Artificial neural network models for the prediction of surface roughness in electrical discharge machining

Markopoulos, Angelos P.; Manolakos, D.E.; Vaxevanidis, Nikolaos M.

doi:10.1007/s10845-008-0081-9

Cited by 132 publications

(57 citation statements)

References 17 publications

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“…On the other hand, the ANN-based models start to increase the RMSEs when they, respectively, reach about 250,000 training samples in Machine 1 and about 50,000 training samples in Turning 2. This phenomenon is due to the over-fitting problem, which undermines a generalization ability of ANN-based models and can be caused from larger training data sizes than needed to solve the given problem [47]. …”

Section: Discussionmentioning

confidence: 99%

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

et al. 2018

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Abstract:In the metal cutting industry, power consumption is an important metric in the analysis of energy efficiency since it relates to energy consumption of machine tools. Much of the research has developed predictive models that correlate process planning decisions with power consumption through theoretical and/or experimental modeling approaches. These models are created by using the theory of metal cutting mechanics and Design of Experiments. However, these models may lose their ability to predict results correctly outside the required assumptions and limited experimental conditions. Thus, they cannot accurately reflect a diversity of machining configurations; i.e., selections of machine tool, workpiece, cutting tool, coolant option, and machining operation for producing a part, which a machining shop has operated. This paper proposes a predictive modeling approach based on historical data collected from machine tool operations. The proposed approach can create multiple predictive models for power consumption, which can be applicable to the diverse machining configurations. It can create fine-grained models predictable up to the level of a numerical control program. It uses standard-based data interfaces such as STEP-NC and MTConnect to implement interoperable and comprehensive data representations. This paper also presents a case study to demonstrate the feasibility and effectiveness of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

et al. 2018

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“…ANN was proposed based on modern biology research concerning human brain tissue, and can be used to simulate neural activity in the human brain (Markopoulos et al, 2008). ANN has the topological structures of information processing, distributed in parallel.…”

Section: Basis Of Artificial Neural Network Methodsmentioning

confidence: 99%

Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Li¹,

Jiang²,

Liu³

2013

WSA

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Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multidimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.

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“…ANNs are frequently used technique for predicting the process output for the given set of process parameter values. For example, Markopoulos et al (2008) used ANN to predict a surface roughness in electrical discharge machining. Benardos and Vosniakos (2002) used Taguchi´s design of experiments and ANNs to predict surface roughness in CNC face milling.…”

Section: Multiresponse Optimisation Based On Artificial Neural Networkmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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Artificial neural network models for the prediction of surface roughness in electrical discharge machining

Cited by 132 publications

References 17 publications

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

Standard Data-Based Predictive Modeling for Power Consumption in Turning Machining

Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Review of Multiresponse Process Optimisation Methods

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