Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively

Adaptive Neuro-Fuzzy Inference System modelling of surface topology in ultra-high precision diamond turning of rapidly solidified aluminium grade (RSA 443)

Surface roughness prediction is a crucial stage during product manufacturing since it acts as a quality indicator. This investigative research thesis presents an online surface roughness prediction, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model during Ultra-High Precision Diamond Turning (UHPDT) of Rapidly Solidified Aluminium (RSA-443) using water and kerosene as coolants. Based on the Taguchi L9 orthogonal array, the cutting parameters (spindle speed, depth of cut and feed rate) are varied at three levels. Acoustic Emission (AE) signals are detected during the UHPDT process using a piezoelectric sensor. Spindle speed, depth of cut, feed rate, AE root mean square, prominent frequency and peak rate are considered as model inputs in this thesis. The experimental results reveal that a better surface finish is obtained using water coolant in comparison to kerosene coolant. Mean Absolute Percentage Error (MAPE) based comparison between ANFIS and Response Surface Method (RSM) is carried out. In this study, the ANFIS model has a prediction accuracy of 79.42% and 69.40% on water-based and kerosene-based results respectively. The RSM model yields higher prediction accuracies of 98.59% and 95.55% on water-based and kerosene-based results respectively

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Dynamic data driven investigation of petrophysical and geomechanical properties for reservoir formation evaluation

Petrophysical and geomechanical properties of the formation such as Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, and porosity provide characteristic description of the hydrocarbon reservoir. It is well-established that static geomechanical properties are good representatives of reservoir formations; however, they are non-continuous along the wellbore, expensive and determining these properties may lead to formation damage. Dynamic geomechanical formation properties from acoustic measurements offer a continuous and non-destructive means to provide a characteristic description of the reservoir formation. In the absence of reliable acoustic measurements of the formation, such as sonic logs, the estimation of the dynamic geomechanical properties becomes challenging. Several techniques like empirical, analytical and intelligent systems have been used to approximate the property estimates. These techniques can also be used to approximate acoustic measurements thus enable dynamic estimation of geomechanical properties. This study intends to explore methodologies and models to dynamically estimate geomechanical properties in the absence of some or all acoustic measurements of the formation. The present work focused on developing empirical and intelligent systems like artificial neural networks (ANN), Gaussian processes (GP), and recurrent neural networks (RNN) to determine the dynamic geomechanical properties. The developed models serve as a cost-effective, reliable, efficient, and robust methods, offering dyanmic geomechanical analysis of the formation. This thesis has five main contributions: (a) a new data-driven empirical model of estimating static Young’s modulus from dynamic Young’s modulus, (b) a new data-driven ANN model for sonic well log prediction, (c) a new data-driven GP model for shear wave transit time prediction, (d) a new dynamic data-driven RNN model for sonic well log reproduction, and (e) an assessment on the ANN as a reliable sonic logging tool

Proceedings of the inaugural construction management and economics ‘Past, Present and Future’ conference CME25, 16-18 July 2007, University of Reading, UK

This conference was an unusual and interesting event. Celebrating 25 years of Construction Management and Economics provides us with an opportunity to reflect on the research that has been reported over the years, to consider where we are now, and to think about the future of academic research in this area. Hence the sub-title of this conference: “past, present and future”. Looking through these papers, some things are clear. First, the range of topics considered interesting has expanded hugely since the journal was first published. Second, the research methods are also more diverse. Third, the involvement of wider groups of stakeholder is evident. There is a danger that this might lead to dilution of the field. But my instinct has always been to argue against the notion that Construction Management and Economics represents a discipline, as such. Granted, there are plenty of university departments around the world that would justify the idea of a discipline. But the vast majority of academic departments who contribute to the life of this journal carry different names to this. Indeed, the range and breadth of methodological approaches to the research reported in Construction Management and Economics indicates that there are several different academic disciplines being brought to bear on the construction sector. Some papers are based on economics, some on psychology and others on operational research, sociology, law, statistics, information technology, and so on. This is why I maintain that construction management is not an academic discipline, but a field of study to which a range of academic disciplines are applied. This may be why it is so interesting to be involved in this journal. The problems to which the papers are applied develop and grow. But the broad topics of the earliest papers in the journal are still relevant today. What has changed a lot is our interpretation of the problems that confront the construction sector all over the world, and the methodological approaches to resolving them. There is a constant difficulty in dealing with topics as inherently practical as these. While the demands of the academic world are driven by the need for the rigorous application of sound methods, the demands of the practical world are quite different. It can be difficult to meet the needs of both sets of stakeholders at the same time. However, increasing numbers of postgraduate courses in our area result in larger numbers of practitioners with a deeper appreciation of what research is all about, and how to interpret and apply the lessons from research. It also seems that there are contributions coming not just from construction-related university departments, but also from departments with identifiable methodological traditions of their own. I like to think that our authors can publish in journals beyond the construction-related areas, to disseminate their theoretical insights into other disciplines, and to contribute to the strength of this journal by citing our articles in more mono-disciplinary journals. This would contribute to the future of the journal in a very strong and developmental way. The greatest danger we face is in excessive self-citation, i.e. referring only to sources within the CM&E literature or, worse, referring only to other articles in the same journal. The only way to ensure a strong and influential position for journals and university departments like ours is to be sure that our work is informing other academic disciplines. This is what I would see as the future, our logical next step. If, as a community of researchers, we are not producing papers that challenge and inform the fundamentals of research methods and analytical processes, then no matter how practically relevant our output is to the industry, it will remain derivative and secondary, based on the methodological insights of others. The balancing act between methodological rigour and practical relevance is a difficult one, but not, of course, a balance that has to be struck in every single paper

Collected Papers (on Neutrosophic Theory and Applications), Volume VIII

This eighth volume of Collected Papers includes 75 papers comprising 973 pages on (theoretic and applied) neutrosophics, written between 2010-2022 by the author alone or in collaboration with the following 102 co-authors (alphabetically ordered) from 24 countries: Mohamed Abdel-Basset, Abduallah Gamal, Firoz Ahmad, Ahmad Yusuf Adhami, Ahmed B. Al-Nafee, Ali Hassan, Mumtaz Ali, Akbar Rezaei, Assia Bakali, Ayoub Bahnasse, Azeddine Elhassouny, Durga Banerjee, Romualdas Bausys, Mircea Boșcoianu, Traian Alexandru Buda, Bui Cong Cuong, Emilia Calefariu, Ahmet Çevik, Chang Su Kim, Victor Christianto, Dae Wan Kim, Daud Ahmad, Arindam Dey, Partha Pratim Dey, Mamouni Dhar, H. A. Elagamy, Ahmed K. Essa, Sudipta Gayen, Bibhas C. Giri, Daniela Gîfu, Noel Batista Hernández, Hojjatollah Farahani, Huda E. Khalid, Irfan Deli, Saeid Jafari, Tèmítópé Gbóláhàn Jaíyéolá, Sripati Jha, Sudan Jha, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, M. Karthika, Kawther F. Alhasan, Giruta Kazakeviciute-Januskeviciene, Qaisar Khan, Kishore Kumar P K, Prem Kumar Singh, Ranjan Kumar, Maikel Leyva-Vázquez, Mahmoud Ismail, Tahir Mahmood, Hafsa Masood Malik, Mohammad Abobala, Mai Mohamed, Gunasekaran Manogaran, Seema Mehra, Kalyan Mondal, Mohamed Talea, Mullai Murugappan, Muhammad Akram, Muhammad Aslam Malik, Muhammad Khalid Mahmood, Nivetha Martin, Durga Nagarajan, Nguyen Van Dinh, Nguyen Xuan Thao, Lewis Nkenyereya, Jagan M. Obbineni, M. Parimala, S. K. Patro, Peide Liu, Pham Hong Phong, Surapati Pramanik, Gyanendra Prasad Joshi, Quek Shio Gai, R. Radha, A.A. Salama, S. Satham Hussain, Mehmet Șahin, Said Broumi, Ganeshsree Selvachandran, Selvaraj Ganesan, Shahbaz Ali, Shouzhen Zeng, Manjeet Singh, A. Stanis Arul Mary, Dragiša Stanujkić, Yusuf Șubaș, Rui-Pu Tan, Mirela Teodorescu, Selçuk Topal, Zenonas Turskis, Vakkas Uluçay, Norberto Valcárcel Izquierdo, V. Venkateswara Rao, Volkan Duran, Ying Li, Young Bae Jun, Wadei F. Al-Omeri, Jian-qiang Wang, Lihshing Leigh Wang, Edmundas Kazimieras Zavadskas