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Development of cell formation algorithm and model for cellular manufacturing system
Published 2011“…The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families that is named cell formation. …”
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2
Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations
Published 2013“…The BFO algorithm is used to create machine cells and part families. …”
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BASE: a bacteria foraging algorithm for cell formation with sequence data
Published 2010“…In addition, a newly developed BFA-based optimization algorithm for CF based on operation sequences is discussed. …”
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Development of a mathematical model for the prediction of chip formation instability and its verification by fuzzy logic with genetic algorithm
Published 2010“…It has been identified that the chip formation process has a discrete nature, associated with the periodic shearing process of the chip during machining of different materials. …”
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A New Cell Formation Method Considering Operation Sequences And Production Volumes
Published 2013“…The graph theory was employed to develop the algorithm. It consists of four stages that the first one shows the sequences of machines that are used by each part in a graph. …”
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6
Inter-cell and intra-cell facility layout models under different demand environments in cellular manufacturing systems
Published 2012“…Moreover, the computation time (CPU Time) of the developed SA algorithm is significantly less than the benchmarked algorithm. …”
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7
Development Of Generative Computer-Aided Process Planning System For Lathe Machining
Published 2019“…To validate the generated tool-path, G-codes generated in media package file (MPF) file format and verified through CNC lathe machine. Indeed, the developed algorithm was able to determine the minimum unit production cost of lathe machining part model. …”
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A Comparative Study of Some Cellular Manufacturing Techniques
Published 2000“…Overall, Bond Energy Algorithm (BEA) was found to be the best cell formation technique.…”
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Feature-based stereo vision relative positioning strategy for formation control of unmanned aerial vehicles
Published 2019“…In addition, several different techniques and approaches for developing the algorithm is discussed as well. As per system requirements and conducted study, the algorithm that is developed for this Vision System is based on Tracking and On-Line Machine Learning approach. …”
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Development of an integrated model for production planning and cell formation in cellular manufacturing systems
Published 2010“…One of the most important design steps is the formation of part families and machine cells or cell formation problem (CF), another problem is “how to determine the optimal quantities of different part-types” or in the other word production planning problem in CMS. …”
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12
Prediction of PVT properties in crude oil systems using support vector machines
Published 2023Subjects:Conference paper -
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Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
Published 2013“…With the optimal parameter sets, an operator can select a suitable combination of variables to obtain a better surface finish or lower burr formation. Optimal machining parameters were the spindle speed of 40000 rpm, the feed rate of 61-75 mm/min, and the depth of cut of 86-92 μm. …”
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SUDOKU HELPER
Published 2015“…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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Development of capability-based virtual cellular manufacturing systems in dual-resource constrained settings over semi-distributed layouts
Published 2012“…The performance of the developed CBVCMSs is improved by utilizing a novel layout namely Semi-Distributed Layouts (SDLs) using Genetic Algorithms (GAs). …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination
Published 2025“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction
Published 2023“…Different Machine Learning algorithms have been investigated, viz. …”
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Text normalization algorithm for facebook chats in Hausa language
Published 2014“…This is accomplished through modification of the technique employed by [1] to fit Hausa NSWs' formation. It was found that our proposed algorithm was able to normalized Hausa NSWs with an accuracy of 100%. …”
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Proceeding Paper -
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Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer
Published 2025“…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
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Jaya algorithm hybridized with extreme gradient boosting to predict the corrosion-induced mass loss of agro-waste based monolithic and Ni-reinforced porous alumina
Published 2024“…Corrosion testing data of these specimens were collected and fitted into both XGBoost and Jaya-XGBoost machine learning algorithms. The results showed that the Jaya-XGBoost model performed better in predicting the corrosion-induced mass loss of both the monolithic and the nickel-reinforced porous alumina than the regular XGBoost model in terms of statistical accuracy measures. …”
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