Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs

 Bubble point pressure is of great significance in reservoir engineering calculations affecting the success of reservoir simulation. For determining this valuable parameter, experimental tests are the most reliable techniques; however, these measurements are costly and time-consuming. So, it is crucial to propose an empirical model for estimating bubble point pressure. The existing correlations mainly have large errors and develop based on restricted database from a specific geographical location. As a result, development of an all-inclusive correlation is essential. In current article, gene expression programming (GEP) was used to create a generalized model for bubble point pressure estimation. To do this, an all-inclusive source of data was utilized for training and testing the model from the petroleum industry. Several statistical approaches including both illustration tools and diverse error functions were utilized to show the supremacy of the developed GEP model. Consequently, the recommended model is the most accurate as compared to the similar correlations in literature with the average absolute relative error (AARE = 11.41%) and determination coefficient (R2 = 0.96). Furthermore, the solution gas-oil ratio shows to be the most influencing variable on determining bubble point pressure according to sensitivity analysis. The results of contour map analysis demonstrate that most portions of the experimental region are predicted via the GEP equation with fewer errors as compared to two well-known literature correlations. Finally, the proposed GEP model can be of high prominence for accurate bubble point pressure estimation.Cited as: Rostami, A., Daneshi, A., Miri, R. Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 126-134, doi: 10.26804/ager.2020.02.0

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Porosity distribution prediction using artificial neural networks

Reservoir characterization plays a very important role in the petroleum industry, especially to the economic success of the reservoir development. Heterogeneity can complicate the evaluation of reservoir properties. Porosity is the primary key to a reliable reservoir model.;Several studies in the literature indicated that accurate evaluation of reservoir properties can be made by the analysis of electric logs. Stringtown oil field in Tyler and Wetzel counties in the northwestern part of West Virginia was selected to conduct this study.;Artificial Neural Networks (ANN) is one of the latest technologies available to the petroleum industry. The objective of this study was to predict reliable porosity values from geophysical log data. In this study, porosity predictions were compared against core measurements and were found to be reliable with R2 of 0.97. The results confirmed the capability of using ANN. The results were utilized to map the Porosity distribution

Oil PVT characterisation using ensemble systems

In reservoir engineering, there is always a need to estimate crude oil Pressure, Volume and Temperature (PVT) properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are often used instead of costly laboratory experiments to estimate these properties. However, these correlations do not always give sufficient accuracy. This paper develops ensemble support vector regression and ensemble regression tree models to predict two important crude oil PVT properties: bubblepoint pressure and oil formation volume factor at bubblepoint. The developed ensemble models are compared with standalone support vector machine (SVM) and regression tree models, and commonly used empirical correlations .The ensemble models give better accuracy when compared to correlations from the literature and more consistent results than the standalone SVM and regression tree models

Aided diagnosis of structural pathologies with an expert system

Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine. Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts. Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.Peer ReviewedPostprint (author's final draft