Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel

Campatelli, Gianni; Lorenzini, Lorenzo; Scippa, Antonio

doi:10.1016/j.jclepro.2013.10.025

Cited by 184 publications

(75 citation statements)

References 30 publications

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“…Otherwise, the consideration of rapid movements may increase the complexity of the analysis of power consumption. Also, power consumption during rapid movements is not much considered because the power required for running axial component systems is confined to a small portion of total power on a machine tool [22,23]. In our view, it is necessary to analyze the power consumption in terms of tool path trajectories given by a Numerical Control (NC) program in order to increase the usability of predictive models for the power consumption in a machining shop.…”

Section: Background Of Studymentioning

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: Background Of Studymentioning

confidence: 99%

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

et al. 2018

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“…Triggered by the necessity to improve the energy efficiency and environmental performance of the manufacturing industry, energy modeling [11][12][13][14][15][16], energy-efficiency improvement [17][18][19][20][21][22] and carbon-emission reduction [23,24] of the manufacturing industry have been studied. Experiments show that power peaks will be caused by state transitions during the machining process [25,26], as shown in Figure 1. State transition indicates the transition process between the two neighboring states during the machining process, such as spindle startup, rapid positioning acceleration, coolant startup, tool change startup, etc.…”

Section: Introductionmentioning

confidence: 99%

“…To fill this gap, an energy demand modeling method for state transition of the turning process is proposed in this paper. Power curve during an actual machining process [25].…”

Section: Introductionmentioning

confidence: 99%

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Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

et al. 2017

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Abstract:Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

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“…Kant and Sangwan [12] used artificial neural network coupled with Genetic Algorithm for predictive modelling and optimization of machining parameters to minimize surface roughness. Campatelli et al [13] used the response surface method to optimize the process parameters for minimizing power consumption in the milling of carbon steel. The research work mentioned above by Jie Yi et al [3] used the conventional machine tool.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling to predict the roughness average in micro milling process

Burlacu

Iordan

2016

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Surface roughness plays a very important role in micro milling process and in any machining process, because indicates the state of the machined surface. Many surface roughness parameters that can be used to analyse a surface, but the most common surface roughness parameter used is the average roughness (R a ). This paper presents the experimental results obtained at micro milling of the C45W steel and the ways to determine the R a parameter with respect to the working conditions. The chemical characteristics of the material were determined from a spectral analysis, chemical composition was measured at one point and two points, graphical and tabular. A profilometer Surtronic 3+ was used to examine the surface roughness profiles; the effect of independent parameters can be investigated and can get a proper relationship between the R a parameter and the process variables. The mathematical model were developed, using multiple regression method with four independent variables D, v, a p , f z ; the analysis was done using statistical software SPSS. The ANOVA analysis of variance and the F-test was used to justify the accuracy of the mathematical model. The multiple regression method was used to determine the correlation between a criterion variable and the predictor variables. The prediction model can be used for micro milling process optimization.

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Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel

Cited by 184 publications

References 30 publications

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

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

Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

Mathematical modelling to predict the roughness average in micro milling process

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