79 research outputs found
Surface Defect Detection And Polishing Parameter Optimization Using Image Processing For G3141 Cold Rolled Steel
Traditionally the surface quality inspection especially for metal polishing purpose is perform by human inspectors. Defect detection is a method of nondestructive testing of material and products to detect defects. This study consists of two parts where the first part is applying vision system to detect and measure surface defects that have been characterized to some level of surface roughness. Specimen of G3141 cold rolled steel is used in this research as it represent the actual material applied in local automotive manufacturer. Gray image of scratch defect on metal surface is detected and information about mean gray pixel value (Ga) is interpreted and converted to surface roughness (Ra) measurement. In this study a new technique is developed where the Ga only read on the specific scratch line without considering the whole image. To realize this, automatic cropping algorithm is developed to detect the region of interest and interpret the Ga value. This techniques will enables the polishing to be done at specific scratch defect area without necessary to develop polishing path throughout the whole surface which is time consuming. Second part is to obtain the optimum polishing parameter by using artificial intelligence technique which is able to predict the grit size, polishing time and polishing force parameter to remove the scratch by polishing process. For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. However due to lack of study done in the field of flat and dry polishing, the polishing parameter data have to be developed. The polishing parameter data for flat and dry polishing is performed by using robotic polishing arm and the experiment runs design by using full factorial design. Results show that the defect detection algorithm able to detect defect only on the scratch area and able to read the Ga value at detected scratch line and transform it to surface roughness measurement at considerably good level of accuracy compared with manual method. Results from MANFIS have shown that the system is able to predict up to 95% accuracy which is considerably high. The overall results from both parts of this research would inspire further advancements to achieve robust machine vision based surface measurement systems for industrial robotic processes specifically in polishing process
Modeling and Optimization of Chemical Mechanical Planarization (Cmp) Using Neural Networks, Anfis and Evolutionary Algorithms
Higher density nano-devices and more metallization layers in microelectronic chips are unceasing goals to the present semiconductor industry. However, topological imperfections (higher non-uniformity) on the wafer surfaces and lower material removal rates (MRR) seriously hamper these pursuing motivations. Since'90, industry has been using chemical mechanical planarization/polishing (CMP) to overcome these obstacles for fabricating integrated circuits (IC) with interconnect geometries of < 0.18 &#956;m. Obviously, the much needed understanding of this new technique is derived basically on the ancient lapping process. Modeling and simulation are critical to transfer CMP from an engineering 'art' to an engineering 'science'. Many efforts in CMP modeling have been made in the last decade, but the available analytical MRR and surface uniformity models cannot precisely describe this highly complicated process, involving simultaneous chemical reactions (and etching), and mechanical abrasion. In this investigation, neural networks (NN), adaptive-based-network fuzzy inference system (ANFIS), and evolutionary algorithms (EA) techniques were applied to successfully overcome the aforementioned modeling and simulation problems. In addition, fine-tuning techniques for re-modifying ANFIS models for sparse-data case using are developed. Furthermore, multi-objective evolutionary algorithms (MOEA) are firstly applied to search for the optimal input settings for CMP process to trade-off the higher MRR and lower non-Uniformity by using the previously constructed models. The results also show the simulation of MOEA optimization can certainly provide accurate guidance to search the optimal input settings for CMP process to produce lower non-uniform wafer surfaces under higher MRR.Mechanical & Aerospace Engineerin
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Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review
Copyright: © 2021 by the authors. The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies
Métodos clásicos y de soft-computing en la optimización de procesos complejos: Aplicación a un proceso de fabricación
Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, febrero de 201
Proceeding Of Mechanical Engineering Research Day 2015 (MERD’15)
This Open Access e-Proceeding contains 74 selected papers from the Mechanical Engineering Research Day 2015 (MERD’15) event, which is held in Kampus Teknologi, Universiti Teknikal Malaysia Melaka (UTeM) - Melaka, Malaysia, on 31 March 2015. The theme chosen for this event is ‘Pioneering Future Discovery’.
The response for MERD’15 is overwhelming as the technical committees have received more than 90 papers from various areas of mechanical engineering. From the total number of submissions, the technical committees have selected 74 papers to be included in this proceeding. The selected papers are grouped into 12 categories: Advanced Materials Processing; Automotive Engineering; Computational Modeling and Analysis & CAD/CAE; Energy Management & Fuels and Lubricants; Hydraulics and Pneumatics & Mechanical Control; Mechanical Design and Optimization; Noise, Vibration and Harshness; Non-Destructive Testing & Structural Mechanics; Surface Engineering and Coatings; Others Related Topic.
With the large number of submissions from the researchers in other faculties, the event has achieved its main objective which is to bring together educators, researchers and practitioners to share their findings and perhaps sustaining the research culture in the university. The topics of MERD’15 are based on a combination of advanced research methodologies, application technologies and review approaches.
As the editor-in-chief, we would like to express our gratitude to the editorial board members for their tireless effort in compiling and reviewing the selected papers for this proceeding. We would also like to extend our great appreciation to the members of the Publication Committee and Secretariat for their excellent cooperation in preparing the proceedings of MERD’15
AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE
The work in this thesis concerns with the development of a novel multisensor data fusion
(MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic
and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous
underwater vehicle (AUV) navigation system, formed by an integration of global positioning
system and inertial navigation system (GPS/INS).
The Kalman filter has been a popular method for integrating the data produced
by the GPS and INS to provide optimal estimates of AUVs position and attitude. In
this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is
proposed. The former is used to fuse the data from a variety of INS sensors whose
output is used as an input to the later where integration with GPS data takes place.
The use of an adaptation scheme based on fuzzy logic approaches to cope with the
divergence problem caused by the insufficiently known a priori filter statistics is also
explored. The choice of fuzzy membership functions for the adaptation scheme is first
carried out using a heuristic approach. Single objective and multiobjective genetic
algorithm techniques are then used to optimize the parameters of the membership
functions with respect to a certain performance criteria in order to improve the overall
accuracy of the integrated navigation system. Results are presented that show
that the proposed algorithms can provide a significant improvement in the overall
navigation performance of an autonomous underwater vehicle navigation.
The proposed technique is known to be the first method used in relation to AUV
navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd.,
Qinetiq, Subsea 7 and South West Water PL
Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression
This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions
Development of chatter threshold boundary for milling of metals
This study reports on a novel experimental method for the prediction of chatter based on the chaos theory. The variation of Poincaré sections of the reconstructed phase space attractor is able to identify the transition of the milling system from a stable to an unstable condition, continuously during the milling process. Two mathematical tools are used to measure the variation of Poincaré sections they being; image correlation and a designed regression model. Image correlation uses Poincaré sections as a pattern and the computation of Pearson’s coefficient assists to develop a chatter threshold boundary. Titanium is the main material in this research, as chatter is more applicable during cutting of titanium due to its specific mechanical properties. Moreover, the method is used in detection of chatter during milling of stainless steel and aluminum in order to demonstrate the method can detect chatter during cutting of other metals. The new method can be used to detect chatter on-line, as it is independent of the cutting parameters and dynamics of the milling process, and can be integrated in the cutting machines. The method does not need expensive equipment and complex process, so it can be easily used in normal production workshop environment. A regression model computes the trend of changes in the Poincaré sections and gives a numerical output value to define the boundary between the stable and unstable state of the milling process. These mathematical tools can be used in expert software to monitor the milling process on-line and detect the onset of chatter
Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data
Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies
Industrial Applications: New Solutions for the New Era
This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section
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