Modelling and Prediction of Surface Roughness and Power Consumption Using Parallel Extreme Learning Machine Based Particle Swarm Optimization

Ahmad, Nooraziah; Janahiraman, Tiagrajah V.

doi:10.1007/978-3-319-14066-7_31

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…Yang et al [87] applied the parallel algorithm to ELM and proposed an incremental ELM based on the parallel chaos search (PC-ELM), which is used to discover hidden nodes in the network output. Ahmad et al [88] proposed a parallel ELM (PIPSO-ELM) based on particle swarm optimization for modeling and prediction of surface roughness and power consumption in manufacturing. PIPSO-ELM is divided into two separate algorithm blocks, each representing surface roughness and power consumption, and then the two basic ELM based performance models are combined with the selected input weight and the hidden bias of PSO.…”

Section: Othersmentioning

confidence: 99%

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

et al. 2020

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Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paper first introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.

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Section: Othersmentioning

confidence: 99%

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

et al. 2020

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“…The prediction result often ends up being unsatisfactory because the ELM input weight and hidden bias are always chosen randomly. The author in [20] discusses the improved variant of ELM, where the weights from input layer to hidden layer were optimized using Particle Swarm Optimization (PSO). Experimental results in [20] shows that improved ELM can produce the best performance based on the ELM architecture.…”

Section: � � � (4)mentioning

confidence: 99%

“…The author in [20] discusses the improved variant of ELM, where the weights from input layer to hidden layer were optimized using Particle Swarm Optimization (PSO). Experimental results in [20] shows that improved ELM can produce the best performance based on the ELM architecture. The advantage of combining ELM with PSO includes the fact that only minimum parameter is needed to be adjusted.…”

Section: � � � (4)mentioning

confidence: 99%

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Multi Objective Optimization for Turning Operation using Hybrid Extreme Learning Machine and Multi Objective Genetic Algorithm

Janahiraman¹,

Ahmad²

2018

IJET

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Turning operation, a type of machining process using Computer Numerical Control (CNC) machine in which a cutting tool, typically a non-rotary tool bit, moves to describe a helix toolpath while the cylindrical metal workpiece rotates. Numerous conflicting performance functions such as maximizing material removal rate, minimizing the product’s quality, maximizing the tool life and others, remains crucial for a system to optimize in order to obtain optimum benefit. The machinist is required to assign the optimal cutting parameters in CNC turning machine which have direct influence on the performance of each cutting process and machined product. It is very crucial for optimal parameters selection to maximize the performance function. A new optimisation model has been proposed in this paper. This model, uses Box Behnken Design (BBD) for design of experiment and the prediction model has been developed using Extreme Learning Machine (ELM) which is tuned using Particle Swarm Optimization. A powerful and effective, Multi Objective Genetic Algorithm (MOGA) will act as an optimizer of the developed model. Turning input parameters such as feed rate, cutting speed and depth of cut were considered as input variables and surface roughness, specific power consumption and cutting force were used as output variables. This novel approach, BBD-ELM-PSO-MOGA can predict the optimal cutting parameters as demonstrated in our case studies with less number of tunable parameters and number of experiments. Therefore, it is fast, less time consuming and easy to be implemented.

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“…Hence, the PSO technique is utilised as the solution for this problem. The details of the modelling method has been explained in the authors' research paper [21]. The advantage of combining ELM with PSO includes the fact that only minimum number of parameters are needed to be adjusted.…”

Section: A Minimum Training Errormentioning

confidence: 99%

The Optimisation of Surface Roughness using Extreme Learning Machine and Particle Swarm Optimization

Janahiraman¹,

Ahmad

Abdullah³

2018

IJMERR

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In recent days, metal cutting has become a highly demanding sector due to growing applications. The turning process is one of the metal cutting processes which produces circular shapes from a cylindrical bar. Currently, turning operation is conducted using computer numerical control machine (CNC). The machinist is required to assign the optimal cutting parameters in CNC turning which have direct influence on the performance of each cutting process. Therefore, it is crucial to achieve the optimal parameters before the process is started. In usual cases, these parameters will be assigned according to machinist's past experience or with reference to the manual handbook provided by the tool supplier. However, this approach can be considered time consuming and do not guarantee that it can produce the desired cutting performance. In light of this issue, a new optimisation technique has been proposed to figure out the optimal cutting parameters. Box Behnken's design is used as the experimental design, while the improved Extreme Learning Machine which is based on Particle Swarm Optimisation is proposed as the prediction model. A powerful and effective, Particle Swarm Optimisation will act as the optimiser of the prediction model. The turning parameters: cutting speed, feed rate and depth of cut, are considered as the input variables to the model. The optimisation results prove that the system is able to predict well and generate optimal cutting parameters to minimize the surface roughness of the machined workpiece.

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Modelling and Prediction of Surface Roughness and Power Consumption Using Parallel Extreme Learning Machine Based Particle Swarm Optimization

Cited by 8 publications

References 8 publications

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

Multi Objective Optimization for Turning Operation using Hybrid Extreme Learning Machine and Multi Objective Genetic Algorithm

The Optimisation of Surface Roughness using Extreme Learning Machine and Particle Swarm Optimization

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