Keywords

1 Introduction and Motivation

Machine tools are one of the central pillars of manufacturing, constituting about 70% of the total operating machines in the industry [1]. The industry has had a share of about 45% of the global electrical energy demand in 2020. The electricity demand of the industry has risen by about 30% between 2010 and 2020 and a further rise of another 35% by 2030 is projected, surpassing even the demand of the buildings sector [2]. While an increasing amount of energy supply is generated by renewable energy sources, total electricity generation is expected to continue to increase and along with it, CO2 emissions will continue to rise [3]. Reducing the energy demand in machine tools can therefore be a meaningful path to lowering the global electrical energy demand and have a positive environmental impact.

Reducing the power consumption of machine tools also reduces their operating costs and thereby gives them competitive advantages over conventional, inefficient machine tools. Operating costs are, however, only one criterion in the evaluation of machine tools. According to Schischke, machine tool users prioritize productivity, quality, accuracy, availability, service and price in the selection of machine tools with energy consumption being the least important criterion [4]. While increasing energy costs will likely make energy consumption more important in the future, energy reduction measures can only be successful in industrial applications if they do not come at significant expense of the former factors. This conflict is exacerbated by the fact that industrial applications increasingly require smaller lot sizes, a larger number of product variants and higher accuracy [5].

Fig. 1.
figure 1

Conflict between energy consumption, productivity and accuracy in machine tools

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Grossmann et al. have illustrated this conflict in Fig. 1 [6]. Though this chart is vastly oversimplified, it is generally true that high productivity makes it more difficult to maintain accuracy and increases the (operational) energy demand. The most significant influence on the accuracy is the thermal error. An industrial survey by Regel et al. among 75 machine tool manufacturers and users has determined that the thermal error makes up almost two thirds of the total positioning error of machine tools [7] and is thus significantly larger than geometric, static and dynamic errors. Moreover, the study revealed that even among the machine tool operators, the thermal error was believed to be almost as large as the other error components combined, showing that much of the thermal error is not being fully dealt with by the machine tool systems. Additionally, a significant portion of the thermal error stems from the tool and the workpiece [7].

Increasing productivity for a single cutting operation means higher feed rates and faster movements. (Above a machine-specific threshold,) this leads to larger dynamic errors and through higher waste heat also to larger thermal errors. The thermal waste heat comes mainly from friction, electric losses, hydraulic and pneumatic systems. The use of cooling systems and air-conditioning can remove some of the waste heat from the machine tool and thereby stabilize its temperature field and reduce the thermal errors at the expense of additional electrical energy.

To summarize this, higher productivity requires more energy to both run and stabilize the manufacturing process. This oversimplified view, that one cannot have high productivity without a large energy consumption is likely the main reason why productivity is first and energy demand is last among the criteria for the selection of machine tools [4]. The reality is far more complex, however. On the one hand, higher productivity actually typically decreases the overall energy consumption. On the other hand, the thermal error can be managed through a large number of compensation strategies, many of which have only negligible effects on the energy demand. Thus, this paper aims to show how high productivity and high accuracy can be achieved by energy-efficient production systems. This will be done by reviewing and evaluating both energy-efficiency measures and thermal error compensation strategies and by explaining their possible interactions and combined uses.

Section 2 will list some ways to improve the energy-efficiency of machine tools and explain their effect on the thermal error. Section 3 gives methods for reducing the thermal error in machine tools and their effect on the energy demand. Finally, a conclusion and outlook on future research topics will be given in Sect. 4.

2 Energy-Efficient Manufacturing Technology

Fig. 2.
figure 2

Power profile of a turning process [8]

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Figure 2 shows an example of a turning process [8] and demonstrates why most of the energy demand of machine tools is fixed, not directly correlated to the process related energy consumption. This energy is used for various support units such as the hydraulics (e.g. for cooling and lubrication, clamping or hydrostatic guides), compressed air (e.g. for bearings, pneumatics and sealing applications), chip removal, oil mist extraction, fluid preparation, machine control, human machine interface, evacuation and air conditioning. Thus, the most effective means of reducing the energy demand of machine tools is to increase the productivity during cutting operations to reduce the operating time and to shut off as many systems as possible during the non-productive phases.

There are a number of ways to reduce the machine tool operating time:

  • Increase the feed rate and/or depth of cutting,

  • Reduce air cutting motions (i.e. optimize CAM strategies),

  • Parallel processing (e.g. simultaneous NC operations, multi-spindle cutting, multiple workpieces in the workspace),

  • Optimized machine tool manipulation (e.g. faster or automated loading, unloading and setup of workpieces).

Of these measures, all but the first one, actually improve the thermal behavior of the machine tool, as they generally lead to more stable temperature fields. While the increased productivity will lead to more waste heat and thus larger deformations, these are easier to deal with using cooling strategies or model based compensation methods. Increasing the feed rate or depth of cut can similarly be beneficial, but it may also increase static and dynamic errors, increase tool wear, increase the thermal error components of the tool and the workpiece and also increase scrap in general. Nevertheless, optimizing cutting parameters to maximize productivity is sensible and there are numerous methods of dealing with the aforementioned issues, e.g. predicting the thermal errors of tool and workpiece, predicting the static deflection and dynamic operating limits of the machine tool and even estimating the remaining useful tool life [9,10,11,12]. Energy optimization via improved cutting parameters was investigated and achieved, e.g., by Li et al. [13] and Han et al. [14].

Another means of improved energy efficiency is energy-optimal enterprise resource planning (ERP). This includes choosing the right machine tools for the individual cutting tasks and optimizing the order of these tasks to make the most of energy-saving shutdown cycles of machine tools [15, 16]. This approach requires some flexibility in as well as knowledge of machine tools, operators and schedules but has the potential to significantly reduce the manufacturing related energy consumption without any additional cost in resources or decrease in product quality.

One of the most important methods for reducing the energy consumption of machine tools is to use demand-oriented supply systems. In the case of the cooling system, this can, e.g. be realized by using clocked compressors instead of a bypass to shut down the compressor when the coolant temperature is acceptable [17]. This can be done in a way that maximizes the shutdown time without affecting the thermal stability of the machine tool. Another important option is to use minimal quantity lubrication (MQL) or even dry machining to reduce the energy demand for cooling and processing of the cutting fluids. However, especially dry milling significantly increases tool wear and MQL typically requires air compressors to supply the large pressures needed and can thus create even larger CO2 emissions than flooded cooling (though overall MQL remains better environmentally) [18]. From a thermal point of view, flooded cooling is the most effective way to remove waste heat from the cutting process and thereby minimize the thermal errors of the machine tool, the tool and the workpiece. However, as previously stated, model based prediction algorithms can create good estimations of these deformations for MQL and dry milling and thereby eliminate most of these errors. Demand oriented bed flushing, evacuation and chip conveyor activation are likewise simple methods for improving the energy efficiency of machine tools. These measures can also affect the thermal behavior of the machine tool but their magnitude is limited and good model based compensation schemes must consider these effects in any case.

Last but not least is the energy-efficient design of machine tools and their components. This includes, e.g. using light-weight structures, using energy-efficient, demand-oriented supply systems, low-friction guides and bearings, regenerative breaking, counterweights and thermo-energetic machine tool design. Using light-weight structures has a significant effect on both thermal as well as dynamic dampening, which is why they are usually rare or restricted to certain components. Additionally, they make FEM simulation based compensation algorithms significantly more difficult to model and parametrize. On the other hand, all measures that reduce friction or the energy demand for moving machine tool assemblies also reduce the waste heat influx and thereby improve both the thermal as well as the energetic behavior of the machine tool. Thermo-energetic design such as thermo-symmetry, passive cooling structures or reducing the effects of waste heat on the TCP position can also be energy-efficiency measures as they allow the reduction or down-sizing of cooling systems and may shorten or eliminate lead times after machine tool startup or after production breaks [19].

3 Thermal Error Compensation

Following the understanding of Großmann et al. [20] thermal error compensation can be distinguished into design based, measurement based and model based methods. Design based methods aim to change or reduce the heat flow in the machine tool so that temperature gradients and the resulting displacements are reduced. These methods include, e.g., various cooling and air conditioning strategies, light-weight structures, material property optimization, thermo-symmetric design and the use of efficient drives and supply systems.

Measurement and model based methods, aim to predict the thermally induced deviation at the TCP and eliminate it via offsets in the machine tool control. Both methods have minimal impact on the energy consumption of the machine tool. However, they have the potential to reduce the amount of cooling needed, thereby decreasing the energy consumption of the cooling system of the machine tool. This in turn can increase the overall energy efficiency of the machine tool. Most of these algorithms can be used in conjunction with the above mentioned design based methods as well as with many of the concepts listed in the previous chapter. It is, however, usually necessary to adjust these prediction algorithms to any method which significantly alters the thermal behavior of the machine tool. Algorithms based on FEM simulations or transfer functions must be expanded to include any additional heat sources and sinks and must be able to reproduce, e.g. load profiles for demand-oriented cooling strategies. Regression based algorithms may require altered load cases for model training as well. Interesting in this respect is the compensation using integrated deformation sensors, which has design based limitations in its usage but can generally handle almost all of the energy efficiency improvement measures without any adjustment or consideration [21].

In the following, two approaches will be presented that can support an energy efficient control of cooling systems. Since cooling systems account for a large part of the machine tool’s energy consumption, efforts should be made to achieve demand-oriented cooling [22].

Wenkler et al. presented an approach to reduce thermal changes in the machine tool by grouping machining processes depending on their caused power losses [23]. Their strategy is based on loss predictions for machining tasks, by analyzing the G-code of a process and predicting the machining-specific loss trajectories. These trajectories can then be arranged so that the loss jumps in the process transitions are minimal. On the one hand, the concentration of loss troughs and peaks directly reduces the thermal dynamics. On the other hand, the concentrated loss peaks can be cooled more efficiently, since the thermal gradient from machine tool to coolant is greater and there are also larger areas in which the cooling system can be switched off or its power consumption can be reduced. Their experimental results reveal a potential reduction of about 10% in thermal changes concerning the segmentation approach. The strategy is currently partially automated but could be fully automated for an industrial application in the future.

A second approach is presented by Shabi et al. [24] aimed at optimizing the cooling strategies. The investigation of two demonstrator machines has shown that the energy consumption of the cooling and lubrication systems on the DBF630 machining center amounts to 44% and on the DMU80 eVo machining center to 45% of the total energy consumption of the machine tools [25]. Thus, there is a great potential for reducing the energy consumption and increasing the efficiency of the machine tools by optimizing the operation of the fluid power systems. Because of the rather inefficient activation procedures of conventional cooling systems, the temporal concentration of cooling relevant areas can lead to a decrease in the activation number and therefore increase the overall efficiency of the cooling system while maintaining thermal stability. To use this potential, optimized fluid power system structures were developed by Shabi in 2020 [26]. Test results of the cooling system on the demonstrator DBF630 in the production process have proven that sufficient cooling capacity is available. Simulation results of the developed cooling system structures have shown that a stable temperature field can be achieved compared with the initial state. In addition, the flow supply leads to an improvement in the hydraulic performance of the pumps. The energy consumption of the pumps used with the new structures, is about 53–70.5% lower than that of the current cooling structures.

4 Conclusion and Outlook

The reduction of the energy demand in manufacturing can significantly impact man-made CO2 emissions and thus aid in the struggle against global warming. One major contributor to production related energy consumption are machine tools, which can be viewed as the workhorse of the industry. Currently many machine tool users value productivity, accuracy and price over the energy-efficiency. There are, however, numerous ways to satisfy these key criteria while employing energy-efficient machine tool designs.

Thus far, many options for lowering the energy demand of machine tools have been suggested as well as methods to help them in dealing with thermal issues. The question of what measures specifically need to be taken to achieve energy-optimal, accurate manufacturing, remains unanswered. This is mainly because the application (workpiece, machining technology, …) and the circumstances (new design vs. existing machine, factory conditions, …) determine the most suitable strategies.

For existing machine tools, barring the option of a major retrofit, design alterations are not possible. Therefore, the best way to save energy will be to optimize the usage of all available machine tools by selecting the best one for each job and keeping them shut off as much as possible. At the same time, the productivity should be maximized with high feed rates and optimized CAM paths while also employing demand-oriented supply systems to the extent that the existing hardware allows. This will definitively impact the thermal error, so that model based compensation algorithms should be employed to maintain the required accuracy. All of these solutions are purely software based and require at most additional sensors (e.g. temperature sensors).

For new machines, the full suite of measures is available and it is important to keep in mind, that changing thermal properties may impact the mechanical properties (especially dynamic behavior) and that the machine tool should be considered together with its factory and the intended process chains. In this context, the Internet of Things has much to offer, so that hardware and software interfaces are also important. Energy-efficient designs and energy-optimal components should be part of all new machine tool designs as far as they are affordable. The design choices should take thermal issues into consideration, which preferably are done via thermo-elastic FEM simulations. After that, the same methods suggested for existing machine tools, will be effective. The most relevant gap in the state of the art is the lack of interconnection between these methods. In the future, ERP, CAM and the NC control must all cooperate to maximize the effects of these energy-efficiency measures for any given workpiece.

By examining the interactions of the thermal accuracy of a machine tool with its energy efficiency, particularly via methods and devices that affect these critical goals, the authors have demonstrated many synergies between these seemingly conflicting goals and also possible solutions in cases where increased energy-efficiency truly leads to larger thermal errors, when these are not otherwise dealt with.