Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Urban bicycles renting systems: Modelling and optimization using nature-inspired search methods

Urban Bicycles Renting Systems (UBRS) are becoming a common and useful component in growing modern cities. For an efficient management and support, the UBRS infrastructure requires the optimation of vehicle routes connecting several bicycle base stations and storage centers. In this study, we model this real-world optimization problem as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneous need for pickup and delivery at each base station location. Based on the VRP model specification, two nature-inspired computational techniques, evolutionary algorithms and ant colony systems, are presented and their performance in tackling the UBRS problem is investigated. In the evolutionary approach, individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. In the ant-based approach, ants build complete solutions formed of several subtours servicing a subset of base stations using a single vehicle based on both apriori (the attractiveness of a move based on the known distance or other factors) and aposteriori (pheromone levels accumulated on visited edges) knowledge. Both algorithms are engaged for the UBRS problem using real data from the cities of Barcelona and Valencia. Computational experiments for several scenarios support a good performance of both population-based search methods. Comparative results indicate that better solutions are obtained on the average by the ant colony system approach for both considered cities

Urban bicycles renting systems: Modelling and optimization using nature-inspired search methods

Urban Bicycles Renting Systems (UBRS) are becoming a common and useful component in growing modern cities. For an efficient management and support, the UBRS infrastructure requires the optimation of vehicle routes connecting several bicycle base stations and storage centers. In this study, we model this real-world optimization problem as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneous need for pickup and delivery at each base station location. Based on the VRP model specification, two nature-inspired computational techniques, evolutionary algorithms and ant colony systems, are presented and their performance in tackling the UBRS problem is investigated. In the evolutionary approach, individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. In the ant-based approach, ants build complete solutions formed of several subtours servicing a subset of base stations using a single vehicle based on both apriori (the attractiveness of a move based on the known distance or other factors) and aposteriori (pheromone levels accumulated on visited edges) knowledge. Both algorithms are engaged for the UBRS problem using real data from the cities of Barcelona and Valencia. Computational experiments for several scenarios support a good performance of both population-based search methods. Comparative results indicate that better solutions are obtained on the average by the ant colony system approach for both considered cities

Detection of heat flux failures in building using a soft computing diagnostic system

The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain - heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real data sets from several Spanish cities in winter time

Learning and training techniques in fuzzy control for energy efficiency in buildings

A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain

The application of a two-step AI model to an automated pneumatic drilling process

Real-world processes may be improved through a combination of artificial intelligence and identification techniques. This work presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on a robotic drill used in the construction of industrial storage centres. The first step entails determination of the most relevant structures in the data set with the application of the connectionist architectures. The second step combines the results of the first one to identify a model for the optimal working conditions of the drilling robot that is based on low-order models such as black box that approximate the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples

Merge Method for Shape-Based Clustering in Time Series Microarray Analysis

A challenging task in time-course microarray data analysis is to combine the information provided by multiple time series in order to cluster genes meaningfully. This paper proposes a novel merge method to accomplish this goal obtaining clusters with highly correlated genes. The main idea of the proposed method is to generate a clustering, starting from clusterings created from different time series individually, that takes into account the number of times each clustering assemble two genes into the same group. Computational experiments are performed for real-world time series microarray with the purpose of finding co-expressed genes related to the production and growth of a certain bacteria. The results obtained by the introduced merge method are compared with clusterings generated by time series individually and averaged as well as interpreted biologically

A Soft Computing System to Perform Face Milling Operations

In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough. The second phase is focus on identifying a model for the face milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel tools