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

Abstract

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, copper 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.

Enriching conflict resolution environments with the provision of context information

“…UMCourt and VirtualECare are two research projects maintained by the Intelligent Systems Lab, at the University of Minho. The VirtualECare project (Costa et al, 2008;Costa et al, 2009) focuses on the Ambient Intelligence paradigm and has as main objective to develop an agent-based environment able to monitor, interact and provide its customers with services to support daily living. Moreover, this project also focuses on the acquisition of data for supporting high-level decision making.…”

“…The use of soft computing techniques can be the answer to this challenge. Indeed, this field has been used recently to address a wide range of different real-world problems Sedano et al, 2010;Corchado et al, 2012;Zargari et al, 2012). Therefore, in this paper, a novel approach on ODR research is presented, merging different concepts: Ambient Intelligence, Role-Playing Games, Emotions, Conflict Handling Styles and Stress.…”

ERP, APS and Simulation Systems Integration to Support Production Planning and Scheduling

“…Soft computing techniques have developed significantly in last ten years. Many soft computing methods have recently come to forefront for solving unlimited number of complex real-world problems particularly related to optimization research area [6][7][8][9][10]. Application of production scheduling problem-solving approximate methods (soft computing) and obtained thereby results due to the complexity of the problem, do not take into account all the constraints of the production system.…”

Facility layout design using a multi‐objective interactive genetic algorithm to support the DM

“…This fact allows the DM to interact with the algorithm, guiding the search process and adjusting it to DM preferences through the subjective evaluations of representative solutions, which are different enough and are chosen using a clustering method called c-means clustering method (Bezdek et al, 1984). This soft computing technique allows the same element to belong to more than one group (Corchado et al, 2012).…”