An investigation into methods for predicting material removal energy consumption in turning

Highlights

• Three methods can be used to predict material removal energy.

• The accuracy and implementation difficulty of three methods are investigated.

• The coefficients for the methods can be obtained experimentally.

• The cutting force based method can provide the most accurate power prediction.

• The methods should be selected according to the required accuracy in industry.

Abstract

The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literature and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literature, which can be explained by the inability of the coefficients from literature to match the specific machining conditions. When the coefficients are obtained from literature, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process.

Introduction

Machining is widely applied in manufacturing industry and contributes to a significant portion of employment and economic growth. Unfortunately, machining also imposes large environmental burden due to energy consumption and related carbon emissions (Liu et al., 2016, Zhang et al., 2017). Many approaches are developed to save energy consumed during machining, such as energy efficient process planning and scheduling. However, the lack of accurate energy data has impeded the implementation of the aforementioned approaches (Hu et al., 2015, Wang et al., 2015). Therefore, accurate prediction of energy consumption in machining is of great importance.

Turning is the one of the most important machining processes and can produce a wide variety of parts. Considering large number of lathes used in manufacturing and the low energy efficiency, there have been significant potential in improving the energy efficiency of turning process. Consequently, it is important to forecast the energy use in turning, which will assist the process designers and machine operators to achieve energy efficient process design and operating.

The total energy during machining can be subdivided into three parts: the standby energy use, run-time operational energy and actual energy involved when removing material (Dahmus and Gutowski, 2004). The detailed energy flow in machining process in shown in Fig. 1. It is vital to investigate the material removal energy, since it is responsible for the new surface generation and determines the quality of a machined part (Sealy et al., 2016). There are three representative methods to predict the material removal power in existing research: specific energy based method (SEM), cutting force based method (CFM) and exponential function based method (EFM). The SEM considers the material removal power to be the product of the specific cutting energy and material removal rate (MRR). The CFM calculates the material removal power by multiplying the cutting force by cutting speed. The EFM predicts the material removal power using an exponential function of cutting parameters.

The above three methods are widely used due to their easy application in engineering. In the three methods, many assumptions and simplifications have been made. The SEM and CFM consider that the material removal power is equal to cutting power, which is the power consumed through the tool tip to remove workpiece material. Actually, the material removal power also includes another part of the power called the loading loss which could reach up to 26% of the cutting power (Xie et al., 2016a). For the SEM, the material removal power is considered to be proportional to MRR, which means that material removal power is proportional to cutting speed, feed and depth of cut. However, this may not be true because the effects of each parameter on material removal power are not linearly proportional. Moreover, research showed that the specific energy is not a fixed value, but affected by the hardness and microstructure of the work material, feed rate, rake angle of the cutting tool (Boothroyd and Knight, 1989). In the CFM and EFM, cutting force and material removal power are assumed to be exponential models of cutting parameters. The assumptions may lead to inaccurate power prediction and costly errors in judgement which parameters are selected to reduce energy consumption for machining operations. There is an urgent need to evaluate the prediction accuracy of these methods.

This study was oriented to evaluate the material removal power prediction accuracy of existing methods based on experimental data. Although the focus is on the turning processes, the proposed studies can be used by any other machining processes, such as milling, drilling and grinding. The remainder of this paper is organized as follows. Section 2 reviews related work, and Section 3 introduces the three methods for predicting material removal power, concept of uncertainty and prediction accuracy. The methodology to acquire the model coefficients from literature and experiments is described in Section 4. An evaluation of the three models is discussed in Section 5. The selection of cutting parameters for energy reduction based on the accurate power prediction is illustrated in Section 6. Finally in Section 7, the conclusions are drawn and future work is discussed.