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

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 literatures 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 literatures, which can be explained by the inability of the coefficients from literatures to match the specific machining conditions. When the coefficients are obtained from literatures, 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 2 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.

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

“…Therefore, many researchers have highlighted the optimization of machining parameters. For example, Lv et al [12] carried out an investigation into methods for predicting material removal energy consumption in turning. Jia et al [13] established prediction models for feeding power and material drilling power to support sustainable machining.…”

Energy performance certification in mechanical manufacturing industry: A review and analysis

“…Jia presented some energy models from the perspective of the therbligs in machining [30,31]. Lv proposed a method for reducing energy loss during machining operations [32] and established the energy prediction model in machining [33]. Yoon performed decomposing of energy elements of machine tool and presented an empirical model of energy consumption in milling.…”

A big data driven analytical framework for energy-intensive manufacturing industries

“…Whereas, the analysis models above are all researched theoretically. In practice, Lv et al (2016Lv et al ( , 2018 have investigated the energy characteristics and the power models of the computer controlled machine tools through experimental studies. The results showed that the energy-saving potential of machining process was tremendous.…”

“…self-organizing and self-adaptive model (Y. Zhang et al, 2017a); ALC method (G. Zhang et al, 2017); smart control model (Zhang et al, 2018b); smart production-logistics systems (Zhang et al, 2018a); experimental study (Lv et al, 2016(Lv et al, , 2018 √ √ Big data can improve manufacturing (Auschitzky et al, 2014); big data for enterprise management (Rabl et al, 2012); √ √ √ Big data for cleaner production (Song et al, 2017;Zhang et al, 2017c); call for papers (Z. 2.3 Knowledge gaps From this review, although the significant process has been made in the two research dimensions mentioned above, as shown in Table 1, there are still some gaps that need to be filled in.…”