Sustav predviđanja i odlučivanja u procesu nadzora alata primjenom ANFIS-a i neuronske mreže

The aim of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time using a combination of a neural decision system, an ANFIS tool wear estimator and a machining error compensation module. The principal presumption was that the force signals contain the most useful information for determining tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals. The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. The fundamental challenge to research was to develop a single-sensor monitoring system, reliable as a commercially available system, but much cheaper than the multi-sensor approach.Cilj ovog rada je prikazati sustav nadzora alata (TCM) koji može detektirati lom alata u stvarnom vremenu primjenjujući kombinaciju sustava za odlučivanje pomoću neuronske mreže, ANFIS procjena trošenje alata i modula za kompenzaciju pogreške u obradi. Glavna pretpostavka je da signali sila sadrže najkorisnije informacije za utvrđivanje stanja alata. Stoga se ANFIS model koristi za izdvajanje značajki o stanju alata kroz signale sila rezanja. Nakon faze učenja ANFIS model trošenja alata je integriran s neuronskom mrežom za utvrđivanje stanja istrošenosti alata (novi, istrošen). Neuronska mreža je korištena u TCM kao podloga za donošenja odluka, pri tomu izbjegavajući stanja prouzročena nepravilnostima u izmjerenim signalima. Predviđanje ukupne pogreške obrade s vrlo visokom točnošću pomoću modula za ugib alata i visokog postotka njegovog eliminiranja kroz predloženi proces kompenzacije pogreške

Soft computing for tool life prediction a manufacturing application of neural - fuzzy systems

Tooling technology is recognised as an element of vital importance within the manufacturing industry. Critical tooling decisions related to tool selection, tool life management, optimal determination of cutting conditions and on-line machining process monitoring and control are based on the existence of reliable detailed process models. Among the decisive factors of process planning and control activities, tool wear and tool life considerations hold a dominant role. Yet, both off-line tool life prediction, as well as real tune tool wear identification and prediction are still issues open to research. The main reason lies with the large number of factors, influencing tool wear, some of them being of stochastic nature. The inherent variability of workpiece materials, cutting tools and machine characteristics, further increases the uncertainty about the machining optimisation problem. In machining practice, tool life prediction is based on the availability of data provided from tool manufacturers, machining data handbooks or from the shop floor. This thesis recognises the need for a data-driven, flexible and yet simple approach in predicting tool life. Model building from sample data depends on the availability of a sufficiently rich cutting data set. Flexibility requires a tool-life model with high adaptation capacity. Simplicity calls for a solution with low complexity and easily interpretable by the user. A neural-fuzzy systems approach is adopted, which meets these targets and predicts tool life for a wide range of turning operations. A literature review has been carried out, covering areas such as tool wear and tool life, neural networks, frizzy sets theory and neural-fuzzy systems integration. Various sources of tool life data have been examined. It is concluded that a combined use of simulated data from existing tool life models and real life data is the best policy to follow. The neurofuzzy tool life model developed is constructed by employing neural network-like learning algorithms. The trained model stores the learned knowledge in the form of frizzy IF-THEN rules on its structure, thus featuring desired transparency. Low model complexity is ensured by employing an algorithm which constructs a rule base of reduced size from the available data. In addition, the flexibility of the developed model is demonstrated by the ease, speed and efficiency of its adaptation on the basis of new tool life data. The development of the neurofuzzy tool life model is based on the Fuzzy Logic Toolbox (vl.0) of MATLAB (v4.2cl), a dedicated tool which facilitates design and evaluation of fuzzy logic systems. Extensive results are presented, which demonstrate the neurofuzzy model predictive performance. The model can be directly employed within a process planning system, facilitating the optimisation of turning operations. Recommendations aremade for further enhancements towards this direction

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Use of data mining techniques to explain the primary factors influencing water sensitivity of asphalt mixtures

The water sensitivity of asphalt mixtures affects the durability of the pavements, and it depends on several parameters related to its composition (aggregates and binder) and the production and application processes. One of the main parameters used in the European Standards to measure the water sensitivity of asphalt mixtures is the indirect tensile strength ratio (ITSR). Therefore, this work aims to obtain a predictive model of ITSR of asphalt mixtures using several parameters that affect water sensitivity and assess their relative importance. The database used to develop the model comprises thirteen parameters collected from one hundred sixty different asphalt mixtures. Data Mining techniques were applied to process the data using Multiple Regression, Artificial Neural Networks, and Support Vector Machines (SVM). The different metrics analysed showed that SVM is the best predictive model of the ITSR (mean absolute deviation of 0.116, root mean square error of 0.150 and Pearson correlation coefficient of 0.667). The application of a sensitivity analysis indicates that the binder content is the parameter that most influences the water sensitivity of asphalt mixtures (26%). However, this property depends simultaneously on other factors such as the characteristics of the coarse and fine aggregates (24.9%), asphalt binder characteristics (19.3%) and the use of additives (10%).Acknowledgements This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R & D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE) , under reference UIDB/04029/2020

Assessment of machinability of inconel 718: A comparative study of CVD & PVD coated tools

281-297This paper highlights the parametric appraisal in turning of inconel 718 using fuzzy inference system coupled with imperialistic competitive algorithm (ICA) approach. The machining variables such as spindle speed, feed rate and depth of cut have been taken into consideration to analyse their effect on evaluation characteristics viz. material removal rate (MRR), flank wear and surface roughness. Fuzzy inference system (FIS) has been used to integrate aforementioned evaluation characteristics into a single response known as multi performance characteristic index (MPCI) to address the issue of impreciseness and uncertainties involved in decision making. Mathematical models have also been proposed for MPCI using non-linear regression analysis which acts as an objective function in ICA. ICA is new meta-heuristic based on social political theory which is used to obtain global optimal parametric combination in machining of Inconel 718. The results indicate that single layer (single coating: AlTiN) physical vapour deposition (PVD) coated tool is more efficient as compared to multi-layered (four coatings: TiN, TiCN, Al2O3 and TiN) chemical vapour deposition (CVD) coated tool

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

The geochemical and geochronological properties of postcollision a-type magmatism (Keban-Elazığ-Turkey)

In this study, the petrographic, geochemical and geochronological characteristics of Late Cretaceous-Middle Eocene Keban igneous rocks were examined in Keban-Elazığ-Turkey. Igneous rocks in the study area are represented by syenite porphyry and quartz monzonites. Petro-graphically, the main mineral paragenesis of rocks showing holocrystalline texture are K-feldspar (Mega-phenocrystalline) + plagioclase ± amphibole ± biotite ± quartz minerals. Secondary mineral phases are represented by calcite, sericite, chlorite and epidote minerals.Accessory mineral phases consist of sphene, apatite, zircon, garnet, pyrite, fluorite and opaque minerals. According to some analysis results, SiO2 (60.09 – 64.37 wt.%), Al2O3 (15.75 – 17.96 wt.%), Fe2O3 (1.18 – 5.30 wt.%), MgO (0.09 – 0.92 wt.%) CaO (2.07 – 4.27 wt.%), Na2O (0.80 – 4.93 wt.%) , K2O (4.69 – 13.42 wt.%), TiO2 (0.22 – 0.37 wt.%), P2O5 (0.05 – 0.26 wt.%), Na2O + K2O (8.22 – 14.22), Zr (200.9 – 665.4 ppm), Hf (4.6 – 18.4 ppm), Ta (1.5 – 2.7 ppm), Nb (24 – 56 ppm) ranges between values. The chondrite normalized rare earth element (REE) patterns display enrichment of light rare earth elements (LREE) compared to the heavy rare earth elements (HREE). The primitive mantle normalized trace element patterns indicate that the large ion lithophile elements (LILE) enriched compared to the high field strength elements (HFSE). According to LA-ICPMS zircon U-Pb crystallization ages ranges between 46.1 ± 0.5, 76.3 ± 0.3, 76.36 ± 0.34 and 77.4 ± 0.3 My. (Late Cretaceous-Middle Eocene). In the tectonic environment diagrams the studied rocks fall into the post-collisional fields (developing after collision). These rocks fall into the A-type granitoid areas and are of shoshonitic character. It falls into the post-collisional region (developed after collision) in the tectonic environment diagrams of the rocks studied. According to the field, petrography, geochemical and geochronological studies are evaluated together, Keban Magmatic rocks are thought to have the characteristics of post-collision developed magmatism

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate