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

Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R$^{2}$) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R$^{2}$ of 0.94 and md of 0.89, and HyFIS with R$^{2}$ of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R$^{2}$: 0.953/0.960, md: 0.903/0.912, then ANFIS with R$^{2}$: 0.943/0.942, md: 0.888/0.890, and HyFIS with R$^{2}$ 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure

Risk Management Effectiveness of Oil And Gas Pipeline Construction in Java Island - Indonesia

One of the important oil and gas industry is oil and gas pipeline which serves to support the gas transmission and distribution process. The oil and gas pipeline routes are so long and cross through some areas that certainly raises the risk of adversely affecting the environment, especially for the pipeline located in a densely populated area, where at certain conditions, the pipe can leak and may cause the explosion. Many researches have been conducted concerning on the project pipeline risks which concluded that the oil and gas pipeline project has very complex risks. In addition, the oil and gas project may cause a potential disaster. Currently oil and gas companies have been implementing risk management to minimize the negative impacts, but the negative impacts are still occurred. This is due to ineffective risk management implementation. This study aims to analyze the effectiveness of risk management system for oil and gas pipeline project in Java Island. Based on a deep study literature review, it shows that the effectiveness of risk management can be achieved by taking account into environmental, social and economic factors that are the pillars of sustainable development system. The potential disaster was also to be considered as an addition factor. Therefore, four identified factors were analyzed using Analytical Hierarchy Process (AHP) method. Data were obtained using questionnaire which were distributed to oil and gas project team. It is found that the most factors to be considered was social aspect (40%) and the other factors contributed 31% for disaster, 15% for economic, 14% for environment. Those factors should be taken account in the design stage as the most priority

Using spatiotemporal patterns to qualitatively represent and manage dynamic situations of interest : a cognitive and integrative approach

Les situations spatio-temporelles dynamiques sont des situations qui évoluent dans l’espace et dans le temps. L’être humain peut identifier des configurations de situations dans son environnement et les utilise pour prendre des décisions. Ces configurations de situations peuvent aussi être appelées « situations d’intérêt » ou encore « patrons spatio-temporels ». En informatique, les situations sont obtenues par des systèmes d’acquisition de données souvent présents dans diverses industries grâce aux récents développements technologiques et qui génèrent des bases de données de plus en plus volumineuses. On relève un problème important dans la littérature lié au fait que les formalismes de représentation utilisés sont souvent incapables de représenter des phénomènes spatiotemporels dynamiques et complexes qui reflètent la réalité. De plus, ils ne prennent pas en considération l’appréhension cognitive (modèle mental) que l’humain peut avoir de son environnement. Ces facteurs rendent difficile la mise en œuvre de tels modèles par des agents logiciels. Dans cette thèse, nous proposons un nouveau modèle de représentation des situations d’intérêt s’appuyant sur la notion des patrons spatiotemporels. Notre approche utilise les graphes conceptuels pour offrir un aspect qualitatif au modèle de représentation. Le modèle se base sur les notions d’événement et d’état pour représenter des phénomènes spatiotemporels dynamiques. Il intègre la notion de contexte pour permettre aux agents logiciels de raisonner avec les instances de patrons détectés. Nous proposons aussi un outil de génération automatisée des relations qualitatives de proximité spatiale en utilisant un classificateur flou. Finalement, nous proposons une plateforme de gestion des patrons spatiotemporels pour faciliter l’intégration de notre modèle dans des applications industrielles réelles. Ainsi, les contributions principales de notre travail sont : Un formalisme de représentation qualitative des situations spatiotemporelles dynamiques en utilisant des graphes conceptuels. ; Une approche cognitive pour la définition des patrons spatio-temporels basée sur l’intégration de l’information contextuelle. ; Un outil de génération automatique des relations spatiales qualitatives de proximité basé sur les classificateurs neuronaux flous. ; Une plateforme de gestion et de détection des patrons spatiotemporels basée sur l’extension d’un moteur de traitement des événements complexes (Complex Event Processing).Dynamic spatiotemporal situations are situations that evolve in space and time. They are part of humans’ daily life. One can be interested in a configuration of situations occurred in the environment and can use it to make decisions. In the literature, such configurations are referred to as “situations of interests” or “spatiotemporal patterns”. In Computer Science, dynamic situations are generated by large scale data acquisition systems which are deployed everywhere thanks to recent technological advances. Spatiotemporal pattern representation is a research subject which gained a lot of attraction from two main research areas. In spatiotemporal analysis, various works extended query languages to represent patterns and to query them from voluminous databases. In Artificial Intelligence, predicate-based models represent spatiotemporal patterns and detect their instances using rule-based mechanisms. Both approaches suffer several shortcomings. For example, they do not allow for representing dynamic and complex spatiotemporal phenomena due to their limited expressiveness. Furthermore, they do not take into account the human’s mental model of the environment in their representation formalisms. This limits the potential of building agent-based solutions to reason about these patterns. In this thesis, we propose a novel approach to represent situations of interest using the concept of spatiotemporal patterns. We use Conceptual Graphs to offer a qualitative representation model of these patterns. Our model is based on the concepts of spatiotemporal events and states to represent dynamic spatiotemporal phenomena. It also incorporates contextual information in order to facilitate building the knowledge base of software agents. Besides, we propose an intelligent proximity tool based on a neuro-fuzzy classifier to support qualitative spatial relations in the pattern model. Finally, we propose a framework to manage spatiotemporal patterns in order to facilitate the integration of our pattern representation model to existing applications in the industry. The main contributions of this thesis are as follows: A qualitative approach to model dynamic spatiotemporal situations of interest using Conceptual Graphs. ; A cognitive approach to represent spatiotemporal patterns by integrating contextual information. ; An automated tool to generate qualitative spatial proximity relations based on a neuro-fuzzy classifier. ; A platform for detection and management of spatiotemporal patterns using an extension of a Complex Event Processing engine

Integration of operations in process systems

The problem of system Integration in the context of an Industrial Enterprise is a multidimensional problem with fundamental dimensions those of: (i) Purpose, goals and objectives, (ii) Overall process operations, (ii) Overall System Design/Redesign, (iii) Information, Data and Software, and (iv) Verification, validation and assurance. Each of the above areas is addressed by respective groups which consider their area as representing the entirety of the problem and they frequently ignore the other important dimensions. The aim of this paper is to consider first the general problem of integration from all its fundamental aspects and focus on the key paradigm of Global Operations. Within this area we consider the issues of system organization and in particular the problems relating to hierarchical organization of the different operational functionalities, the issues of aggregation and disaggregation and the related problems of the "top-down" and "bottom up" approaches. System complexity is considered within this framework and the notion of emergent properties is then considered as a problem of aggregation of behaviours, which may be also seen as projections within the setup of a control and information architecture. Such an architecture is defined for the multi-level hierarchical organisational structure that we consider. We show that systems and control concepts and problems play a central role the development of an overall integration methodology and interpreting the features of the different emergent properties. The subject of modelling emerges with a central role in the effort to develop a methodology for systems integration, as well as quantifying the predictors of relevant emergent properties. The approach introduced here is intimately linked to Multilevel hybrid systems (Hierarchy of Operations), and provides a complementary dimension to issues of System Design (and Re-design) dominated by the theory of Structure Evolving systems [10] (in the total Design and Life-cycle analysis) emerges as the central approach. This paper provides an overview of the subject area and focuses on the development of the general conceptual framework for integration. We additionally develop and propose a systems framework for the evolutionary integration in this paper

Productivity analysis of horizontal directional drilling

The National Research Council of Canada reported that rehabilitation of municipal water systems between 1997 and 2012 would cost $28 billion (NRC, 2004). With the rapid increase of new installations, the need for replacement and repair of pipe utilities and also the demand for trenchless excavation methods, increase. This must be done with minimum disruption to public. One alternative to reduce disruption is to use horizontal directional drilling (HDD) for new pipe installation scenarios. Consequently, contractors, engineers, and decision makers are facing continuous challenges regarding to estimation of execution time and cost of new pipe installations, while using HDD. This is because productivity prediction and consequently the cost estimation of HDD involves a large number of objective and subjective factors that need to be considered. It is well known that prediction of both productivity and cost is an important process in establishing and employing management strategies for a construction operation. This calls for the need of developing a dedicated HDD productivity model that meets present day requirements of this area of construction industry. There are two main objectives of the current research. The first objective is to identify the factors that affect productivity of HDD operations. The second objective is to develop a productivity prediction model for different soil conditions. To achieve these two objectives a thorough literature review was carried out. Thereafter, data on potential factors on productivity were collected from HDD experts across North America and abroad. Following data collection, the current research identified managerial, mechanical, environmental and pipe physical conditions parameters operating in three types of soils: clay, rock and sandy soils. Prior to model development, Analytical Hierarchy Process (AHP) technique was used to classify and rank these factors according to their relative importance. A neurofuzzy (NF) approach is employed to develop HDD productivity prediction model for pipe installation. The merits of this approach are that it decreases uncertainties in results, addresses non-linear relationships and deals well with imprecise and linguistic data. The following eight factors were finally selected as inputs of the model to be developed: operator/ crew skills, soil type, drilling rig capabilities, machine conditions, unseen buried obstacles, pipe diameter, pipe length and site weather and safety conditions. The model is validated using actual project data. The developed NF model showed average validation percent of 94.7%, 82.3% and 86.7%, for clay, rock and sand, respectively. The model is also used to produce productivity curves (production rate vs. influencing factors) for each soil type. Finally, an automated user-friendly productivity prediction tool (HDD-PP) based on present NF model is developed to predict HDD productivity. This tool is coded in MatLab ® language using the graphical user interface tool (GUI). The tool was used to test a case study. It was proved to be helpful for contractors, consultants and HDD professionals in predicting execution time and to estimate cost of HDD projects during the preconstruction phase in the environment of imprecise and noisy inputs

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments