Integrated energy analysis of cutting process and spindle subsystem in a turning machine

Abstract

This paper presents a novel approach for systematic energy efficiency evaluation and optimization in turning operations, combining spindle, chiller and material removal models. Framing a joint machine-process design approach, the proposed study aims at selecting optimal combinations of cutting parameters (feed rate, depth of cut and spindle speed) for a given spindle-chiller assembly, able to minimize the energy consumption. Contrary to most of the literature, where the efficiency analysis is fully empirical, relying on extended cutting test campaigns, here a model-based approach is adopted. The goal is to characterize a key subsystem of modern machine tools, often used in both turning and milling machines, composed by a permanent magnet brushless direct-drive spindle with a dedicated chiller unit. Analytical relationships are identified, producing efficiency maps as a function of various process parameters. Physic-based models are exploited, reproducing electrical and mechanical energy dissipation occurring in the spindle and chiller units and in the material removal process. The models parameters are identified by a reduced set of spindle ramp-up and cutting tests, executed in an industrial context. Then, an overall process efficiency optimization is performed and discussed.

Cross-level model of a transfer machine energy demand using a two-machine generalized threshold representation

“…These works show a clear relation between used technological parameters and cycle time and energy use. Similar conclusions can be drawn from the literature on power demand of turning operations (Wójcicki et al, 2018b). As scientific studies typically concern one level of analysis, the attempt to optimize a system globally, starting at a machine level (where the machining process is executed) and then using the obtained work-point in a system optimization, inherently brings to a globally suboptimal solution, because of the unresolved coupling between levels.…”

“…A common approach in modelling machines at system level is using discrete-event models, in which a Machine Tool (MT) is represented by means of a state machine diagram, with a different given power consumption for every machine state. While a constant power assumption is a good approximation for non-processing states, during machining, power consumption varies and can strongly depend on selected processing parameters, such as material removal rate (MRR), feed or depth of cut (Wójcicki et al, 2018b). Given the probability distribution of switching from one state to another, one can derive the average energy consumption of a MT or the entire system.…”

Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints

“…Another interesting observation from literature review is that the spindle system is responsible for a significant portion of energy consumption of machine tools [7,12,21]. Moreover, the energy consumption characteristics of the whole machine tools depend on the working condition of spindle system to a certain extent [22]. Hence, the energy optimization of spindle system is one of the most profitable strategies for improving energy efficiency of machine tools.…”

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

“…Campatelli et al [8] employed an experimental approach to model the energy consumption in the milling of AISI 1050 carbon steel, and claimed that increasing the material removal rate (MRR) as much as possible will help reduce energy consumption during the processing. Wójcicki et al [9] adopted a model-based approach for a systematic energy efficiency evaluation and optimization during turning operations, combining a spindle, chiller, and material removal models, and taking into account the strong interrelations between the cutting process, a spindle with a permanent magnet motor, and its chiller. Zhong et al [10] considered the effects of the cutting parameter combinations on the energy consumption at a certain material removal rate, and based on this discovery, considered a specific energy consumption as the optimization goal and took the cutting parameters as the optimization variables during the material removal process during the turning; cutting parameter sets with a large feed rate were then recommended.…”