Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys

Konstantinidis, Fotios K.; Myrillas, Nikolaos; Mouroutsos, Spyridon G.; Koulouriotis, Dimitrios E.; Γαστεράτος, Αντώνιος

doi:10.3390/machines10090746

Cited by 43 publications

(6 citation statements)

References 91 publications

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“…Our approach and embodied AI research, in general, can have great implications in the flourishing field of human-machine collaboration and, more specifically, industrial robotics. According to the flowchart drawn by Konstantinidis et al (2022), it could lead to business opportunities (and we have already seen examples of robot assistant products or pets (Keroglou et al 2023), research opportunities (as we have already seen advancement in RL research that answers the high demands of robotic agents like massive datasets in Deitke et al (2022) and competitions), and lastly, the next human-centered industrial revolution called "Industry 5.0" (that emphasizes human-machine interaction). Othman et al (2016) also shows the use of robots in advanced manufacturing systems that will, no doubt, be benefited by embodied AI research.…”

Section: Problem Statementmentioning

confidence: 99%

Decomposing user-defined tasks in a reinforcement learning setup using TextWorld

Petsanis,

Keroglou,

Ch. Kapoutsis

et al. 2023

Front. Robot. AI

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The current paper proposes a hierarchical reinforcement learning (HRL) method to decompose a complex task into simpler sub-tasks and leverage those to improve the training of an autonomous agent in a simulated environment. For practical reasons (i.e., illustrating purposes, easy implementation, user-friendly interface, and useful functionalities), we employ two Python frameworks called TextWorld and MiniGrid. MiniGrid functions as a 2D simulated representation of the real environment, while TextWorld functions as a high-level abstraction of this simulated environment. Training on this abstraction disentangles manipulation from navigation actions and allows us to design a dense reward function instead of a sparse reward function for the lower-level environment, which, as we show, improves the performance of training. Formal methods are utilized throughout the paper to establish that our algorithm is not prevented from deriving solutions.

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Section: Problem Statementmentioning

confidence: 99%

Decomposing user-defined tasks in a reinforcement learning setup using TextWorld

Petsanis,

Keroglou,

Ch. Kapoutsis

et al. 2023

Front. Robot. AI

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“…Data-driven decision systems help humans and robots optimize production scheduling, running equipment, forecasting breakdowns, and evaluating industrial performance. Moreover, the collaboration between humans and machines allows for a faultless environment where the versatility of humans and the precision of machines may achieve production performance that is free from errors and optimized [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Kristiani,

Wang,

Liu

et al. 2024

Sensors

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This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

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“…The successful implementation of the Industry 4.0 concept requires taking actions enabling the implementation of new methods of production and operation of enterprises characterized by three main features [ 10 , 11 , 12 , 13 ]: horizontal integration along the value chain network (e.g., free flow of information, finance, and materials from the customer through the manufacturer to the supplier, and vice versa); vertical integration (network production systems), related to the integration of hierarchical subsystems within the company (from the operational level with actuators or sensors, through the control level, to the production management level) in order to enable the creation of a highly flexible and reconfigurable production system; end-to-end engineering integration, related integration across the entire value chain to support product development and customization (from design to service). …”

Section: Introductionmentioning

confidence: 99%

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

Krenczyk

Paprocka

2023

Materials

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The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

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Assessment of Industry 4.0 for Modern Manufacturing Ecosystem: A Systematic Survey of Surveys

Cited by 43 publications

References 91 publications

Decomposing user-defined tasks in a reinforcement learning setup using TextWorld

Decomposing user-defined tasks in a reinforcement learning setup using TextWorld

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

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