Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

Fuzzy Supplier Selection Strategies in Supply Chain Management

Supplier selection is a major strategy for manufacture to run the production process smoothly in supply chain network. Supplier categorization, selection and performance evaluation are decisions of strategic importance to companies. Global competition, mass customization, high customer expectations and harsh economic conditions are forcing companies to rely on external suppliers to contribute a larger portion of parts, materials, and assemblies to finished products and to manage a growing number of processes and functions that were once controlled internally. Thus supplier performance evaluation is very important to choose the right supplier for the right product for supply chain management. In this paper a fuzzy supplier selection algorithm (FSSA) is implemented to rank the technically efficient vendors according to both predetermined performance criteria and additional product-related performance criteria. Investigation of the properties of the best supplier alternative by ranking the fuzzy indices allow to develop an algorithm which is based on calculating fuzzy suitability indices for the efficient supplier alternatives and validity is illustrated through an example problem

On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets

Type-reduction of type-2 fuzzy sets is considered to be a defuzzification bottleneck because of the computational complexity involved in the process of type-reduction. In this research, we prove that the closed-form Nie-Tan operator, which outputs the average of the upper and lower bounds of the footprint of uncertainty, is actually an accurate method for defuzzifing interval type-2 fuzzy sets

Aide à la décision pour l'expertise des barrages

National audienceUn modèle d'évaluation des performances des barrages vis-à-vis de leurs principaux modes de rupture et de dégradation a été développé (Curt, 2008). Les données d'entrée (indicateurs) et de sortie (performance du barrage) de ce modèle sont des distributions de possibilité. Cette communication est axée sur la problématique de la prise de décision associée à ce résultat possibiliste : comment prioriser les actions de maintenance à entreprendre sur le barrage et comment transmettre l'information aux gestionnaires ? Nous proposons une analyse comparative des méthodes de défuzzification afin de sélectionner les méthodes répondant le mieux à cette problématique d'aide à l'expertise des barrages. / An assessment model of dam performances as regards their main failure modes and degradation modes was developed (Curt, 2008). The input data (indicators) and the ouput data (dam performance) of this model are possibility distributions. This paper focuses on the problematic of decision making associated to this possibility result: how to classify maintenance actions that have to be made on that dam and how to convey this result to dam managers? A comparative analysis of defuzzyfication methods is provided; those methods allow to best answer to this problematic of dam expertise

Non-excusable delays in construction

Existing literature and research findings indicated that delays are common amongst construction projects in many countries across the globe. Delays can be caused by one or more of the following: the client (excusable with compensation); force majeur or third party (excusable delays without compensation); or the contractors (non-excusable delays or contractor-responsible delays). Previous studies cited that approximately 50% of these delays can be classified as non-excusable delays. The root-causes (or factors) that cause non-excusable delays identified in these studies however, are given no detailed attention. Improving and constantly monitoring the factors causing non-excusable delays can help to determine and improve contractor's performance. This research explores issues related to the factors causing non-excusable delays. [Continues.