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2. Relevant Parameters Identification in Traditional & Stretch and Blow Thermoplastics Injection Molding

Author

Lopes Silva, Bruno Miguel, Marques, Rubén, Santos, Tiago, Sousa, João, and Alenyà, Guillem

Abstract

Trabajo presentado en la 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), celebrada en Chemnitz (Alemania), del 15 al 17 de junio de 2022, Prediction of the quality of plastic molded parts based on process parameters is gaining a lot of attention. The complexity of the injection molding process lies in the high number of parameters that intervene throughout the process. From the large set of process parameters, the key issue is to find the relevant ones that should be used to correctly classify the produced parts. In this work we compared algorithms from the three main families of feature selection methods: filter, wrapper, and embedded. Additionally, a hybrid approach was also evaluated that takes into account not only the supervised contribution but also an unsupervised method in an effort to evaluate each feature without the influence of the target label. Validation involves three different machines from three different brands working with three different processes and materials. A novelty in this paper is the inclusion of two injection molding processes: traditional injection molding and stretch blow injection molding. All datasets were provided by a plastic injection company in Portugal. The results show that despite the variability in materials and machines, there is a group of variables that should be monitored in all of these processes (including the two types of molding techniques). In addition to the process parameters, adding the ambiance temperature around the machine improves the representation of the process through the data.

Published
2022

5. Hyperspectral Imaging System for Marine Litter Detection

Author

Freitas, Sara, primary, Silva, Hugo, additional, Almeida, Carlos, additional, Viegas, Diana, additional, Amaral, Alexandre, additional, Santos, Tiago, additional, Dias, Andre, additional, Jorge, Pedro A. S., additional, Pham, Christopher K., additional, Moutinho, Jose, additional, and Silva, Eduardo, additional

Published
2021
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22. Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model.

Author

Santos, Tiago, Schrunner, Stefan, Geiger, Bernhard C., Pfeiler, Olivia, Zernig, Anja, Kaestner, Andre, and Kern, Roman

Subjects
*IMAGE processing, *FEATURE extraction, *SEMICONDUCTOR manufacturing, *IMAGE reconstruction, *MANUFACTURING processes, *SEMICONDUCTOR devices
Abstract

Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results. [ABSTRACT FROM AUTHOR]

Published
2019
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30. A New Nested Benders Decomposition Strategy for Parallel Processing Applied to the Hydrothermal Scheduling Problem.

Author

Santos, Tiago Norbiato, Diniz, Andre Luiz, and Borges, Carmen Lucia Tancredo

Abstract

Several optimization strategies are suitable for parallel processing, such as mathematical-programming based decomposition strategies, like Lagrangian relaxation and two-stage Benders decomposition, and evolutionary programming-like algorithms. In such cases, most subproblems can be solved independently by different processors. However, parallel processing is not suitable for some problems solved by nested Benders decomposition, due to the hierarchy between subproblems related to different time steps. In particular, for the deterministic case, parallel programming has been restricted to thread processing in lower-level machine computation and optimization solver codes. This paper proposes a new nested Benders decomposition strategy that is suitable for parallel processing, where time-coupled stages are solved simultaneously and an alternative procedure is employed to share initial conditions for the next stages as well as Benders cuts for the previous stages. The methodology is applied to the deterministic short term hydrothermal scheduling problem for the real large-scale Brazilian system, where the advantages of the proposed approach become more evident. In order to show the applicability of the method in low-budget and small parallel environments, the case study was processed with up to 24 process on multi-core computers. [ABSTRACT FROM PUBLISHER]

Published
2017
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41. A Dynamic Piecewise Linear Model for DC Transmission Losses in Optimal Scheduling Problems.

Author

dos Santos, Tiago Norbiato and Diniz, Andre Luiz

Subjects
*DIRECT currents, *ELECTRIC lines, *POWER transmission, *SCHEDULING, *PROBLEM solving, *APPROXIMATION theory, *ITERATIVE methods (Mathematics), *LINEAR programming
Abstract

This paper proposes a dynamic piecewise linear model to represent dc transmission losses in optimal scheduling problems. An iterative procedure is proposed, where the linear cuts to approximate quadratic losses in each transmission line are adjusted iteratively as the optimization problem is solved. Applications of this approach to the network constrained short-term hydrothermal scheduling problem and to static dc optimal power flow problems yield a higher accuracy in representing line transmission losses as compared to other approaches, such as a priori iterative estimation, static piecewise liner model and successive linear programming. Study cases for a large-scale system also show reasonable results regarding CPU times. [ABSTRACT FROM PUBLISHER]

Published
2011
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42. A New Multiperiod Stage Definition for the Multistage Benders Decomposition Approach Applied to Hydrothermal Scheduling.

Author

dos Santos, Tiago Norbiato and Diniz, Andre Luiz

Subjects
*HYDROTHERMAL electric power systems, *ELECTRIC power production, *INTERCONNECTED power systems, *PRODUCTION scheduling, *ELECTRIC power systems, *ELECTRIC power distribution, *ELECTRIC utilities
Abstract

Multistage Benders decomposition (MSBD), also known as dual dynamic programming, is a well-established technique to solve hydrothermal scheduling problems, especially for predominantly hydro systems. The MSBD methodology solves the problem by iterative forward and backward recursions, approximating the cost-to-go function for each stage by Benders cuts, as opposed to traditional dynamic programming approaches that discretize the state space at each time-step. The classical definition of the stages in the MSBD approach is to assign a stage for each time period. In this paper, we propose a new strategy to decompose the problem, where each stage comprises all variables and constraints of several time periods. Numerical results of the application of this strategy to the short- term hydrothermal scheduling problem confirm the advantages of this strategy in terms of CPU time, as compared to the classical stage definition approach. We show that there is an "optimal aggregation factor," which best balances the trade-off between solving a "larger number of shorter subproblems" and solving a "smaller number of larger subproblems." The primal and dual solutions related to different aggregation factors are also compared, and the stability of the results is confirmed. Extensions of the proposed strategy to stochastic problems are discussed. [ABSTRACT FROM AUTHOR]

Published
2009
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