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Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第9期238卷 3818-3834页

作者： Biswas, Shatarupa Singh, Yogesh Mukherjee, Manidipto Natl Inst Technol Mech Engn Dept Silchar 788010 Assam India CSIR Cent Mech Engn Res Inst Durgapur 713209 W Bengal India Acad Sci & Innovat Res Ghaziabad India

In the field of machining different grades of alloys using wire electrical discharge machining (WEDM), developing efficient techniques has been a challenge. Typically, specific supervised learning and optimization (SLO) methods are created for individual alloys, resulting in a time-consuming process. To address this issue, a unified SLO approach capable of handling various grades of alloys with different compositions and physical characteristics is proposed. In this study, three stochastic optimization tools are combined with a multi-layer neural network to determine the optimal SLO strategy. Five different alloy grades are considered, with inputs consisting of six variables, including chemical composition, and physical properties. The neural network architecture used is 19-500-500-4, and it is combined with a gray wolf stochastic algorithm, proving to be the most effective SLO methodology. The results indicate that regardless of the alloy grade, the pulse on time significantly affects the material removal rate, kerf width, and thickness of the recast layer (RL). Surface roughness is primarily influenced by alloy properties such as density, specific heat, and elements like Cr, Mo, and Ni. Additionally, the recast layer is dependent on factors such as V, Ti, thermal conductivity, and thermal diffusivity of the materials. Validation experiments demonstrate that the mean squared error of all responses for each alloy remains within 5.0%. This unified SLO approach provides a promising solution for optimizing the machining process of different alloy grades, reducing the need for separate techniques for each material.

关键词： WEDM stochastic algorithms neural network supervised learning optimization

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A stochastic STEEPEST-DESCENT ALGORITHM

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1988年第2期59卷 307-323页

作者： WARDI, Y 1.School of Electrical Engineering Georgia Institute of Technology Atlanta Georgia

A stochastic steepest-descent algorithm for function minimization under noisy observations is presented. Function evaluation is done by performing a number of random experiments on a suitable probability space. The number of experiments performed at a point generated by the algorithm reflects a balance between the conflicting requirements of accuracy and computational complexity. The algorithm uses an adaptive precision scheme to determine the number of random experiments at a point; this number tends to increase whenever a stationary point is approached and to decrease otherwise. Two rules are used to determine the number of random experiments at a point; one, in the inner loop of the algorithm, uses the magnitude of the observed gradient of the function to be minimized; and the other, in the outer-loop, uses a measure of accumulated errors in function evaluations at past points generated by the algorithm. Once a stochastic approximation of the function to be minimized is obtained at a point, the algorithm proceeds to generate the next point by using the steepest-descent deterministic methods of Armijo and Polak (Refs. 3, 4). Convergence of the algorithm to stationary points is demonstrated under suitable assumptions.

关键词： Steepest-descent algorithm Armijo stepsize adaptive precision schemes stochastic algorithms

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SQuadMDS: A lean stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP

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NEUROCOMPUTING 2022年 503卷 17-27页

作者： Lambert, Pierre de Bodt, Cyril Verleysen, Michel Lee, John A. UCLouvain ICTEAM ELEN Pl Levant 3bte L5-03-02 B-1348 Louvain La Neuve Belgium MIT Media Lab Human Dynam Grp 20 Ames StWiesner Bldg E15 Cambridge MA 02139 USA UCLouvain IREC MIRO Ave Hippocrate 55 B1-54-07 B-1200 Brussels Belgium

Multidimensional scaling is a process that aims to embed high dimensional data into a lower -dimensional space;this process is often used for the purpose of data visualisation. Common multidimen-sional scaling algorithms tend to have high computational complexities, making them inapplicable on large data sets. This work introduces a stochastic, force directed approach to multidimensional scaling with a time and space complexity of O eth N THORN , with N data points. The method can be combined with force directed layouts of the family of neighbour embedding such as t-SNE, to produce embeddings that pre-serve both the global and the local structures of the data. Experiments assess the quality of the embeddings produced by the standalone version and its hybrid extension both quantitatively and qualitatively, showing competitive results outperforming state-of-the-art approaches. Codes are available at https://***/PierreLambert3/SQuaD-MDS-and-FItSNE-hybrid. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Dimensionality reduction t-SNE UMAP Neighbor embedding MDS Global structure stochastic algorithms Data visualization Distance preservation

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A stochastic ALGORITHM FOR OPTIMIZATION PROBLEMS WITH CONTINUA OF INEQUALITIES

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1988年第2期56卷 285-311页

作者： WARDI, Y 1.School of Electrical Engineering Georgia Institute of Technology Atlanta Georgia

Optimization algorithms for solving mathematical programming problems involving continua of inequalities are presented. The algorithms use an outer-approximation method, by which they attempt to approximate, at each point, the maxima of sets of inequality constraints. They do so by performing random experiments, resulting in a finite number of points, over which the maximum is taken. They use constraint-dropping schemes, by which they eliminate points from the constraint set at hand, which are felt to be irrelevant. At each point that the algorithms construct, they evaluate a measure of optimality, which indicates the distance of the point from the set of solutions of the optimization problem. They use this measure to determine the number of random experiments performed. Thus, the number of such experiments tends to be small initially, when the points at hand are far from optimal, and they tend to increase when an optimal point is approached.

关键词： Constraint-dropping schemes outer-approximation methods stochastic algorithms optimality functions continua of constraints

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A stochastic algorithm finding generalized means on compact manifolds

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stochastic PROCESSES AND THEIR APPLICATIONS 2014年第10期124卷 3463-3479页

作者： Arnaudon, Marc Miclo, Laurent Univ Bordeaux UMR 5251 Inst Math Bordeaux Bordeaux France CNRS F-75700 Paris France Univ Toulouse Inst Math Toulouse UMR 5219 Toulouse France

A stochastic algorithm is proposed, finding the set of generalized means associated to a probability measure nu on a compact Riemannian manifold and a continuous cost function kappa on M x M. Generalized means include p-means for p is an element of (0, infinity), computed with any continuous distance function, not necessarily the Riemannian distance. They also include means for lengths computed from Finsler metrics, or for divergences. The algorithm is fed sequentially with independent random variables (Y-n)(n is an element of N) distributed according to nu and this is the only knowledge of nu required. It evolves like a Brownian motion between the times it jumps in the direction of the Y-n. Its principle is based on simulated annealing and homogenization, so that temperature and approximation schemes must be tuned up. The proof relies on the investigation of the evolution of a time-inhomogeneous L-2 functional and on the corresponding spectral gap estimates due to Holley, Kusuoka and Stroock. (C) 2014 Elsevier B.V. All rights reserved.

关键词： stochastic algorithms Simulated annealing Homogenization Probability measures on compact Riemannian manifolds Intrinsic means Instantaneous invariant measures Gibbs measures Spectral gap at small temperature

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Multi-agent approach and stochastic optimization: random events in manufacturing systems

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JOURNAL OF INTELLIGENT MANUFACTURING 1999年第1期10卷 81-101页

作者： Fleury, G Goujon, JY Gourgand, M Lacomme, P Univ Blaise Pascal Lab Math Appliquees F-63177 Aubiere France Univ Blaise Pascal Lab Informat Modelisat & Optimisat Syst F-63177 Aubiere France

We propose a method to solve industrial problems and to take into account random events. It is called the triple coupling. It is based on stochastic algorithms, a simulation model and the multi-agents model of artificial intelligence. The method we propose is easy to use and allows us to take into account most of the constraints found in manufacturing systems. Experts look for solutions to increasing the capacity of production. But the production can be disturbed by random events experienced by the system. Industrial experts need schedules which prevent the consequences of random events. Minimizing such consequences is very important to increasing system delivery. Capital investment is often very high in factories and the cost of the investment goes on regardless of whether the resources are running or not. The multi-agent approach is used to determine schedules for which the consequences of random events are low, and a stochastic algorithm is proposed which permits us to optimize a random variable. We prove that this algorithm finds, with probability one, the schedule of the production for which the consequences of random events are the lowest. We propose to measure the consequences of random events using an influence ratio. Our approach has been used to study the consequences of random events in Peugeot sand foundries of Sept-Fons (France). A benchmark test is presented to prove the efficiency of our solution. For the Peugeot sand foundry of Sept-Fond, random events increase the production time by about 20% compared with the production time without any random events occurring. We have determined schedules of production for which the consequences of random events are about 0.5%.

关键词： manufacturing systems multi-agents technology planning random events breakdowns stochastic algorithms

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A stochastic Model for a Pseudo Affine Projection Algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2009年第1期57卷 107-118页

作者： de Almeida, Sergio J. M. Bermudez, Jose Carlos M. Bershad, Neil J. Catholic Univ Pelotas RS Brazil Univ Fed Santa Catarina Dept Elect Engn Florianopolis SC Brazil Univ Calif Irvine Dept Elect Engn & Comp Sci Newport Beach CA 92660 USA

This paper presents a statistical analysis of a Pseudo Affine Projection (PAP) algorithm, obtained from the Affine Projection algorithm (AP) for a step size alpha < 1 and a scalar error signal in the weight update. Deterministic recursive equations are derived for the mean weight and for the mean square error (MSE) for a large number of adaptive taps N compared to the order P of the algorithm. Simulations are presented which show good to excellent agreement with the theory in the transient and steady states. The PAP learning behavior is of special interest in applications where tradeoffs are necessary between convergence speed and steady-state misadjustment.

关键词： Adaptive filters affine projection analysis pseudo affine projection stochastic algorithms

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A correctness result for synthesizing plans with loops in stochastic domains

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2020年 119卷 92-107页

作者： Treszkai, Laszlo Belle, Vaishak Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Alan Turing Inst London England

Finite-state controllers (FSCs), such as plans with loops, are powerful and compact representations of action selection widely used in robotics, video games and logistics. There has been steady progress on synthesizing FSCs in deterministic environments, but the algorithmic machinery needed for lifting such techniques to stochastic environments is not yet fully understood. While the derivation of FSCs has received some attention in the context of discounted expected reward measures, they are often solved approximately and/or without correctness guarantees. In essence, that makes it difficult to analyze fundamental concerns such as: do all paths terminate, and do the majority of paths reach a goal state? In this paper, we present new theoretical results on a generic technique for synthesizing FSCs in stochastic environments, allowing for highly granular specifications on termination and goal satisfaction. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Plan and program synthesis stochastic domains Loops in plans and programs stochastic algorithms Planning in robotics

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Application of deterministic, stochastic, and hybrid methods for cloud provider selection

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-23页

作者： de Moraes, Lucas Borges Parpinelli, Rafael Stubs Fiorese, Adriano Dept Comp Sci DCC Joinville SC Brazil Grad Program Appl Comp PPGCA Joinville SC Brazil Santa Catarina State Univ UDESC Joinville SC Brazil

Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.

关键词： Cloud provider selection problem Cloud provider ranking Evolutionary computing Natural computing Performance indicator Hybrid methods stochastic algorithms Deterministic algorithms

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stochastic analysis of the LMS algorithm for cyclostationary colored Gaussian inputs

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SIGNAL PROCESSING 2019年 160卷 127-136页

作者： Bermudez, Jose C. M. Bershad, Neil J. Eweda, Eweda Univ Fed Santa Catarina Dept Elect & Elect Engn Florianopolis SC Brazil Univ Calif Irvine Dept Elect & Comp Engn Irvine CA 92717 USA C4 Adv Solut POB 43269 Abu Dhabi U Arab Emirates Univ Fed Santa Catarina Dept Engn Eletr BR-88040970 Florianopolis SC Brazil

This paper studies the stochastic behavior of the LMS algorithm for a system identification framework when the input signal is a cyclostationary colored Gaussian process. The unknown system is modeled by the standard random walk model. Well-known results for the LMS algorithm are extended to the cyclostationary case and used for predicting the mean-square weight deviation (MSD) and excess meansquare error (EMSE) behavior of the algorithm. Monte Carlo simulations provide strong support for the theory. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Adaptive filters Analysis LMS Algorithm Cyclostationary signals stochastic algorithms

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