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Weinert A, Tormey D, O’Hara C, McAfee M. Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies. SENSORS (BASEL, SWITZERLAND) 2023; 23:2313. [PMID: 36850913 PMCID: PMC9966701 DOI: 10.3390/s23042313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.
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Affiliation(s)
- Albert Weinert
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - David Tormey
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Christopher O’Hara
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Marion McAfee
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
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Zhang Y, Tu Z, Zhao W, He L. Design of emotional branding communication model based on system dynamics in social media environment and its influence on new product sales. Front Psychol 2022; 13:959986. [PMID: 35983199 PMCID: PMC9379138 DOI: 10.3389/fpsyg.2022.959986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
In the current social media environment, emotional branding communication has become a common marketing tool for brand owners, and therefore it has become particularly important and urgent to study it. Based on the perspective of brand equity theory, combined with the new characteristics of marketing communication in the social media environment, this paper constructed an emotional branding communication model in the social media environment. The system dynamics (SD) method was used to simulate and analyze the new product marketing system to assess whether it could stir the emotional needs of the consumers and resonate within their hearts. This paper discusses the asymmetric communication of different brands regarding the same commodity to determine the impact of this exchange mechanism, that is, only the weak brands in the market initially adopt marketing methods, while the strong brands do not participate in social marketing activities. It was found that the influence of marketing frequency and marketing intensity on symmetric and asymmetric communications was different. In the face of different types of competitors, the marketing strategy of weak brands needs emphasis. Through unit consistency test, structure verification test, effectiveness, and rationality test, it was proven that the emotional branding communication model and new product sales interaction simulation model established in this paper were reasonable and effective.
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Ali W, Khan WU, Raja MAZ, He Y, Li Y. Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target. ENTROPY 2021; 23:e23050550. [PMID: 33947058 PMCID: PMC8146196 DOI: 10.3390/e23050550] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/24/2021] [Accepted: 04/27/2021] [Indexed: 12/01/2022]
Abstract
In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.
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Affiliation(s)
- Wasiq Ali
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; (W.A.); (Y.L.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Wasim Ullah Khan
- School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
- Correspondence: (W.U.K.); (Y.H.)
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan;
| | - Yigang He
- School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
- Correspondence: (W.U.K.); (Y.H.)
| | - Yaan Li
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; (W.A.); (Y.L.)
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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. SENSORS 2021; 21:s21062085. [PMID: 33809743 PMCID: PMC8002332 DOI: 10.3390/s21062085] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/05/2021] [Accepted: 03/10/2021] [Indexed: 11/23/2022]
Abstract
State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.
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Barzegar V, Laflamme S, Hu C, Dodson J. Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series. SENSORS (BASEL, SWITZERLAND) 2021; 21:1954. [PMID: 33802233 PMCID: PMC8001144 DOI: 10.3390/s21061954] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 11/17/2022]
Abstract
Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm's promise in real-time high-rate applications.
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Affiliation(s)
- Vahid Barzegar
- Department of Civil, Construction, and Environmental Engineering, Iowa State University, 813 Bissell Road, Ames, IA 50011, USA;
| | - Simon Laflamme
- Department of Civil, Construction, and Environmental Engineering, Iowa State University, 813 Bissell Road, Ames, IA 50011, USA;
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
| | - Chao Hu
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;
- Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
| | - Jacob Dodson
- Air Force Research Laboratory, Munitions Directorate, Fuzes Branch, Eglin Air Force Base, FL 32542, USA;
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A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems. VIBRATION 2020. [DOI: 10.3390/vibration3030016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
High-rate dynamic systems are defined as engineering systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms. The implementation of feedback decision mechanisms in high-rate systems could improve their operations and safety, and even be critical to their deployment. However, these systems are characterized by large uncertainties, high non-stationarities, and unmodeled dynamics, and it follows that the design of real-time state-estimators for such purpose is difficult. In this paper, we compare the promise of five time-frequency representation (TFR) methods at conducting real-time state estimation for high-rate systems, with the objective of providing a path to designing implementable algorithms. In particular, we examine the performance of the short-time Fourier transform (STFT), wavelet transformation (WT), Wigner–Ville distribution (WVD), synchrosqueezed transform (SST), and multi-synchrosqueezed transform (MSST) methods. This study is conducted using experimental data from the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) testbed, consisting of a rapidly moving cart on a cantilever beam that acts as a moving boundary condition. The capability of each method at extracting the beam’s fundamental frequency is evaluated in terms of precision, spectral energy concentration, computation speed, and convergence speed. It is found that both the STFT and WT methods are promising methods due to their fast computation speed, with the WT showing particular promise due to its faster convergence, but at the cost of lower precision on the estimation depending on circumstances.
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Transverse Vibration of Clamped-Pinned-Free Beam with Mass at Free End. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9152996] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Engineering systems undergoing extreme and harsh environments can often times experience rapid damaging effects. In order to minimize loss of economic investment and human lives, structural health monitoring (SHM) of these high-rate systems is being researched. An experimental testbed has been developed to validate SHM methods in a controllable and repeatable laboratory environment. This study applies the Euler-Bernoulli beam theory to this testbed to develop analytical solutions of the system. The transverse vibration of a clamped-pinned-free beam with a point mass at the free end is discussed in detail. Results are derived for varying pin locations and mass values. Eigenvalue plots of the first five modes are presented along with their respective mode shapes. The theoretical calculations are experimentally validated and discussed.
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