Integrated machining error compensation method using OMM data and modified PNN algorithm

Cho, Myeong-Woo; Kim, Gun-Hee; Seo, Тае-Il; Hong, Yeon-Chan; Cheng, Harry H.

doi:10.1016/j.ijmachtools.2005.10.002

Cited by 79 publications

(29 citation statements)

References 14 publications

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“…Therefore, in this study, the modified PNN algorithm terminating method is employed, which uses variable decreasing threshold values as increasing layers. 9,10 This process is shown in Fig. 3.…”

Section: Modified Pnn Algorithmmentioning

confidence: 96%

Predicting of cutting forces in a micromilling process based on frequency analysis of sensor signals and modified polynomial neural network algorithm

Hong

Cho

2012

Int. J. Precis. Eng. Manuf.

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Recently, with increasing demand for precise micro-components productions, the importance of micro machining processes is increasing in many fields, including the automotive, aerospace engineering, medical instruments and computer industries. However, compared with macro machining processes, it is very difficult to observe the machining process due to its low MRR (material removal rate), very small tool size, high speed spindle, and low sensor signals levels, etc. Micro tool dynamometer can be a solution for this; however, its applications are limited due to the expense, sensitivity, robustness, and workpiece size. Thus, in the present study, a useful indirect cutting force measurement method involving an acceleration sensor and current hall sensor is proposed. A series of experiments were performed on a precise micro machining stage. Measured signals were analyzed in the frequency domain after FFT (Fast Fourier Transform), and the results were compared with the cutting force components measured via the acceleration sensor and current hall sensor, respectively. From the results, it could be verified that the proposed indirect cutting force measurement method is a useful way to monitor the micro end-milling processes. Finally, to predict the cutting forces in micromilling processes, the modified polynomial neural network (PNN) and the back-propagation neural network are compared.

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Section: Modified Pnn Algorithmmentioning

confidence: 96%

Predicting of cutting forces in a micromilling process based on frequency analysis of sensor signals and modified polynomial neural network algorithm

Hong

Cho

2012

Int. J. Precis. Eng. Manuf.

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“…Final execution therefore aims to remove any remaining excess material. Iterative compensation is backed up by several studies to manage inherent measurement error [63][64][65][66][67][68]. …”

Section: Program Correctionmentioning

confidence: 99%

Development and testing of an error compensation algorithm for photogrammetry assisted robotic machining

2016

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“…Compensation can relate to the chosen errors or the total error. Typical approach to the error compensation includes building the mathematical models taking into account the data from the sensors placed on the machine tool in on-machine-measurement (OMM) system (Ziegert and Kalle 1994;Ouafi et al 2000;Cho et al 2006;Marinescu et al 2011), by the reconstruction of the control program (Mäkelä et al 2011;Cui et al 2012) and more.…”

Section: Introductionmentioning

confidence: 99%

A metaheuristic for fast machining error compensation

Stryczek

2014

J Intell Manuf

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In case of complex parts machining or multidirectional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

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Integrated machining error compensation method using OMM data and modified PNN algorithm

Cited by 79 publications

References 14 publications

Predicting of cutting forces in a micromilling process based on frequency analysis of sensor signals and modified polynomial neural network algorithm

Predicting of cutting forces in a micromilling process based on frequency analysis of sensor signals and modified polynomial neural network algorithm

Development and testing of an error compensation algorithm for photogrammetry assisted robotic machining

A metaheuristic for fast machining error compensation

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