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Optimization of LPBF Processing and Aging Treatment Parameters for Maraging Steel Using Genetic algorithms: Experimental Validation and Fracture Behavior Analysis

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LASERS IN MANUFACTURING AND MATERIALS PROCESSING 2025年

作者： Jaballah, Ola Omidi, Narges Belmarouf, Chaimae Iltaf, Asim Barka, Noureddine El Ouafi, Abderrazak Univ Quebec Rimouski Dept Math Comp Sci & Engn Rimouski PQ G5L 3A1 Canada

This study investigates the optimization of laser powder bed fusion (LPBF) processing parameters and aging treatment conditions for maraging steel to enhance its mechanical performance. A series of experiments were conducted to generate robust datasets for artificial intelligence-based tools, enabling advanced optimization strategies. A genetic algorithm (GA) was employed to optimize relative density and microhardness by systematically analyzing the effects of laser power, scanning speed, hatch spacing, aging time, and temperature. The optimized parameters predicted by GA were validated through additional experiments by manufacturing samples using the recommended settings. Within these optimized conditions, tensile behavior was evaluated, accompanied by fractographic and microstructural analyses to gain deeper insights into fracture mechanisms. The findings of this study provide valuable guidance for optimizing manufacturing processes and heat treatments, offering a foundation for industrial applications requiring high-performance maraging steel components.

关键词： LPBF Maraging Steel Aging Treatment Artificial Intelligence genetics algorithm

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IWO-IGA-A Hybrid Whale Optimization algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

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algorithmS 2024年第3期17卷 115页

作者： Saleem, Sharoon Hussain, Fawad Baloch, Naveed Khan Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm's efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm's superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

关键词： whale optimization algorithm genetics algorithm network-on-chip real-time parameter control

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A complementary webometric ranking system based on the website quality and traffic measures: A study focusing on top-ranked world universities

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EDUCATION FOR INFORMATION 2021年第3期37卷 337-354页

作者： Farashi, Sajjad Bashirian, Saeed Hamadan Univ Med Sci Res & Technol Hamadan Hamadan Iran Hamadan Univ Med Sci Dept Publ Hlth Sch Publ Hlth Hamadan Hamadan Iran Hamadan Univ Med Sci Social Determinants Hlth Res Ctr Hamadan Hamadan Iran Hamadan Univ Med Sci Dent Implants Res Ctr Hamadan Hamadan Iran

Ranking of universities regarding their web-based activities plays a pivotal role in promoting scientific advancement since it motivates the open access accessibility to scientific results. In this study, a new ranking system based on the website quality factors and traffic evaluation was proposed. Since top-ranked universities are usually considered as the standard models for lower ranked ones, the focus of this study was on top-ranked universities. The proposed ranking was compared with well-known Webometrics ranking system. The website traffic and quality assessment were acquired for websites of top-ranked world universities and the correlation between these indices and the Webometrics ranking was evaluated. The summation of the weighted value of obtained measures according to an optimal weight vector obtained by a genetic algorithm framework was used for ranking purposes. The results showed that the website total traffic size was correlated with Webometrics rank (R approximate to -0.6, p < 0.01). Also, using the weighted value of website quality and traffic measures, the proposed ranking system could predict Webometrics ranking by the accuracy of up to 69%. Even though the method was proposed for universities, it could be applied for ranking other types of centers or companies, provided that the suitable cost function for the genetics algorithm framework was defined.

关键词： Webometrics ranking website traffic website quality measures correlation analysis genetics algorithm

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Android Malware Prediction Using Extreme Learning Machine with Different Kernel Functions 19

Android Malware Prediction Using Extreme Learning Machine wi...

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15th Asian Internet Engineering Conference (AINTEC)

作者： Kumar, Lov Hota, Chinmay Mahindru, Arvind Neti, Lalita Bhanu Murthy BITS Pilani Hyderabad Campus Hyderabad India DAV Univ Jalandhar Punjab India

ISBN: (纸本)9781450368490

Android is currently the most popular smartphone platform which occupied 88% of global sale by the end of 2nd quarter 2018. With the popularity of these applications, it is also inviting cybercriminals to develop malware application for accessing important information from smartphones. The major objective of cybercriminals to develop Malware apps or Malicious apps to threaten the organization privacy data, user privacy data, and device integrity. Early identification of such malware apps can help the android user to save private data and device integrity. In this study, features extracted from intermediate code representations obtained using decompilation of APK file are used for providing requisite input data to develop the models for predicting android malware applications. These models are trained using extreme learning with multiple kernel functions ans also compared with the model trained using most frequently used classifiers like linear regression, decision tree, polynomial regression, and logistic regression. This paper also focuses on the effectiveness of data sampling techniques for balancing data and feature selection methods for selecting right sets of significant uncorrelated metrics. The high-value of accuracy and AUC confirm the predicting capability of data sampling, sets of metrics, and training algorithms to malware and normal applications.

关键词： Artificial neural network Maintainability genetics algorithm Object-Oriented Metrics Parallel Computing

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Intelligent onboard assistant for maneuvering in port

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2016年第3期13卷 304-309页

作者： Marichal, G. N. Hernandez, A. Rojas, J. A. Melon, E. Rodriguez, J. A. Padron, I. Univ La Laguna Escuela Politecn Super Ingn Secc Nat Maquinas & Radioelect Naval C Avda Francisco Larroche S-N Santa Cruz De Tenerife 38071 Spain

An assistant onboard system is proposed in order to provide help to the captain. It is a Genetic Neuro-Fuzzy system which is able to advices about how the maneuver should be done depending on each particular situation. Particularly the casting-off is taking into account. In this way, the obstacles situated around the vessel like others boats or the port structures are considered when the maneuver is in process. Depending on the distances the system provides adequate values to the three control parameters: angle turn of tiller, principal engine power and lateral engine power.

关键词： Neuro-Fuzzy genetics algorithm vessel maneuver

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Developing a fuzzy linear programming model for locating recovery facility in a closed loop supply chain

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 2015年第2期8卷 122-137页

作者： Alimoradi, Ali Yussuf, Rosnah Mohd Ismail, Napsiah Bt. Zulkifli, Norzima Univ Putra Malaysia Fac Engn Dept Mech & Mfg Engn Serdang 43400 Selangor Malaysia

In this research a closed loop supply chain is designed which incorporates reverse logistics and forward logistic system simultaneously. In the design of reverse logistic system, recovery options are embedded in traditional supply chain for treating returned products. The recovery system includes collection centres, remanufacturing plants and disposal centres. Since the product return is supply driven, there is an uncertainty about it. In the proposed configuration for closed loop supply chain, the optimised configuration for supply chain in terms of locating recovery plants is developed. Accordingly, a fuzzy mixed integer linear programming model develops to deal with the uncertainty of returning products by customers. A general-purpose solver (LINGO 8.0) and a Meta heuristic approach (genetics algorithm) are implemented to solve the proposed model. The answers are compared by defining indexes and then the optimal answer, configuration and variables are identified. This solution will suggest a new design of supply chain network in which waste of materials is minimised and the new raw materials are necessary only when the used products may not be recovered by recovery options.

关键词： recovery system closed loop supply chain fuzzy linear programming genetics algorithm

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A Model to Assess the Effectiveness of Fault Prediction Techniques for Quality Assurance 12

A Model to Assess the Effectiveness of Fault Prediction Tech...

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12 IEEE Int C Elect Energy Env Communications Computer Control

作者： Kumar, Lov Rath, Santanu Ku. Natl Inst Technol Dept CS&E Rourkela India

ISBN: (纸本)9781467365406

Fault prediction techniques aim to predict faulty module in order to reduce the effort to be applied in later phase of software development. Majority of the approaches available in literature for fault prediction are based on regression analysis and neural network techniques. It is observed that numerous software metrics are also being used as input for fault prediction. In this paper, a cost evaluation model has been proposed for Object-Oriented software which performs cost based analysis for misclassification of faults. Appropriately, this work focuses on inspecting the usability of fault prediction. Chidamber and Kemerer (CK) metrics suite has been considered to provide requisite input data to design the model using logistic regression and hybrid approach of Neural network and Particle Swarm Optimization (Neuro-PSO and Modified Neuro-PSO). Here, fault considered as dependent variable and CK metric suite are as independent variables. A case study of Eclipse JDT core has been considered for predicting a comparative study of performances of two approaches. Fault prediction is found to be useful where normalized estimated fault removal cost (NEcost) was less than certain threshold value. Modified Neuro-PSO model obtained promising results in terms of cost analysis when compared with those of Neuro-PSO and logistic regression.

关键词： CK Metric Fault prediction Eclipse JDT core genetics algorithm Neuro-PSO Modified Neuro-PSO regression analysis

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An integrated system based on fuzzy genetic algorithm and neural networks for stock price forecasting Case study of price index of Tehran Stock Exchange

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2014年第3期31卷 281-292页

作者： Abbassi, Noraddin Aghaei, Mohammad Fard, Mahdi Tarbiat Modares Univ Tehran Iran Tarbiat Modares Univ Dept Accounting Tehran Iran Islamic Azad Univ Dept Accounting Branch Karaj Karaj Iran

Purpose - The aim of this research is to predict the total stock market index of the Tehran Stock Exchange, using the compound method of fuzzy genetics and neural network, in order for the active participants of the finance market as well as macro decision makers to be able to predict the market trend. Design/methodology/approach - First, the prediction was done by neural network, then the output weight of optimum neural network was taken as standard to repeat this prediction using the genetic algorithm, and then the extracted pattern from the neural network was stated through discernible rules using fuzzy theory. Findings - The main attention of this paper is investors and traders to achieve a method for predicting the stock market. Concerning the results of previous research, which confirms the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by compounding the non-linear method such as fuzzy genetics and neural network. The results indicate superiority of the designed system in predicting price index of the Tehran Stock Exchange. Originality/value - This paper states its originality and value by compounding the non-linear method issues pattern to predict stock market, to encourage further investigation by academics and practitioners in the field.

关键词： Artificial neural network Fuzzy genetic system Fuzzy theory genetics algorithm

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GA and ANFIS based Power System Stabilizer

GA and ANFIS based Power System Stabilizer

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Gandhi, P. R. Joshi, S. K. Sardar Vallabhbhai Patel Inst Technol Dept Elect Engn Vasad India M S Univ Baroda Elect Engn Dept Vadodara India

ISBN: (纸本)9781479913039

This paper discusses the design of genetics algorithm based proportional integral derivative-power system stabilizer and Adaptive Neuro Fuzzy Inference System based power system stabilizer for the stability analysis of the single machine infinite bus system. The controller has been used to generate the appropriate supplementary control signal for the excitation system of synchronous generator. The signal generated has been used to damp the low frequency oscillations and improves the performance of power system dynamics. The non-linear simulations of the system has been carried out, which results show the efficacy and capability of two schemes for the design of PSS under the various disturbances and faults conditions.

关键词： Adaptive Neuro Fuzzy Inference System Eigen values genetics algorithm power system stabilizer synchronous machine dynamics

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GA and ANFIS based Power System Stabilizer

GA and ANFIS based Power System Stabilizer

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IEEE Power and Energy Society General Meeting

作者： P.R.Gandhi S.K.Joshi Electrical Engineering Department Sardar Vallabhbhai Patel Institute of Technology Vasad India Electrical Engineering Department The M.S.University of Baroda Vadodara India

ISBN: (纸本)9781479913022

This paper discusses the design of genetics algorithm based proportional integral derivative-power system stabilizer and Adaptive Neuro Fuzzy Inference System based power system stabilizer for the stability analysis of the single machine infinite bus system. The controller has been used to generate the appropriate supplementary control signal for the excitation system of synchronous generator. The signal generated has been used to damp the low frequency oscillations and improves the performance of power system dynamics. The non- linear simulations of the system has been carried out, which results show the efficacy and capability of two schemes for the design of PSS under the various disturbances and faults conditions.

关键词： Adaptive Neuro Fuzzy Inference System Eigen values genetics algorithm power system stabilizer synchronous machine dynamics Power system stability adaptive neuro fuzzy inference system single machine infinite bus system exciters Power system dynamics Gallium

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