Browsing by Author "Ahmad, Bilal"
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- Decision support based on digital twin simulation: a case studyPublication . Pires, Flávia; Souza, Matheus; Ahmad, Bilal; Leitão, PauloThe significance of Digital Twins is considered vital in the reshaping of the manufacturing field with the emergence of the fourth industrial revolution. The potential of applying the Digital Twin technology is being studied extensively as a key enabler of engineering cyber-physical systems. However, it is still in its infancy, and only a few scientific papers are describing its applicability in case-studies, prototypes or industrial systems. Bearing this in mind, this paper presents a comprehensive overview of Digital Twins in the manufacturing domain and defines a conceptual architecture that considers simulation capabilities to support the optimisation of production processes. The designed approach is applied to a proof-of-concept case study that considers a flexible production cell and uses the simulation of the system to dynamically support decision making to optimise the production processes when changes occur in the real production system.
- Digital twin based what-if simulation for energy managementPublication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, PauloThe 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.
- Recommendation system using reinforcement learning for what-If simulation in digital twinPublication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, PauloThe 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.
- Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systemsPublication . Pires, Flávia; Leitão, Paulo; Moreira, António Paulo G. M.; Ahmad, BilalDigital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.
- Trust model experimental validation to improve the digital twin recommendation systemPublication . Pires, Flávia; Ahmad, Bilal; Moreira, António Paulo G. M.; Leitão, PauloIn 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.