Ramón Quiza

Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process

The strong points of Estimation-of-Distribution algorithms (EDAs) and specifically cross-entropy methods are widely acknowledged. One of the main advantages of EDAs is that the fusion of prior information into the optimization procedure is straightforward, thereby reducing convergence time when such information is available. This study presents the modified Multi-Objective Cross-Entropy (MOCE+) method, based on a new procedure for addressing constraints: (i) the use of variable cutoff values for selecting the elitist population; and, (ii) filtering of the elitist population after each epoch. We study the proposed method in different test suites and compare its performance with some other well-known optimization methods. The comparative study demonstrates the good figures of merit of the MOCE+ method in complex test suites. Finally, the proposed method is applied to the multi-objective optimization of a micro-drilling process. Two conflicting targets are considered: total drilling time and vibrations on the plane that is perpendicular to the drilling axis. The Pareto front, obtained through the optimization process, is analyzed through quality metrics and the available options in the decision-making process. Overall, the quality metrics of the MOCE+ method were better than the metrics of the other optimization methods considered in this work. The reported optimization of the micro-drilling process with the proposed method could potentially have a direct impact on improvements in industrial efficiency.

Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization

The complexity of machining processes relies on the inherent physical mechanisms governing these processes including nonlinear, emergent, and time-variant behavior. The measurement of surface roughness is a critical step done offline by expensive quality control procedures. The surface roughness prediction using an online efficient computational method is a difficult task due to the complexity of machining processes. The paradigm of hybrid incremental modeling makes it possible to address the complexity and nonlinear behavior of machining processes. Parametrization of models is, however, one bottleneck for full deployment of solutions, and the optimal setting of model parameters becomes an essential task. This paper presents a method based on simulated annealing for optimal parameters tuning of the hybrid incremental model. The hybrid incremental modeling plus simulated annealing is applied for predicting the surface roughness in milling processes. Two comparative studies to assess the accuracy and overall quality of the proposed strategy are carried out. The first comparative demonstrates that the proposed strategy is more accurate than theoretical, energy-based, and Taguchi models for predicting surface roughness. The second study also corroborates that hybrid incremental model plus simulated annealing is better than a Bayesian network and a multilayer perceptron for correctly predicting the surface roughness.

Hybrid Modeling and Optimization of Manufacturing

This chapter begins with an explanation about the importance of modeling and optimization of manufacturing processes not only from the scientific and researching point of view but also for practical industrial applications. Then it introduces the hybrid approach which combines artificial intelligence tools and finite element method for these modeling and optimization tasks. The advantages and shortcomings of each of these techniques are exposed, highlighting the convenience of combining both methods, increasing the robustness and flexibility. Furthermore, the different approaches for combining artificial intelligence and finite element method in modeling and optimization of manufactured processes are outlined and preliminarily evaluated. 1.1 Relevance and Convenience of Hybrid Modeling and Optimization of Manufacturing Processes Modeling of the physical phenomena involved in manufacturing processes (machining, forming, foundry, etc.) has been recognized as one of the most important tasks in manufacturing research. Accurate and realistic mathematical models not only allow understanding how these phenomena take place, but also facilitate the development of new manufacturing processes. Knowledge about the quantitative relationships between the different parameters involved in manufacturing processes also permits the implementation of effective monitoring and control system, which are indispensable in the high automated modern industry. Moreover, the use of highly optimized manufacturing processes is widely accepted as a necessary condition for achieving effectiveness, efficiency and economic competitiveness in manufacturing workshops. R. Quiza et al., Hybrid Modeling and Optimization of Manufacturing, SpringerBriefs in Computational Mechanics, DOI: 10.1007/978-3-642-28085-6_1, The Author(s) 2012 1 Unfortunately, the physical nature of the phenomena underlying the manufacturing processes is not easy to understand, as they involve complex nonlinear relationships which are no completely explained up to date. This situation worsens with the use of modern materials for parts and tools. For example, for many years, the tool life of high speed steels cutting tools, working at relatively low speeds, was described by the well-known Taylor’s law (Childs et al. 2000) with an acceptable error level. However, the introduction of multi-coated carbides and PCBN tools, at high cutting speeds and hard conditions has made the Taylor’s law useless or, at less, extremely limited (Dolinsek et al. 2001). Two main approaches can be used in modeling manufacturing processes (see Fig. 1.1). On one hand, the phenomenological modeling is based on the identification and mathematical description of the physical phenomena. It has the advantage of being more realistic and accurate. Also they help to understand the mechanisms of these phenomena. This mathematical description comes in the form of expressions, usually differential equations which can be solve analytically only for a limited set of simple problems. For most of the real problems, these equations (especially partial differential equations) have not analytical solutions and approximated numerical solutions must be obtained instead. Between the numerical methods used for solving partial differential equation, the finite element method (FEM) has reach the higher application levels, because of its ability for being applied to problems defined over complex spatial domains and the relative simplicity of its computational implementation (Dixit and Dixit 2008). FEM has been successfully applied to a wide variety of manufacturing processes, such as machining (Arrazola and Ozel 2010; Mamalis et al. 2008), forming (Gudur and Dixit 2008; Shahani et al. 2009), welding (Anca et al. 2011) and foundry (Lewis et al. 2005). Sometimes, the phenomenological models are too much idealized for giving results accurate enough for being used in practical applications. In these cases, empirical models, based on correlation of experimental data, play a crucial role. The main drawback of this kind of models is its incapability for identifying or explaining the physical relationship between the involved variables. However, they usually offer accurate outcomes for industrial applications and had been widely used in optimization of manufacturing processes. Traditionally, the mathematical techniques used for correlating data in empirical models were those based on statistics. These tools are quite simple and are Fig. 1.1 Classification of modeling techniques 2

Modeling and Optimization of Mechanical Systems and Processes

This chapter reviews the most commonly used techniques used for modeling and optimizing mechanical systems and processes. Statistical and artificial intelligence based tools for modeling are summarized, pointing their advantages and shortcomings. Also, analytic, numeric and stochastic optimization techniques are briefly explained. Finally, two cases of study are developed in order to illustrate the use of these tools, the first one dealing with the modeling of the surface roughness in a drilling process and the other one, on the multi-objective optimization of a hot forging process.

Artificial Intelligence Tools

This chapter summarizes the main concepts on artificial intelligence, remarking those tools which are commonly applied to the modeling and optimization of manufacturing processes. Special emphasis has been done on soft computing techniques, because of the wide use that these ones have in this field. Each of the main soft computing techniques (artificial neural networks, fuzzy logic and stochastic optimization) is explained and, examples of applications are given.

Computational Methods and Optimization

This chapter aims to illustrate the application of computer-based techniques and tools in modelling and optimization of hard-machining processes. An overview of the current state-of-the-art in this wide topic is reflected. Computational methods are explained not only for modelling the relationships between the variables in the cutting process, but also for optimizing the most important parameters. The characteristics of these techniques are exposed and their advantages and shortcomings are compared. Foreseen future trends in this field are presented.

Editorial: A brief overview of artificial intelligence applications in machining

A brief overview of the main applications of AI in machining is carried out. This does not claim to be and exhaustive review but a simple outline of current state-of-the art and future trend in this branch. In accordance with this, only review papers or very representative and recent works are cited.

A Simple Multi-Objective Optimization Based on the Cross-Entropy Method

A simple multi-objective cross-entropy method is presented in this paper, with only four parameters that facilitate the initial setting and tuning of the proposed strategy. The effects of these parameters on improved performance are analyzed on the basis of well-known test suites. The histogram interval number and the elite fraction had no significant influence on the execution time, so their respective values could be selected to maximize the quality of the Pareto front. On the contrary, the epoch number and the working population size had an impact on both the execution time and the quality of the Pareto front. Studying the rationale behind this behavior, we obtained clear guidelines for setting the most appropriate values, according to the characteristics of the problem under consideration. Moreover, the suitability of this method is analyzed based on a comparative study with other multi-objective optimization strategies. While the behavior of simple test suites was similar to all methods under consideration, the proposed algorithm outperformed the other methods considered in this paper in complex problems, with many decision variables. Finally, the efficiency of the proposed method is corroborated in a real case study represented by a two-objective optimization of the microdrilling process. The proposed strategy performed better than the other methods with a higher hyperarea and a shorter execution time.

A fuzzy-genetic system to predict the cutting force in microdrilling processes

This paper presents the modeling of thrust force in microdrilling processes of five commonly used alloys (titanium-based, tungsten-based, aluminum-based and invar). The process was carried out by peck drilling and the influence of five parameters (drill diameter, cutting speed, feed rate, one-step feed length and total drilling length) on the behavior of the thrust force was considered. A fuzzy system was used for describing these relationships and genetic algorithms were used for fitting the parameters of the model from the experimental data. Finally a comparison with a traditional cutting model obtained with a regression model was made showing both models a similar correlation values (R2), 0.84 for the regression model and 0.86 for the fuzzy-genetic system. However, the fuzzy model showed a better generalization capability (> 0.9) than the regression model, (very poor, near to 0).

Intelligent Models for Predicting the Thrust Force and Perpendicular Vibrations in Microdrilling Processes

This paper presents the modeling of thrust force and perpendicular vibrations in micro drilling processes of five commonly used alloys (titanium-based, tungsten-based, aluminum-based and invar). The process was carried out by peck drilling and the influence of five parameters (drill diameter, cutting speed, feed rate, one-step feed length and total drilling length) on the behavior of the thrust force was considered. Some important mechanical and thermal properties of the work piece material were also considered in the model. Two different models were tried: the first one based on artificial neural networks and the second one based on fuzzy inference systems. Outcomes of both approaches were compared to each other and to a multiple regression model. The neural model shows not only a better goodness-of-fit but also a higher generalization capability.