Titlebook: Innovations in Mechatronics Engineering II; José Machado,Filomena Soares,Olena O. Hrybiuk Conference proceedings 2022 The Editor(s) (if ap

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Algorithmization of Functional-Modular Design of Packaging Equipment Using the Optimization Synthesization synthesis, which can be used to determine the rational layout of the technological machine of arbitrary structural complexity. This study provides a detailed description of the early stages of design. How to systematize and formalize the initial data is shown. The formulation of the optimiza

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Implementation of the SMED Methodology in a CNC Drilling Machine to Improve Its Availability,o 90%, contributing to the increase of the global efficiency of the process, with an OEE of about 85%. The results were positive after the implementation of SMED. Consequently, the availability of the CNC drilling machine has increased, which has increased overall process effectiveness.

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2195-4356 me set, provides engineering researchers and professionals with a timely overview and extensive information on trends and technologies behind the future developments of mechatronics systems in the era of Industry 4.0... .978-3-031-09384-5978-3-031-09385-2Series ISSN 2195-4356 Series E-ISSN 2195-4364

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Signal Processing ,e in the number of devices that generate data. Currently, the concept of Indus-try 4.0 and related technologies facilitate the collection, processing, and use of large amounts of data. For this purpose, the possibility use of the data mining method (wavelet analysis and logistic regression) to devel

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Cork-Rubber Composite Blocks for Vibration Isolation: Determining Mechanical Behaviour Using ANN ane geometry. The development of a new composite requires several iterations before achieving the projects’ requirements. The application of modelling techniques at an early stage could help reduce product development time. As a first step towards the development of new cork rubber composites, an exis

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Deep Neural Networks: A Hybrid Approach Using Box&Jenkins Methodology, process. When dealing with the intrinsic challenges for financial time series, Machine Learning methodologies, in particular Deep Learning, was pointed out as being a promising option. Previous works, highlight the potential of Deep Neural Network architectures, but also their limitations with rega