Advances in Branch-and-Fix methods to solve the Hamiltonian cycle problem in manufacturing optimization

159 p.Esta tesis parte del problema de la optimización de la ruta de la herramienta donde se contribuye con unsistema de soporte para la toma de decisiones que genera rutas óptimas en la tecnología de FabricaciónAditiva. Esta contribución sirve como punto de partida o inspiración para analizar el problema del cicloHamiltoniano (HCP). El HCP consiste en visitar todos los vértices de un grafo dado una única vez odeterminar que dicho ciclo no existe. Muchos de los métodos propuestos en la literatura sirven paragrafos no dirigidos y los que se enfocan en los grafos dirigidos no han sido implementados ni testeados.Uno de los métodos para resolver el problema es el Branch-and-Fix (BF), un método exacto que utiliza latranformación del HCP a un problema continuo. El BF es un algoritmo de ramificación que consiste enconstruir un árbol de decisión donde en cada vértice dos problemas lineales son resueltos. Este método hasido testeado en grafos de tamaño pequeño y por ello, no se ha estudiado en profundidad las limitacionesque puede presentar. Por ello, en esta tesis se proponen cuatro contribuciones metodológicasrelacionadas con el HCP y el BF: 1) mejorar la enficiencia del BF en diferentes aspectos, 2) proponer unmétodo de ramificación global, 3) proponer un método del BF colapsado, 4) extender el HCP a unescenario multi-objetivo y proponer un método para resolverlo

Erregresio logistikoa eta mozketa puntuak: bihotz-gutxiegitasunaren hilkortasunaren azterketa

Bihotz gutxiegitasun desorekatu larria duten pazienteetan hilkortasunaren eta NT-pro-BNP parametro klinikoaren arteko erlazioa aztertu nahi da. Helburua NT-pro-BNP-ren mozketa puntua kalkulatzea delarik. Horretarako erregresio logistiko sinpleko eredu estatistiko eraikiko da eta mozketa puntu optimoak kalkulatzeko teknikak aplikatuko zaizkio ereduari. (Hizkuntza:EUSKARA

Application of advanced regression methods for wear prediction of superalloys

Analytical models able to predict the tool wear can provide companies instruments to optimize the cutting processes. The focus of this thesis is to accomplish a study of the tool wear process in the turning process of superalloys, including its dependence on multiple factors related to the characteristics of the workpiece and machinery used for turning. As a natural extension of this study we propose the application of some statistical and machine learning techniques to address the prediction of the tool wear. Data corresponding to different tests carried out as part of the European project called Himmoval is used. The process of prediction involves selecting features from the variables acquired by different sensors that characterize the machining process. Additionally, several machine learning algorithms are implemented and applied to analyze the data from the wear experiments. Among these algorithms, Gradient Boosting Regressor predominates over the rest of regression methods evaluated.Tecnali

Solving large flexible job shop scheduling instances by generating a diverse set of scheduling policies with deep reinforcement learning

The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied in the literature, and multiple approaches have been proposed within the heuristic, exact, and metaheuristic methods. However, the industry's demand to be able to respond in real-time to disruptive events has generated the necessity to be able to generate new schedules within a few seconds. Among these methods, under this constraint, only dispatching rules (DRs) are capable of generating schedules, even though their quality can be improved. To improve the results, recent methods have been proposed for modeling the FJSSP as a Markov Decision Process (MDP) and employing reinforcement learning to create a policy that generates an optimal solution assigning operations to machines. Nonetheless, there is still room for improvement, particularly in the larger FJSSP instances which are common in real-world scenarios. Therefore, the objective of this paper is to propose a method capable of robustly solving large instances of the FJSSP. To achieve this, we propose a novel way of modeling the FJSSP as an MDP using graph neural networks. We also present two methods to make inference more robust: generating a diverse set of scheduling policies that can be parallelized and limiting them using DRs. We have tested our approach on synthetically generated instances and various public benchmarks and found that our approach outperforms dispatching rules and achieves better results than three other recent deep reinforcement learning methods on larger FJSSP instances

Adaptation of a Branching Algorithm to Solve the Multi-Objective Hamiltonian Cycle Problem

The Hamiltonian cycle problem (HCP) consists of finding a cycle of length N in an N-vertices graph. In this investigation, a graph G is considered with an associated set of matrices, in which each cell in the matrix corresponds to the weight of an arc. Thus, a multi-objective variant of the HCP is addressed and a Pareto set of solutions that minimizes the weights of the arcs for each objective is computed. To solve the HCP problem, the Branch-and-Fix algorithm is employed, a specific branching algorithm that uses the embedding of the problem in a particular stochastic process. To address the multi-objective HCP, the Branch-and-Fix algorithm is extended by computing different Hamiltonian cycles and fathoming the branches of the tree at earlier stages. The introduced anytime algorithm can produce a valid solution at any time of the execution, improving the quality of the Pareto Set as time increases.This project was funded by the ELKARTEK Research Programme of the Basque Government (project KK-2019/00068). This work has been possible thanks to the support of the computing infrastructure of the i2BASQUE academic network. The work of Roberto Santana was funded by the Basque Government (project IT-1244-19), and Spanish Ministry of Economy and Competitiveness MINECO (project TIN2016-78365-R)

Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation

The tool-path problem has been extensively studied in manufacturing technologies, as it has a considerable impact on production time. Additive manufacturing is one of these technologies; it takes time to fabricate parts, so the selection of optimal tool-paths is critical. This research analyzes the tool-path problem in the direct energy deposition technology; it introduces the main processes, and analyzes the characteristics of tool-path problem. It explains the approaches applied in the literature to solve the problem; as these are mainly geometric approximations, they are far from optimal. Based on this analysis, this paper introduces a mathematical framework for direct energy deposition and a novel problem called sequence strategy generation. Finally, it solves the problem using a benchmark for several different parts. The results reveal that the approach can be applied to parts with different characteristics, and the solution to the sequence strategy problem can be used to generate tool-paths.This work was supported in part by the Project HARITIVE under Grant HAZITEK 2017 and in part by the Project ADDISEND under Grant ELKARTEK 2018 through Basque Government, and in part by the European Union Horizon 2020 Research and Innovation Programme under Grant 822064. The work of Roberto Santana was supported in part by IT-1244-19, in part by the ELKARTEK Programmes through Basque Government, and in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-78365-R

A slag prediction model in an electric arc furnace process for special steel production

In the steel industry, there are some parameters that are difficult to measure online due to technical difficulties. In these scenarios, soft-sensors, which are online tools that aim forecasting of certain variables, play an indispensable role for quality control. In this investigation, different soft sensors are developed to address the problem of predicting the slag quantity and composition in an electric arc furnace process. The results provide evidence that the models perform better for simulated data than for real data. They also reveal higher accuracy in predicting the composition of the slag than the measured quantity of the slag.The project leading to this research work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 820670

Data Driven Performance Prediction in Steel Making

This work presents three data-driven models based on process data, to estimate different indicators related to process performance in a steel production process. The generated models allow the optimization of the process parameters to achieve optimal performance and quality levels. A new approach based on ensembles has been developed with feature selection methods and four state-of-the-art regression approximations (random forest, gradient boosting, xgboost and neural networks). The results show that the proposed approach makes the prediction more stable reducing the variance for all cases, even in one case, slightly reducing the bias. Furthermore, from the four machine learning paradigms presented, random forest is the one with the best results in a quantitative way, obtaining a coefficient of determination of 0.98 as a maximum, depending on the target sub-process.This research is supported by the European Union’s Horizon 2020 Research and Innovation Framework Programme [grant agreement No 723661; COCOP; http://www.cocop-spire.eu (accessed on 6 January 2022)]. The authors want to acknowledge the work of the whole COCOP consortium

Wire Arc Additive Manufacturing of Mn4Ni2CrMo Steel: Comparison of Mechanical and Metallographic Properties of PAW and GMAW

Wire arc additive manufacturing, WAAM, is a popular wire-feed additive manufacturing technology that creates components through the deposition of material layer-by-layer. WAAM has become a promising alternative to conventional machining due to its high deposition rate, environmental friendliness and cost competitiveness. In this research work, a comparison is made between two different WAAM technologies, GMAW (gas metal arc welding) and PAW (plasma arc welding). Comparative between processes is centered in the main variations while manufacturing Mn4Ni2CrMo steel walls concerning geometry and process parameters maintaining the same deposition ratio as well as the mechanical and metallographic properties obtained in the walls with both processes, in which the applied energy is significantly different. This study shows that acceptable mechanical characteristics are obtained in both processes compared to the corresponding forging standard for the tested material, values are 23% higher for UTS and 56% for elongation in vertical direction in the PAW process compared to GMAW (no differences in UTS and elongation results for horizontal direction and in Charpy for both directions) and without significant directional effects of the additive manufacturing technology used.This research was funded by BASQUE GOVERNMENT, grant number KK-2018/00115 (ADDISEND, ELKARTEK 2018 programme) and grant number ZE-2017/00038 (HARITIVE, HAZITEK 2017 programme)