Keywords

1 Introduction

In view of increasing ecological awareness, rising production costs and strict political demands for the sustainable use of resources and the reduction of greenhouse gas emissions, companies are increasingly moving towards environmentally friendly production. According to a study by the German Federal Association of Energy and Water Industries, approximately 45% of total electricity consumption in Germany was used by the industry sector in 2020 [1]. Of this, metal production and processing, which includes the automotive industry, account for a significant share of about 23% [2]. In average more than two thirds is used by MT [3]. Therefore, MT are a interesting use case for energy optimization.

There are various optimization approaches, most of which relate to specific operating states of MT [4]. The productive operating state, with the longest runtime in series production and highest power consumption, has the greatest savings potential [5]. However, manipulations in productive operating state can affect the functionality of the machine. In series production, this can have an impact on the entire production chain. Monitoring of important key performance indicators (KPIs) to ensure the production goals must take place to safeguard the process. The steps to be taken for a successful implementation of such an optimization will be discussed and tested in this paper. The following question is to be answered: What steps are necessary for successful closed-loop optimization in series production? This leads to two further questions. How is the connection to the machine organized? How can the KPIs be determined and tracked?

Section 2 explains the technical environment of the optimization, as well as important production and control fundamentals. Section 3 gives an overview of already implemented energy optimization approaches on MT. The applicability to the considered technical context will be discussed. Section 4 presents a possible approach to realize energy optimization on MT in series production. All necessary steps are analyzed in detail. Section 5 contains the implementation and evaluation of the approach on a grinding machine in serial production. Section 6 finally summarizes the results and discusses the generality of the approach.

2 Technical Context

In the following, the technical environment and MT as an object of optimization are examined in more detail. Production and control fundamentals are explained and requirements for the optimization procedure are identified.

2.1 Series Production

With increased quantities, workpieces are often processed in series production, whereby a workpiece usually to go through different manufacturing processes until the desired product is produced. An industrial production facility with multiple manufacturing resources is called a manufacturing system (MS) [6]. The performance of a MS or individual machines is measured in terms of overall equipment effectiveness (OEE), which is a measure of value creation consisting of availability, performance and quality factors [7].

The availability factor relates the actual productive time of a system to the operating time, in which planned downtimes such as tool setups or unplanned downtimes such as malfunctions can also occur. The performance factor describes the utilized duration of the system's production time and identifies short downtimes. The quality factor describes the ratio of defect-free parts that meet quality requirements to the total number of parts produced. The quality can be controlled directly in the machine (inline) or on a separate measuring machine (offline). The multiplication of the three factors gives the OEE in percent and is calculated as follows. [8]

$$ {\text{OEE }} = {\text{ availability }} \cdot {\text{ performance }} \cdot {\text{ quality}} $$
(1)

According to Bertagnolli in [9], average OEE values in autotmotive industry are around 60–80%. The OEE is also an indicator of the effective use of energy in MS and must not be negatively influenced by improvement measures.

2.2 Machine Tools

MT are an essential part of a MS. In the structure of a MT, a distinction is made between main and auxiliary assemblies. The main assembly includes the components that carry out the actual manufacturing process, also called the main process. Accompanying the main process there are several auxiliary processes, which support the manufacturing process by auxiliary units and provide for example cooling and lubrication. They do not directly influence the process. In addition, auxiliary units account for around 60% of the MT total energy consumption [10]. This makes auxiliary units a promising object for energy demand optimization.

2.3 Control System of Machine Tools

The linking and interaction of the individual components of a modern MT is realized by the Programmable Logic Controller (PLC). Inter alia, the PLC has tasks such as starting and stopping the movements of spindles and axes, switching auxiliary media on and off, and changing target values of forces, torques and rotational speeds. Depending on the requirements for precision or complexity, the PLC can be also used to address a numerical control system, named Computerized Numerical Control (CNC). [11].

Depending on the subsystems control of a MT and the material flow, the machine is set to different operating states by the control system. The productive operating state usually has the highest power consumption and longest runtime and thus the greatest potential for energy savings [12]. Therefore, only this state will be examined.

3 State of the Art

Some approaches to energy optimization of MT already exist in a similar technical context. However, the extent to which these provide a suitable approach for implementing energy efficiency measures will be considered below.

In Denkena et al. [13] the authors present a strategy to reduce the energy consumption of a MT. Therefore, the high-pressure coolant flow is adapted to the optimal quantity of flow while taking the influence on the tool wear into account. With additionally attached sensors, the volume flow was recorded, and new operating points were transferred from the NC code to the frequency inverter by a PROFIBUS connection. By this implementation up to 37% energy could be saved in the optimal case. Nonetheless the authors state, that the necessary software functions are not commonly available and tests outside of secured laboratory conditions were not conducted.

In [14], the energy consumption of a CNC-controlled MT was optimized in productive operation. For this purpose, a virtual MT was simulated, which estimated the energy consumption and feed rate for each line of the NC program. A generic optimizer adjusted spindle speed, feed rate and coolant pump pressure in the form of an improved NC program. Experiments on the CNC machine were able to show an energy saving of 13% after optimization. However, successful application outside laboratory conditions on MT that are more complex remains questionable.

Outside of secure laboratory conditions, the manipulation of a MT in a production system can affect the entire production chain. In [15], an approach for the safe process optimization of MT in series production was presented. The core of the work was the determination of production critical KPIs to validate the process capability of the machine. From this, a decision tree for the optimization of MT in series production was derived, which provides recommendations for action in the event of a violation of the KPIs and checks the validity of the optimization. Based on these results, an approach for the implementation of a closed loop optimization will be presented in the following.

4 Approach

The realization of a closed optimization loop requires not only the necessary connectivity to the control system of the MT, but also safety measures to adequately safeguard the manufacturing process and reach production goals during series operation. To avoid a negative impact on the OEE, it is recommended to control the machine with the shortest possible downtime and constant component quality. The following assumptions and requirements should be met for this purpose.

4.1 PLC

The control of the MT represents a production critical system and ensures the machine functions. The PLC must not be overloaded by the optimization cycle. However, continuous setpoint specifications and computationally intensive data analyses could provoke this, making a different solution necessary.

External industrial computers, mainly called industrial edge devices, can reduce load on the control system. The edge device communicates with the control system via Profinet and can collect and specify data without significant loads on the PLC. Furthermore, it is possible to calculate new setpoints based on the collected data using computationally intensive algorithms.

4.2 Connectivity

The key figures of the OEE presented in Sect. 2 must be checked continuously in order to guarantee a high overall effectiveness of the process. For a closed loop optimization, it is necessary to collect this data either from the machine or from external sources such as a Manufacturing Execution System (MES). Therefore edge device must be connected to external systems outside of the MT.

4.3 Setpoint Specification

Optimized parameters should be able to be activated without downtime. The data transfer takes place via the Profinet connection of the PLC. The PLC should continuously check whether new parameters are being transferred. As soon as these are available, their values have to be checked for validity regarding syntax and semantics to avoid malfunctions. If a value is invalid, it is to be rejected.

4.4 Optimization Procedure

MT in series operation repeatedly perform a defined production process within one cycle. The process result can therefore be validated after each cycle. It is not advisable to change the setpoints dynamically during the production process, since the cause-effect chain would not be traceable. Instead, the target value should be specified statically, so that a new target value is defined before the start of a production cycle.

4.5 Closed Loop Energy Demand Optimization

In the first step, the edge device must be integrated into the existing system of the plant. The necessary hardware installations, such as the edge device and peripherals, must be connected. A connection to the control system is established via Profinet. Access to the MES is established via the machine's LAN switch. The PLC must be extended by function blocks that enable communication to the edge device. The overall approach structure, with the systems connections is shown in Fig. 1.

Fig. 1.
figure 1

Overall approach structure with peripheral connections

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Secondly, the data acquisition is to be realized. Important data points for safeguarding the OEE, as well as the parameters for specifying the target values, must be localized in the PLC and integrated in the edge device. Applications for controlling the setpoints and for recording the data points may have to be implemented in the edge device. All optimization data must be collected and put into a uniform format for comparability. Finally, the data must be merged and matched on a time and cycle basis.

In the last step, the optimization itself is facilitated. Target values must be checked for syntax and semantics before each specification and the OEE must be continuously monitored to detect defective production. The optimizer and its application should be designed according to the use case. For instance, the selection can be based on the possible number of trials or the system complexity.

5 Implementation

The approach is to be implemented on a grinding machine in a camshaft production, which processes the surfaces of the camshafts. The optimization will be realized during series operation.

5.1 Hardware Installation

The MT has an inline quality inspection. A measuring device for tracking the total electrical power consumption is available and transmits the current power consumption to the MES every second. The Industrial Edge from Siemens is used for data collection and setpoint specification. The edge device is connected to the machine and MES via the Profinet and LAN connections as described in Sect. 4.6.

5.2 Data Acquisition

The setpoints to be specified are taken from the respective function blocks of the PLC. For the approach, a high-pressure cooling pump is varied about pressure in order to reduce the total energy consumption. In the given solution, the configuration of the edge device is done via a web interface, which offers the possibility to define the necessary data points from the PLC and predefined setpoints in tabular form. Applications for writing and reading the data points were also developed via the web interface. The characteristics required for tracking the OEE and the energy savings are as follows:

  • The cycle time of the production cycle specifies the speed of the production process and serves as the start and end point for predefined setpoints.

  • The operating status of the MT provides information about whether the machine is in productive operation.

  • For the evaluation of the component geometry, the discrete values of the probes are collected. The tolerance limits are available in the corresponding function block, so that this information will also be collected.

  • The energy measurement is carried out via the counter reading of the multifunction meter and is to be collected in the controller in the associated function module.

In principle, it is possible to collect further process data that could serve as indicators for the changes in the system. However, with this approach, attention is paid only to the unique values that provide information about the OEE.

For comparability of the data, it is converted into a uniform CSV format and sorted by time and cycle. Data generated during the manufacturing, must be aggregated to a uniform frequency. The part geometry is recorded after each production cycle and thus assigned to the correct production cycle via the part ID of the workpiece.

5.3 Optimization

Here, an optimization problem was investigated under three constraints named quality (N1), cycle time (N2) and technical availability (N3). These can be represented by lower and upper tolerance limits \({\mathrm{x}}_{\mathrm{min}}^{\mathrm{i}}\), \({\mathrm{x}}_{\mathrm{max}}^{\mathrm{i}}\) as follows \({\mathrm{N}}_{\mathrm{i}}= [{\mathrm{x}}_{\mathrm{min}}^{\mathrm{i}}\), \({\mathrm{x}}_{\mathrm{max}}^{\mathrm{i}}],\mathrm{ i }= 1, \dots ,3\).

To solve this problem, the task is to find a local minimum of the function without violating the constraints. The function to be optimized \(E: {\Omega \subset {\mathbb{R}}}^{d}\to {\mathbb{R}}\) describes the energy consumption, where d corresponds to the number of inputs and \(\Omega \) defines the set of admissible input parameters, which do not violate the constraints (Ni).

In order to safeguard the process, the factors of the OEE should be considered individually, thus ensuring that all components of the OEE are complied with. A slight reduction in OEE is already an indication of process deterioration. To determine the OEE coefficient, the values of the constraints are considered binary rather than discrete numerical values. As soon as one of the target parameters leaves one of the \({N}_{i}\) process reliability is endangered and the input parameter used is discarded.

For example, the experiments are performed using the gradient descent with continuous validation of the target variables. Using this gradient method, one progresses from an arbitrary starting point in the direction of the steepest descent, until a stationary point is reached. The method now iteratively computes from a predefined value \({\mathrm{x}}_{0}\) a sequence of points (\({\mathrm{x}}_{\mathrm{k}}\)), \(\mathrm{k}\in {\mathbb{N}}\) according to the following iteration rule \({\mathrm{x}}_{\mathrm{k}+1}={\mathrm{x}}_{\mathrm{k}}+{\mathrm{\alpha }}_{\mathrm{k}}*{\mathrm{d}}_{\mathrm{k}},\mathrm{ k}=\mathrm{0,1},\dots \), where the descent direction is given by \({\mathrm{d}}_{\mathrm{k}}=-\nabla \mathrm{ E}({\mathrm{x}}_{\mathrm{k}})\) and \({\mathrm{\alpha }}_{\mathrm{k}}\in {\mathbb{R}}\) determines the step size. The step size is a variable quantity that can be chosen smaller with increasing computing power or time to increase the accuracy of the optimization.

6 Results

The approach of a closed-loop optimization on a MT in series operation could be successfully carried out. Using an edge device did not overload the PLC. With the existing software for the configuration of the data to be collected and setpoints to be described, the process could be monitored continuously and the setpoints could be given to the machine without any problems.

Fig. 2.
figure 2

Power consumption of the MT during Closed loop optimization experiments

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The experiments led to energy savings without changing the value of the OEE. As shown in Fig. 2, iterative gradient descent was used to localize energetically improved operating points. Compared to the starting point, an energy saving of about 12% was achieved. An approximately linear relationship was found in the range of values considered.

7 Conclusion

The paper presented a practical approach to realize a closed loop energy demand optimization of milling machine tools in series production. Data evaluation and optimization runs on an edge device that is physically connected to the machine's PLC. The optimization KPIs can be tracked using data from the PLC and the MES. A constant OEE is understood as a benchmark for successful optimization.

The feasibility of the approach was shown for a grinding machine, where energy savings of about 12% could be achieved. With the ability to read data directly from the PLC via the edge device and tracking OEE as a general benchmark, the applicability of the approach to other milling machine tools is conceivable.

Future work should examine whether this approach can be automated. For this purpose, however, it should be investigated to what extent the cause-effect chain can be traced with regard to medium- and long-term consequences. The tests did not show any abnormalities, but this could be the case with regard to tool durability, for example.