focusing virtualisation, connectivity and real-time monitoring

Publication . Pires, Flávia; Melo, Victória; Almeida, João; Leitão, Paulo

Industry 4.0 is re-shaping the manufacturing world, and amongst the several associated emerging methods and technologies, Digital Twin is becoming a popular approach both in industry and academia. However, the lack of knowledge about the characteristics, functionalities, best practices and benefits that it can provide, especially for small and medium enterprises, constraints its wider adoption. The use of real applications and demonstrators can contribute to exposing stakeholders to these new and innovative technologies and approaches, showing the applicability of Digital Twins. This paper presents several experiments in implementing Digital Twin for automation scenarios, considering different technologies and functionalities, namely in terms of virtualisation, connectivity and monitoring. Lessons learnt and challenges are also provided as a result of the experimental implementations.

2020Conference paper

Open access

Digital twin based what-if simulation for energy management

Publication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, Paulo

The manufacturing sector is one of the largest energy consumers in the industrial world, being the energy consumption by the shop-floor equipment, e.g., robots, machines and AGVs (Autonomous Guided Vehicles), a major issue. The combination of energy-efficient technologies with intelligent and digital technologies can reduce energy consumption. The application of the digital twin concept in the energy efficiency field is a promising research topic, taking advantage of the Industry 4.0 technological developments. This paper presents a digital twin architecture for energy optimisation in manufacturing systems, particularly based on a what-if simulation model. The applicability of the proposed what-if simulation model within the digital twin is presented to promote the efficient energy management of AGVs in a battery pack assembly line case study.

2021Conference paper

Open access