Forest-Genetic method to optimize parameter design of multiresponse experiment

[EN] We propose a methodology for the improvement of the parameter design that consists of the combination of Random Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization. The first phase corresponds to the previous preparation of the data set by using normalization functions. In the second phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called it Multivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase, we obtained the optimal combination of parameter levels with the integration of properties of our modelling scheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us to compare and validate the virtues of our methodology versus other proposals involving Artificial Neural Networks (ANN) and Simulated Annealing (SA).[ES] Proponemos una metodología para la mejora del diseño de parámetros que consiste en la combinación de Random Forest (RF) con Algoritmos Genéticos (GA) en 3 fases: normalización, modelización y optimización. La primera fase corresponde a la preparación previa del conjunto de datos mediante funciones de normalización. En la segunda fase, diseñamos un esquema de modelización ajustado a múltiples características de calidad, que hemos llamado Multivariante Random Forest (MRF) para la determinación de la función objetivo. Finalmente, en la tercera fase se obtiene la combinación ¿optima de los niveles de los parámetros mediante la integración de propiedades dadas por nuestro esquema de modelización y las desirabibity functions en el establecimiento del correspondiente GA. Dos casos ilustrativos nos permiten comparar y validar las virtudes de nuestra metodología versus otras propuestas que involucran Redes Neuronales Artificiales (ANN) y Simulated Annealing (SA).Villa Murillo, A.; Carrión García, A.; Sozzi, A. (2020). Forest-Genetic method to optimize parameter design of multiresponse experiment. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial. 23(66):9-25. https://doi.org/10.4114/intartif.vol23iss66pp9-25S925236

Modelling Dental Milling Process with Machine Learning-Based Regression Algorithms

Control of dental milling processes is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective functions, while model of relationship between the parameters and outputs (such as costs or time) is not known. Therefore, the model must be discovered in advance to use it for optimisation. Machine learning algorithms serve this purpose perfectly. There are plethoras of competing methods and the question is which shall be selected. In this paper, we present results of extensive investigation on this question. We evaluated several well-known classical regression algorithms, ensemble approaches and feature selection techniques in order to find the best model for dental milling model

A Hybrid System for Dental Milling Parameters Optimisation

This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a high-precision machining centre with five axes for dental milling purposes

Hybridization of machine learning for advanced manufacturing

Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”, “Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser sostenible. El futuro de muchas compañías depende de su grado de adaptación a los cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes, buscando productos personalizados y específicos con alta calidad, a un bajo coste y no contaminantes. Por todo ello, las empresas industriales implantan innovaciones tecnológicas para conseguirlo. Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada (Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el presente trabajo de investigación, en el que se han concebido y aplicado soluciones inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el propósito de obtener el modelo matemático que representa mejor el sistema real bajo estudio. Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan solucionar problemas complejos del mundo real y de un amplio espectro, manejando aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc. La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos de datos diferentes, se corresponden con: • Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL. • Análisis de datos para el mantenimiento predictivo de una empresa del sector de la automoción, como es la multinacional Grupo Antolin. Adicionalmente se ha colaborado con el grupo de investigación GICAP de la Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que forman parte de esta tesis y otros relacionados. Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos. De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML pueden realizar en el campo de la Fabricación Avanzada

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

AbstractAmong the various types of metal matrix composites, SiC particle-reinforced aluminum matrix composites (SiCp/Al) are finding increasing applications in many industrial fields such as aerospace, automotive, and electronics. However, SiCp/Al composites are considered as difficult-to-cut materials due to the hard ceramic reinforcement, which causes severe machinability degradation by increasing cutting tool wear, cutting force, etc. To improve the machinability of SiCp/Al composites, many techniques including conventional and nonconventional machining processes have been employed. The purpose of this study is to evaluate the machining performance of SiCp/Al composites using conventional machining, i.e., turning, milling, drilling, and grinding, and using nonconventional machining, namely electrical discharge machining (EDM), powder mixed EDM, wire EDM, electrochemical machining, and newly developed high-efficiency machining technologies, e.g., blasting erosion arc machining. This research not only presents an overview of the machining aspects of SiCp/Al composites using various processing technologies but also establishes optimization parameters as reference of industry applications

An intelligent approach for multi-response optimization: a case study of non-traditional machining process

The present work proposes an intelligent approach to solve multi-response optimization problem in electrical discharge machining of AISI D2 using response surface methodology (RSM) combined with optimization techniques. Four process parameters (factors) such as discharge current (Ip), pulse-on-time (Ton), duty factor (τ) and flushing pressure (Fp) and four important responses like material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra) and circularity (r1/r2) of machined component are considered in this study. A Box-Behnken RSM design is used to collect experimental data and develop empirical models relating input parameters and responses. Genetic algorithm (GA), an efficient search technique, is used to obtain the optimal setting for desired responses. It is to be noted that there is no single optimal setting which will produce best performance satisfying all the responses. In industries, to solve such problems, managers frequently depend on their past experience and judgement. Human intervention causes uncertainties present in the decision making process gleaned into solution methodology resulting in inferior solutions. Fuzzy inference system has been a viable option to address multiple response problems considering uncertainties and impreciseness caused during judgement process and experimental data collection. However, choosing right kind of membership functions and development of fuzzy rule base happen to be cumbersome job for the managers. To address this issue, a methodology based on combined neuro-fuzzy system and particle swarm optimization (PSO) is adopted to optimize multiple responses simultaneously. To avoid the conflicting nature of responses, they are first converted to signal-to-noise (S/N) ratio and then normalized. The proposed neuro-fuzzy approach is used to convert the responses into a single equivalent response known as Multi-response Performance Characteristic Index (MPCI). The effect of parameters on MPCI values has been studied in detail and a process model has been developed. Finally, optimal parameter setting is obtained by particle swarm optimization technique. The optimal setting so generated that satisfy all the responses may not be the best one due to aggregation of responses into a single response during neuro-fuzzy stage. In this direction, a multi-objective optimization based on non-dominated sorting genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters. The proposed optimal settings are validated using thermal-modeling of finite element analysis

Measurements, Models, Systems and Design

531 s.