Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’

Luttervelt, C.A. van; Childs, T.H.C.; Jawahir, I.S.; Klocke, Fritz; Venuvinod, Patri K.; Altintaş, Yusuf; Armarego, E.J.A.; Dornfeld, D.; Grabec, Igor; Leopold, Jürgen; Lindstrøm, Bo; Lucca, D.A.; Obikawa, Toshiyuki; Shirakashi,; Sato, Haru–Tada

doi:10.1016/s0007-8506(07)63244-2

Cited by 318 publications

(106 citation statements)

References 69 publications

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“…[1] Van Luttervelt, C.A., Childs, T.H.C., Jawahir, I.S., Klocke, F., Venuvinod, P.K., Altintas, Y., Armarego, E., Dornfeld, D., Grabec, I., Leopold, J., Lindstrom, B., Lucca, D.,Obikawa, T., Shirakashi, Sato, H. (1998). Present situation and future trends in modelling of machining operations progress report of the CIRP working group 'Modelling of machining operations',…”

Section: Referencesmentioning

confidence: 99%

Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey wolf optimizer algorithm

Sekulić¹,

Pejić²,

Brezočnik³

et al. 2018

Adv produc engineer manag

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In this research study proposed are a response surface methodology (RSM), genetic algorithm (GA) and a grey wolf optimizer (GWO) algorithm for prediction of surface roughness in ball-end milling of hardened steel. The RSM is a conventional predicting approach, GA is an evolutionary algorithm and GWO is a new swarm intelligence-based algorithm. Spindle speed, feed per tooth, axial depth and radial depth of cut were selected as input parameters. Experiments were performed on a CNC milling center and experimental data were collected based on a four-factor-five-level central composite design (CCD). RSM was applied for establishing the basic relationship between input parameters and surface roughness. After that analysis of variance (ANOVA) was conducted for the evaluation of the proposed mathematical model. A predefined reduced quadratic model was used as a reference model for a build-up of predictive models using GA and GWO algorithm. Predicted values of RSM, GA and GWO models are compared with experimental results. In the comparison of model performance for all the three models it was found that GWO model is the best solution. The model accuracy was found to be at 91.80 % and 89.58 % for training and testing data, respectively, which showed the effectiveness of the GWO algorithm for modeling machining processes. References[1] Van Luttervelt, C.A., Childs, T.H.C., Jawahir, I.S., Klocke, F., Venuvinod, P.K., Altintas, Y., Armarego, E., Dornfeld, D., Grabec, I., Leopold, J., Lindstrom, B., Lucca, D.,Obikawa, T., Shirakashi, Sato, H. (1998). Present situation and future trends in modelling of machining operations progress report of the CIRP working group 'Modelling of machining operations',

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Section: Referencesmentioning

confidence: 99%

Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey wolf optimizer algorithm

Sekulić¹,

Pejić²,

Brezočnik³

et al. 2018

Adv produc engineer manag

View full text Add to dashboard Cite

show abstract

“…A recent review [9] has indicated that the present state of machining process modelling cannot provide reliable solutions to most machining problems. A need was suggested [9] for augmenting process modelling with sensing and learning (through ANN, etc.).…”

Section: Synergy Between Modelling and Learning Through The Use Of Anmentioning

confidence: 99%

“…A need was suggested [9] for augmenting process modelling with sensing and learning (through ANN, etc.). In such synergy, modelling would capture a deep understanding of the portions of the process for which the cause-effect relationships have been described analytically.…”

Section: Synergy Between Modelling and Learning Through The Use Of Anmentioning

confidence: 99%

Predicting Machining Errors in Turning Using Hybrid Learning

Venuvinod

Djorjevich

et al. 2001

Int J Adv Manuf Technol

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“…The second one is numerical approach which deals with fundamental mechanics and physics of metal cutting concentrating tool tip contact zone and interaction of tool-workpiece pair [9], [10]. Recently, Altintas et al [11] developed a simulation model of metal cutting process in a more generalized way using ALE (Arbitrary Lagrangian Eulerian) method.…”

Section: Introductionmentioning

confidence: 99%

A Method to Determine Cutting Force Coefficients in Turning Using Mechanistic Approach

Bera¹,

Manikandan²,

Bansal³

et al. 2018

IJMMM

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Abstract-During performing turning operation, cutting force plays a significant role in metal cutting process affecting tool-workpiece deflection, machine tool vibration and eventually part quality. The present research work aims to develop a mechanistic cutting force model that will be used in development of tool-workpiece flexible system of thin-wall machining. It also concentrates to study the mechanistic constants used in the force model in case of turning operation. The proposed model can be used for the reliable and accurate estimation of the cutting forces establishing relationship of various force components (cutting force and feed force) with uncut chip thickness. The accurate estimation of cutting force is required to improve thin-walled part accuracy by controlling the tool-workpiece deflection induced surface errors and tool-workpiece vibration.

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Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’

Cited by 318 publications

References 69 publications

Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey wolf optimizer algorithm

Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey wolf optimizer algorithm

Predicting Machining Errors in Turning Using Hybrid Learning

A Method to Determine Cutting Force Coefficients in Turning Using Mechanistic Approach

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