Experimental modelling and optimization of turning medium carbon steel

Hassan, G. A.; Suliman, S. M. A.

doi:10.1080/00207549008942775

Cited by 18 publications

(15 citation statements)

References 6 publications

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“…The consumed energy can be approximated by the product of the specific energy consumption (SEC) and the total volume of material removed [16,17] or by the product of power demand and processing time [18], therefore, the output responses often vary. No universal input-output and in-process parameter relationship models are applicable to all metal cutting processes [19]. In order to acquire the whole energy consumption during an entire metal cutting process, the energy consumption of each component needs to be represented by individual calculation models.…”

Section: Introductionmentioning

confidence: 99%

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Zhong

Tang

Lv³

et al. 2015

Int J Adv Manuf Technol

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The manufacturing industry has been focusing on calculating energy consumption because of increased sustainability and environmental consciousness, resulting in a large number of calculation models. However, evaluation on these existing models is missing, so that practitioners often have trouble selecting the right energy models. To identify the most appropriate, cost-effective, and easy-to-implement energy models, three tasks were conducted in this paper. First, various experimental or empirical energy consumption models of spindle acceleration, spindle rotation, feed and material removal, and those of machine tool during material removal processes are reviewed. Then, five evaluation criteria, applicability, accuracy, computational efforts, complexity of fitting coefficients, and ease of data collection, are proposed. These criteria are identified based on the general steps to run the calculation models. Finally, the evaluation criteria are applied with the supporting experimental data of external turning to rank the models. This work is expected to assist practitioners in selecting models to calculate energy consumption in metal cutting processes for a particular situation and purpose.

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Section: Introductionmentioning

confidence: 99%

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Zhong

Tang

Lv³

et al. 2015

Int J Adv Manuf Technol

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“…Sekhon (1982) proposes an optimization algorithm, based on DP approach to solve a four-stage machining operation problem when machine variables are considered to be discrete in nature. Hassan and Suliman (1990) illustrate the use of regression analysis with which prediction of surface roughness, tool vibration, cutting time, and power consumption is made prior to obtaining optimal cutting conditions by Powell's method (Powel, 1964). Agapious (1992a,b & c) suggests DP technique to formulate single and multi-pass machining operations.…”

Section: Iterative Mathematical Search Techniquementioning

confidence: 99%

“…Hassan & Suliman (1990) use a second order multiple-regression model for medium carbon steel turning operation. Feng (Jack) and show that for a reasonable large data set, regression analysis generates results comparable to artificial neural network-based modelling for surface roughness prediction in finished metal turning process.…”

Section: Statistical Regression Techniquementioning

confidence: 99%

“…Optimization tools and techniques proposed are also based on Taguchi method (Ross, 1989), response surface design (Montgomery, 2001), mathematical programming (Hillier & Liebermann, 1999), genetic algorithm (Goldberg, 2002), tabu search (Glover, 1990), and simulated annealing (Kirkpatrick, Gelett, & Vecchi, 1983). Despite numerous studies on process optimization problems, there exists no universal input-output and inprocess parameter relationship model, which is applicable to all kinds of metal cutting processes (Hassan & Suliman, 1990). Luong & Spedding (1995) claim a lack of basic mathematical model that can predict cutting behaviour over a wide range of cutting conditions.…”

Section: Introductionmentioning

confidence: 98%

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A review of optimization techniques in metal cutting processes

Mukherjee

Ray

2006

Computers & Industrial Engineering

467

197

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“…Optimization tools and techniques proposed are also based on Taguchi method [6], response surface design [7], mathematical programming [8], genetic algorithm [9], tabu search [10], and simulated annealing [11]. Despite of numerous studies on process optimization problems, there exists no universal input-output and in process parameter relationship model, which is applicable to all kinds of metal cutting processes [12]. Luong & Spedding [13] claim a lack of basic mathematical model that can predict cutting behavior over a wide range of cutting conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Cutting Force and Surface Roughness Based on Taguchi Technique for AA6061

Yahya¹,

Salim²

2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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Abstract-Optimization of machining parameters has great concerned in manufacturing environments, there for in this study cutting force and surface roughness are optimized based on machining parameters. Many researchers have established the relationship between the surface roughness and machining parameters, but less attention has been paid to tool shape and geometry, in this study tool flute is first time used as machining factor beside cutting speed, depth of cut and feed rate. Response Surface Method and analysis of variance are adopted to establish the relationship of machining parameters to the surface roughness and cutting force using Taguchi technique. Optimization of cutting force and surface roughness are simultaneously adopted, either by optimized cutting force or surface roughness. The minimum surface roughness obtained is 0.2398 µm with corresponding cutting parameter is 12.01 N.

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Experimental modelling and optimization of turning medium carbon steel

Cited by 18 publications

References 6 publications

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process

A review of optimization techniques in metal cutting processes

Optimization of Cutting Force and Surface Roughness Based on Taguchi Technique for AA6061

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