Smart manufacturing involves the use of a variety of automation solutions, such as robotics, machines with embedded software, and advanced sensors collecting vast quantities of data. Efficient control of a complex com...
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Smart manufacturing involves the use of a variety of automation solutions, such as robotics, machines with embedded software, and advanced sensors collecting vast quantities of data. Efficient control of a complex composition of such solutions, as well as the analysis of the collected data, are essential for improving the efficiency of the production processes and decision-making. Data analysis and process optimization are enabled through the application of state-of-the-art optimization and machine learning algorithms. However, the efficient use of these algorithms often depends on the careful selection of the parameters, which on its own is a process that requires a high degree of expertise and time. Therefore, another class of algorithms can be applied that is designed to discover the optimal parameter configuration given the specific nature of the manufacturing process and the used algorithm. In this work, we systematically analyze the published literature to discover which parameter selection techniques are used in the context of Industry 4.0, for which processes, and how these benefit from automated parameter selection. Within our literature review, we discover nine relevant publications, most of which concentrate on parameter selection for machine learning algorithms through various numerical optimization and metaheuristic techniques. (C) 2024 The Authors. Published by Elsevier B.V.
In the context of the widespread popularity of electric vehicles, wired charging has various limitations, while wireless charging has become a research hotspot due to its safety in avoiding physical contact, sparks an...
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In this work, the degree of automation of mass transfer devices for processing by-products of ethyl alcohol production is analyzed;in this regard, a number of shortcomings are identified, one of which is the optimal a...
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Derivations of the Kalman filter that ensure optimality with respect to minimum variance typically assume that the process and measurement noise are uncorrelated. However, several works have presented generalized form...
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This paper addresses the trajectory planning problem of a spatial dual-arm robot and proposes a method to minimize the floating pedestal perturbation using a nonlinear planning method. The joint angle of the dual-arm ...
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This paper presents the design and automationcontrol of milk pasteurization process in the dairy industry using a Siemens S7-300 PLC and online monitoring with a Siemens TP 700 comport HMI panel. Pasteurization is th...
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This paper presents a model-free H2/H∞ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm i...
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The feeding axis driven by linear motors experiences nonlinear changes in friction resistance during the feeding process, making high-precision drive control difficult. By combining modeling and experiments, the Prand...
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With the rapid development of new generation technologies, the digital twin (DT) has become a core focus for the solution of assembly process in the fourth industrial evolution. So, based on DT, aiming at the unobserv...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
With the rapid development of new generation technologies, the digital twin (DT) has become a core focus for the solution of assembly process in the fourth industrial evolution. So, based on DT, aiming at the unobservable, unpredictable, and uncontrollable problems during the assembly process of complex products like large solid rocket engines, we propose a digital twin-driven framework for online prediction and control method toward assembly quality for complex products. Under this framework, firstly, the high-fidelity modeling method is presented involving 3D CAD and assembly quality model based on point cloud towards unobservable problems. Secondly, online deviation traceability and quality prediction are introduced to aim at unpredictable problems. Thirdly, intelligent regulation and control mechanism for assembly quality based on DT, AR, deep learning, etc. is developed for uncontrollable problems in assembly process. Finally, the large rocket engine nozzle is taken as a case to verify the entire framework and method effectiveness.
With the rapid expansion of the logistics industry, the bin packing problem (BPP) has become an increasingly popular area of research due to its extensive applications in conveyor belt systems and warehouse scenarios....
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