Browsing by Author "Aour, Benaoumeur"

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Automation of three-dimensional inspection using the iterative closest point algorithm: application to a gas turbine blade
(Springer Nature, 2024) Bloul, Benattia; Aour, Benaoumeur; Harhout, Riad
This paper examines an innovative approach to the automated inspection of turbine blades in power generation systems. By integrating point cloud generated from computer-aided design (CAD) with iterative methods, this methodology aims to improve the efficiency and accuracy of the inspection process. The combination of theoretical blade geometry data, captured via CAD point clouds, with advanced algorithms derived from iterative methods, allows for a rapid assessment of the blade’s condition. This approach has the potential to reduce inspection times, increase assessment accuracy, and detect anomalies at an early stage. The paper explores in detail the key aspects of this method, including the creation and alignment of CAD point cloud with real blades, the application of iterative methods to detect degradation and anomalies, and the preliminary results obtained from specific case studies. However, challenges remain, such as the quality of input data and the need to develop iterative models specific to each application. Future prospects include the refinement of data capture techniques, the exploration of new iterative methods, and the integration of machine learning for even more advanced automation. Overall, this approach represents a significant advance in the field of industrial maintenance, enabling proactive and efficient management of turbine blade inspection and maintenance. It offers advantages in terms of cost, speed, and reliability for maintaining the sustainability and performance of power generation systems.
Numerical investigations on the surface integrity of a mechanical part machined by the end milling process using different teeth of the milling cutter
(2022) Bloul, Benattia; Aour, Benaoumeur; Chanal, Hélène; Chtioui, Nargess; Harhout, Riad
This study allows to analyze the influence of the teeth number of the milling cutter during end milling machining on the surface quality, mechanical characteristics, cutting forces, are valuable in terms of providing high precision and efficient machining in order to give a new approach to improve the surface quality of a mechanical part, reducing residual stresses, cutting forces by the material removal process. The Surface roughness is particularly sensitive to the runout errors of the insert, number of cutter teeth and the nose radius during this type of machining. This paper is a comparative study of the surface finish, stresses, and cutting forces when machining with a 2, 6 and 8 tooth cutter under the same cutting conditions. In addition to the investigations on the surface integrity and the comparative geometrical study, we have provided a calculation tool to simulate the influence of the residual stresses and the loads applied on the mechanical part. This approach examines the characteristics of the surface topography of aluminum parts, a stainless steel tool and the material strength of this part in general. The investigation is carried out by several simulated and realizable tests and performed on the HPC unit and the machine tool by exploiting the finite element method in the case of stable machining. The exploitation of the finite element method has led to promising results in terms of propagation of the machined surface state, residual stresses and cutting forces which have a considerable effect on the wear on the main edge of the cutter. The simulation shows that the roughness of the surface condition is inversely influenced by the teeth number of the end mill, the cutting forces as well as the residual stresses.
Study of geometrical defects of free-form surface machined using neural network
(SAGE Publications Inc, 2021) Bloul, Benattia; Chanal, Hélène; Aour, Benaoumeur; Chtioui, Nargess
The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluating the defects of complex forms. The originality of the approach is the use of artificial intelligence to position the cloud of measured points, obtained with a three-dimensional measuring machine equipped with a contactless sensor, with regard to the 3D CAD model of the THA. The artificial intelligence algorithm used is based on neural networks that are trained using a virtual positioning realized with 3D CAD software. Finally, the difference between the positioned point cloud and the CAD model allows us to evaluate the shape defect of the measured THA surface. We found that the error of the proposed method is at the vicinity of micron scale.