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Machining stability enhancement in multi-axis milling of titanium hollow blade by introducing multiple damping and rigid supporters

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JOURNAL OF MANUFACTURING PROCESSES 2021年 64卷 198-208页

作者： Hou, Junming Zhao, Zhengcai Fu, Yucan Qian, Ning Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Nanjing Inst Technol Ind Ctr Nanjing 211167 Peoples R China

Hollow blades, which are important components of aeroengines, have thin-walled structures and are subjected to vibrations in the machining process owing to its low rigidity and damping. This paper proposes an approach to enhance the machining stability of titanium hollow blades by introducing multiple damping and rigid supporters to the blade machining system in multi-axis milling process. A theoretical prediction model, considering the influence of original system, transmission and supporter, is established to predict the dynamic parameters of blade with a single damping or rigid supporter. Then, the model is applied in multi-supporter system and an optimization algorithm is used to determine reasonable positions of multiple supporters. The stability lobe diagrams of blade system with no supporter, optimized damping supporters and optimized rigid supporters are analysed and validated by blade machining experiment. The conclusion can be achieved that the sound signal is suppressed up to 40 % by adding optimized supporters and the machining stability is enhanced effectively.

关键词： Hollow blade Dynamic parameters optimization algorithm Multiple supporters

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Optimizing manufacturing plant layouts using AI and heuristic algorithms

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-11页

作者： Raju, G. S. Vizhian, S. Paul Kumar, M. Vijay Sufyan, Md Saeb Madhusudan, M. Gururaja, M. N. Prasad, C. Durga Mohan, Dhanesh G. Hanumanthappa, Harish Bangalore Univ Dept Mech Engn UVCE Bengaluru 560001 Karnataka India JSS Sci & Technol Univ Dept Mech Engn SJCE Mysore 570006 Karnataka India Govt Engn Coll Dept Civil Engn Nawada 805111 India Dayananda Sagar Coll Engn Dept Mech Engn Bengaluru 560111 Karnataka India Dayananda Sagar Acad Technol & Management Dept Mech Engn Kanakapura Rd Bengaluru 560082 Karnataka India RV Inst Technol & Management Dept Mech Engn Bengaluru 560076 Karnataka India Univ Sunderland Fac Technol Sch Engn Sunderland England Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura Punjab India RV Univ Sch Comp Sci & Engn Bengaluru Karnataka India

The study outlines an artificial intelligence-based methodology for defining manufacturing plant layout in the context of mass customisation, where a significant amount of part variability needs to be controlled. A variety of actions are suggested, with an emphasis on data transformations that make it possible to retrieve pertinent information for the plant layout design of the manufacturing facility. The article also uses optimization algorithms and simulation techniques for designing plant layouts and how those methods have changed throughout time as a result of the development of computer systems and artificial intelligence methods. A variety of layout metrics, including the number of facilities and temporary storage space, are gathered to determine the plan's efficiency. With the revised layout, the suggested solution boosts layout efficiency while reducing operator mobility and temporary storage space. Starting with the production routing of each product, the proposed algorithms would enable manufacturing companies to automate plant layout design in the context of mass customization using artificial intelligence techniques. It also emphasizes how important it is to review the layout design procedure whenever new products are introduced.

关键词： Manufacturing Plant layout optimization algorithm Artificial intelligence

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Semi-Analytical Sensitivity Analysis for Multibody System Dynamics Described by Differential-Algebraic Equations

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AIAA JOURNAL 2021年第3期59卷 893-904页

作者： Peng, Haijun Zhang, Mengru Zhang, Lidan Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Delft Univ Technol Dept Precis & Microsyst Engn NL-2600 AA Delft Netherlands

A large number of deployable space structures involve multibody system dynamics, and in order to effectively analyze and optimize dynamic performance, the sensitivity information of multibody systems is often required. At present, the sensitivity analysis methods of multibody system dynamics, which have been widely used, are mainly finite difference method, direct differentiation method, and adjoint variable method. Among them, the finite difference method is an approximate method;the direct differentiation method and the adjoint variable method are analytical methods. Based on the dynamic problems of the multibody system in the form of differential-algebraic equations, the semi-analytical sensitivity analysis method for multibody system dynamics is proposed in this paper, which combines the simplicity of the finite difference method with the accuracy of the analytical methods. It includes the local semi-analytical method based on the element level and the global semi-analytical method based on the system level, of which the latter has higher computational efficiency. Through two numerical examples, the effectiveness and numerical stability of the method are verified. This method not only retains the accuracy and efficiency of the analytical methods, but also simplifies the derivation and coding of analytical formulas by combining with the existing programs. It has stronger versatility and is beneficial to the sensitivity calculation of large-scale complex multibody systems.

关键词： Sensitivity Analysis Multibody Dynamics Numerical Integration Finite Difference Method Space Structures Trusses Mathematical Models Structural optimization optimization algorithm Finite Element Method

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A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2021年第6期193卷 1-17页

作者： Song, Chenguang Yao, Leihua Hua, Chengya Ni, Qihang China Univ Geosci Beijing Sch Engn & Technol Beijing 100083 Peoples R China

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model's performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.

关键词： VMD SSA LSSVM Water quality prediction optimization algorithm Hybrid model

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Application of a novel grey Bernoulli model to predict the global consumption of renewable energy

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ENERGY REPORTS 2021年 7卷 7200-7211页

作者： Duan, Huiming Wang, Siqi He, Chenglin Huang, Jiangbo Chongqing Univ Posts & Telecommun Sch Sci Chongqing 400065 Peoples R China

Accurate prediction of renewable energy can provide an important basis for national energy security and the government to formulate policies. Therefore, according to the non-linearity of energy prediction system, this paper proposes a new nonlinear grey Bernoulli optimization model by the grey Bernoulli extended model, which is a nonlinear grey prediction model, and studies the properties of the new model. The order and nonlinear coefficient of the new model are optimized by Particle Swarm optimization. Then, through the consumption of global renewable energy, such as solar, wind and hydropower as empirical analyses, the results of the four evaluation indicators show that the new model works better than the original model, which has higher prediction accuracy than before and makes the prediction model more applicable. At the same time, the model results were compared with the weighted grey model, Verhulst and the discrete grey model, and the new model has the highest accuracy. Finally, the new model is used to forecast the global consumption of wind, solar and hydropower energy in 2019-2023. The results will provide important forecasting information for global energy conservation and emission reduction policies. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Grey prediction model Bernoulli model Renewable energy consumption optimization algorithm Forecasting

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Application Of Chimp-based ANFIS Model For Forecasting The Compressive Strength Of The Improved High-performance Concrete

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淡江理工学刊 2024年第4期27卷 2295-2306页

作者： Yan Yuan

In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R^2 and lower Root means square error values. Comparing the findings shows that the created CO - ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.

关键词： High-performance concrete Compressive strength Support vector regression Adaptive neuro-fuzzy inference system optimization algorithm

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AG-SGD: Angle-Based Stochastic Gradient Descent

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IEEE ACCESS 2021年 9卷 23007-23024页

作者： Song, Chongya Pons, Alexander Yen, Kang Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

In the field of neural network, stochastic gradient descent is often employed as an effective method of accelerating the result's convergence. Generating the new gradient from the past gradient is a common method adopted by many existing optimization algorithms. Since the past gradient is not computed based on the most updated stochastic gradient descent state, it can introduce a deviation to the new gradient computation, negatively impacting its rate of convergence. To resolve this problem, we propose an algorithm that quantifies this deviation based on the angle between the past and the current gradients, which is then applied to calibrate these two gradients, generating a more accurate new gradient. To demonstrate the broad applicability of the algorithm, the proposed method is implemented into a neural network and a logistic regression classifier which are evaluated on the datasets MNIST and NSL-KDD, respectively. An in-depth analysis is performed to compare our algorithm with nine optimization algorithms in two experiments, demonstrating the advantages in the cost and the error rate reductions from adopting the proposed method.

关键词： Neural networks optimization Classification algorithms Trajectory Rendering (computer graphics) Reliability Object recognition Stochastic gradient descent optimization algorithm gradient deviation neural network

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Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第1期28卷 160-168页

作者： Chi, Shenshen Yu, Xuexiang Wang, Lei Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Key Lab Aviat Aerosp Ground Cooperat Monitoring & KLAHEI KLAHEI18015 Huainan 232001 Peoples R China Anhui Univ Sci & Technol Coal Ind Engn Res Ctr Min Area Environm & Disaste Huainan 232001 Peoples R China

In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tan beta, b and theta was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability.

关键词： BP neural network MIV algorithm mining subsidence optimization algorithm PIM underground mining

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Modeling and optimal control of fast filling process of hydrogen to fuel cell vehicle

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JOURNAL OF ENERGY STORAGE 2021年 35卷

作者： Bai, Yunfeng Zhang, Caizhi Duan, Hao Jiang, Shangfeng Zhou, Zhiming Grouset, Didier Zhang, Mingjun Ye, Xuefeng Chongqing Univ Sch Automot Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Chongqing Automot Collaborat Innovat Ctr Chongqing 400044 Peoples R China Zhengzhou Yutong Bus Co Ltd Yutong Ind PkYutong Rd Zhengzhou 450061 Henan Peoples R China IMT Mines Albi CNRS UMR 5302 RAPSODEE Campus Jarlard F-81013 Albi 09 France Beijing PERIC Hydrogen Technol Co Ltd Damei Ctr Qingnian Rd Beijing 100123 Peoples R China Chongqing Changan New Energy Vehicle Technol Co L Prop Res Inst Chongqing 400000 Peoples R China

Due to the rapid compression of hydrogen and the Joule-Thompson effect specific to hydrogen during the fast filling process, the internal temperature of the cylinder rises sharply which may lead to hidden safety hazards. In this study, a high-pressure hydrogen filling process is considered, and a simple mathematical model of a cascade storage system of a hydrogen refilling station is developed to analyze the temperature rise in hydrogen cylinders under different working conditions. The results show the pressure switching coefficient has a great impact on the filling time, and the pre-cooling of hydrogen has a significant impact on the temperature rise and the states of charge (SOC) within cylinder. Herein, a multi-objective iterative optimization algorithm is proposed to calculate the above two controllable variables (pressure switching coefficient, pre-cooling temperature of hydrogen) with the objectives of faster refueling, lower energy consumption and higher SOC within cylinders in cascade hydrogen refueling. Besides, this method could significantly decrease energy consumption, improve SOC and allow acceptable refueling time.

关键词： Cascade storage system Fast filling Pressure switching coefficient Pre-cooling temperature optimization algorithm

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Testing coverage criteria for optimized deep belief network with search and rescue

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JOURNAL OF BIG DATA 2021年第1期8卷 1-20页

作者： Jammalamadaka, Kiran Parveen, Nikhat Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.

关键词： Deep learning Coverage criteria optimization algorithm Adversarial example Software testing

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