Empirical power-consumption model for material removal in three-axis milling

Highlights

• Energy consumption of a vertical milling machine was decomposed.

• Material removal power was empirically modeled with respect to the process parameters and tool wear.

• The difference between power under severe-wear and slight wear conditions was modeled.

• Rotational speed, feed, and depth of cut simultaneously affect on the material-removal power.

• Appropriate process parameters could be selected considering the tool wear states.

Abstract

Environmental impact concerns have prompted various ecodirectives and legislation requiring manufacturing industries to take a greater role in reducing energy consumption and carbon emission. Research has indicated that monitoring and assessment of energy consumption is a basic approach that can be used to achieve energy savings. In this study, we decomposed the energy elements of a milling machine used for cutting to model additional material-removal power caused by the cutting load with respect to various process parameters. Because the material-removal power is directly related to the cutting force, current and power monitoring has been a good solution for indirect monitoring system. However, the effect of the process parameters on the material-removal power is more complex. In this study, the material-removal power consisted of ∼7.6% of the total power consumption of the machine tool, and increased with the flank wear of the tool. The difference between power under severe-wear and slight-wear conditions was empirically modeled using response surface methodology. The cutting power varied in terms of all process parameters, and its increase caused by the tool wear depended on the process conditions. Though both the material-removal power and its difference have a positive correlation with the material-removal rate, the absolute difference was not proportional to the material-removal power itself. With the constructed model, the overall energy consumption of the machine tool could be estimated more precisely, and the tool-wear states could be more accurately predicted in accordance with various process parameters.

Introduction

The energy crisis, caused by an imbalance between energy supply and demand, has been a reality for some time. In an effort to maintain or increase productivity, the industrial sector has become one of the major energy consumers (U.S. Energy Information Administration (EIA), 2013a). Moreover, energy-price policies have facilitated energy-consumption activities in the interest of economic development (Allcott, 2011). In July 2013, the average retail price for industrial electricity use was 7.32 cents per kWh, whereas residential and commercial electricity prices were 12.62 and 10.81 cents per kWh (U.S. EIA, 2013b).

A greater awareness of potentially harmful environmental practices has prompted various ecodirectives and legislation requiring energy-efficient manufacturing (Chiarini, 2012). In particular, cutting machine tools used in manufacturing processes consume significantly more energy compared with other types of machine tools, such as lasers or welding machines (Fraunhofer, 2012).

The basic energy-saving approach is to measure and assess the energy-consumption characteristics of the process being considered. The energy consumption of machine tools has been decomposed and modeled in accordance with the process parameters. In the study by Balogun and Mativenga (2013), the process states of the machine tool were classified into basic, ready, and cutting states; the total energy consumption was modeled in terms of the respective power elements and time. Salonitis and Ball (2013) divided the energy consumption into a background element, process element, and additional load element. Similarly, Yoon et al. (2013) decomposed energy consumption into basic energy (E_BASIC), momentum energy (E_STAGE+E_SPINDLE), and material removal energy (E_MACHINING).

The proportion of each energy element varies with respect to the type of machine tool and various process parameters. Because the basic energy consumption of a machine tool is constant, the total energy consumption is most affected by the process time. Therefore, many studies have revealed that the specific energy consumption (SEC) can be modeled as the reciprocal of the material-removal rate (Li and Kara, 2011).

Among the decomposed energy elements, the material removal energy (E_MACHINING) originates from the additional cutting load during the process. Many researchers have tried to model this cutting force or spindle torque component. According to Li and Kara (2011), this component consists of a specific tool-tip energy (STE) and specific unproductive energy (SUE). In practice, information on the energy consumption related to the cutting force is difficult to obtain. Typically, energy monitoring at the machine level uses basic sensing techniques (O’Driscoll and O’Donnell, 2013); however, machine tools are equipped with numerous auxiliary devices, which affect the total energy consumption.

Hence, the purpose of this research was to devise a means of monitoring the machine tool conditions in terms of the process parameters in an effort to model the energy-consumption components of the manufacturing process. A tool-condition monitoring (TCM) system can provide condition information for effective tool replacement, avoiding potential damage caused by severe tool wear or breakage (Cuppini et al., 1990). Among various sensor systems for TCM systems, power measurement has been considered as an economical monitoring solution. Power or current usually does not require complex measurement systems, and does not disturb the process, unlike a dynamometer (Teti et al., 2010).

Modern computer numerical control (CNC) systems offer information on spindle or motor power in terms of rated current values (Oliveira et al., 2008). Thus, the cutting power can be derived from this information. However, these current values are very sensitive and fluctuate with respect to the tool tooth movement (Shao et al., 2004); hence, appropriate mean power values must be used. Additionally, from the perspective of energy management, energy assessment is one of the tools available in computer-aided design/manufacturing (CAD/CAM) systems (Christoffersen et al., 2006). CAD/CAM provides an estimate of the overall energy-consumption values (Avram and Xirouchakis, 2011) with integrated consideration of the manufacturing cost (Yoon et al., 2012).

In this research, the energy consumption of a milling machine was decomposed into a basic element, momentum element, and material-removal element. The material-removal element was analyzed with respect to the tool wear. An increase in the material-removal energy consumption was observed following an increase in the tool wear. An empirical model was constructed in terms of the process parameters using the measured data. A set of experiments was performed with various process parameters. Because the variation of the material-removal elements originated from the cutting load and tool wear, the constructed model is applicable to estimations of tool-wear conditions and energy consumption/minimization practices.