Keywords

1 Introduction

Digitalization of manufacturing processes is one of the key strategic research pathways for the manufacturing industry, since it can enable future factories to operate autonomously or with minimal human intervention with automated decision-making algorithms that ensure reliable, safe and productive operation. Initiatives, such as Industry 4.0 have showcased the importance of the digital transformation of manufacturing and provide roadmaps for implementation of technologies and concepts related to this topic. To this end, both academia and industry invest heavily on developing technologies that can enable this digital transformation [1].

Key enabling technologies for this digital transformation are the inline quality control systems that constantly monitor the manufacturing process, detect deviations from the optimal operation and apply corrective measures. Especially when machining is concerned, the stability of the process is one of the most crucial factors that affect its quality. The presence of chatter, which is a self-excited vibration, is detrimental for the tool life, surface quality of the workpiece and safe operation of the machine tool [2]. This phenomenon has been studied for years and several technologies have been developed that try to suppress chatter, based on inline monitoring and control systems. However, the industrial implementation of such systems is still limited and one of the key reasons is the fact that sensing systems for machining require complex integration of sensors and data acquisition devices in machine tools. End users are reluctant to integrate an invasive system in their machine tool, which would require modifications in the machine tool frame, spindle housing, etc. On top of that, high investment costs for the acquisition and integration of the sensing system adds an additional barrier for smaller companies to adopt such technologies. A promising solution to this issue that can foster the digitalization of machining processes is sensor integrated tooling. The concept of sensor integrated tooling is based on installation of sensors in replaceable components of the machining system (e.g. tool holders, fixtures, vices), which provides a non-invasive monitoring approach and can be transferred seamlessly between different machine tools.

2 State of the Art

Since sensor integrated tooling is a very promising solution for monitoring of the machining process, academia and industry have put a lot of effort into the development of such systems. The most common approach that has been followed is the integration of sensors in the tool holder or cutting tool, since it enables to reach as close to the machining process as possible. Xie et al. [3] have integrated a vibration sensor and six capacitance sensors, along with the required electronics for wireless transmission in a ring structure that has been installed externally on the tool holder, in order to estimate the wear level of the cutting tool. Bleicher et al. [4] have integrated a capacitive MEMS sensor in the internal structure of the tool holder that has been used to measure vibrations and transmit the data wirelessly through Bluetooth. Rizal et al. [5] have integrated a rotating dynamometer, based on strain gauges, in the outer geometry of the tool holder to measure the cutting force during milling. Totis et al. [6] have integrated triaxial piezoelectric force transducers between the insert cartridge and the body of the milling head, in order to measure the individual cutting forces on each cutting edge. In a similar fashion, Luo et al. [7] have integrated polyvinylidene fluoride (PVDF) sensors between the insert cartridge and the body of the milling head to measure the cutting forces. Both approaches enable measurement very close to the cutting zone; however, their applicability is limited in large, indexable milling cutters. In order to address this issue, Cen et al. [8] used PVDF sensors that were adhered on the shank of the end mill. The strain signals generated by the sensors were used to calculate the cutting forces at the end mill tip, by treating it as a cantilever beam and using the Euler-Bernoulli beam theory. A significant challenge regarding wireless sensory devices is related to the fact that they need to be charged, thus introducing downtime to the equipment. To tackle this, Ostasevicius et al. [9] have developed a self-powered wireless sensor integrated tool holder for tool wear monitoring, which uses the tool holder vibrations to excite a piezoelectric transducer. The transducer charges a capacitor that powers the low-power electronics of the tool holder. The integration of sensors on fixtures has also been investigated. Liu et al. [10] integrated PVDF thin-film sensors into fixtures to monitor the cutting forces on thin-wall aircraft structural parts. Rezvani et al. [11] have replaced the jaws of a vice with sensor integrated plates with strain gauge and PZT sensors to monitor the clamping force and cutting forces during milling. Apart from academic approaches there are also commercial sensor integrated tooling systems, such as the iTENDO tool holder from Schunk [12] or the spike tool holder from promicron [13].

Although the aforementioned approaches are very promising and can offer high quality measurement are based on expensive sensing elements and complex integrations that significantly increase their implementation cost. All those aspects lead in complex or very expensive systems or both, that are not economically viable for SMEs and small manufacturers that want to digitalize their machining processes.

To this end, this paper proposes a simple and low-cost solution for sensor integrated tooling, through the integration of a MEMS accelerometer on a machining vice. In the rest of the paper, the dynamic analysis of the monitoring setup and its experimental validation are presented. Moreover, the sensor integrated vice is validated in a case study of chatter detection, compared to an expensive, lab-scale setup to prove its performance. Finally, the results and conclusions of this study are presented.

3 Approach

For the development of the sensing setup a commercial, general-purpose milling vice (Vertex VA-6) has been equipped with a low-cost MEMS accelerometer from Micromega (IAC-CM-U). The first step of the development is the modelling of the dynamic behavior of the sensor integrated vice, in order to determine the best integration approach and to ensure that the dynamic behavior of the system will not impact the measurement quality due to the operating conditions during the milling process. An adapter plate was manufactured and integrated on the back of the steady jaw of the vice, on which the sensor was be installed (Fig. 1).

Fig. 1.
figure 1

Integration approach for the sensory vice

Full size image

The first step of the approach is the modal analysis of the system that can give a first estimation of its dynamic response. Table 1 shows the analysis results. The modal analysis has been setup in ANSYS and validated experimentally with an impact hammer test. The experimental equipment used for the impact test are a Kistler 9724A5000 impact hammer and a Kistler 8762A10 tri-axial accelerometer. Labview was used to calculate the Frequency Response Functions (Fig. 2). As it can be observed, there is a general agreement between simulation and experimental validation.

Table 1. Modal analysis results
Full size table
Fig. 2.
figure 2

Experimentally obtained frequency response functions

Full size image

The first natural frequency of the sensory vice occurs at 1171 Hz. The target milling machine where it is going to be installed has a spindle with a maximum rotating speed of 3600RPM. Even with a multi tooth cutter (e.g. 6 teeth), the maximum tooth passing frequency that will be observed during machining in this specific machine tool is 360 Hz. Therefore, there is no risk for resonance phenomena. For other machine tools, capable of high-speed machining, another vice should be selected with higher natural frequencies; however, the overall approach is still the same.

The next step is to quantify the effect of the operating conditions in the dynamic behavior of the sensor integrated vice. A very efficient method of determining the dynamic behavior of a system in a wide frequency range is the harmonic analysis. The intermittent cutting during milling introduces dynamic loads that have a harmonic nature. As a result, it is possible to quantify the dynamic behavior of the sensor integrated vice during machining through a harmonic analysis. The harmonic analysis has been setup with a harmonic force acting on the workpiece. The cutting forces that were used in the analysis were \({F}_{x}=600N\), \({F}_{y}=280N\) and \({F}_{z}=550N\), which are typical values for roughing of hardened steel [14]. The vibration amplitudes at the sensor position have been measured during the analysis. The results of the harmonic analysis are presented in Fig. 3. The simulation results showcase the stiffness of the integration point of the sensor. As a result, no unwanted compliance will be introduced during the operation of the system, interfering with the process.

Fig. 3.
figure 3

Vibration amplitudes at the sensor location

Full size image

4 Case Study on Chatter Detection

In order to test the actual suitability of the sensor integrated vice for monitoring of the milling process, a case study on chatter detection was conducted. The whole monitoring system was comprised of the sensory vice and a Labjack T7 data acquisition system, which fed the vibration data in real time to a personal computer (Fig. 4).

Fig. 4.
figure 4

Monitoring system setup

Full size image

An indicative vibration signal in the feed axis, as well as the plot of is Fast Fourier Transform (FFT) is presented in Fig. 5. In general, a good signal quality can be observed with a slight noise level ranging the whole frequency spectrum.

Fig. 5.
figure 5

Vibration signal (left) and its FFT (right) using the sensory milling vice

Full size image

The case study that has been selected to validate the performance of the sensory vice as an enabler for a milling monitoring system was chatter detection. The chatter detection system is based on a proprietary development of the Laboratory for Manufacturing Systems and Automation and is described in detail in [15]. For the sake of completeness, a short description is given here. The chatter detection algorithm is based on vibration signals in the feed and cross-feed axes, which are decomposed with Variational Mode Decomposition (VMD). From the decomposed signal, the modes that are related to chatter are analyzed and chatter related features are extracted from them in the time and frequency domains. The features are fed to a Support Vector Machine (SVM) classifier to detect chatter status. In [15], the system was developed and tested using a highly expensive, lab-scale setup, comprised of a Kistler 8762A10 ceramic shear accelerometer and a National Instruments PXI-4472 sound and vibration module for data acquisition. In order to validate the performance of the sensory milling vice, the system was retrained and tested with data coming from the proposed system. Using the stability lobe diagrams of the machine tool, tool holder and cutting tool system, generated in [15], the process parameters that led to stable and chatter machining were selected for the machining experiments.

5 Results and Discussion

The SVM classifier has been trained with the data from the sensory milling vice. The results are presented below, as well as the results that have been achieved with the lab-scale monitoring setup. The Receiver Operating Characteristic (ROC) curves, as well as the confusion matrices of the classifiers are presented (Fig. 6 and Fig. 7).

Fig. 6.
figure 6

ROC curve (left) and confusion matrix (right) of the sensory milling vice

Full size image
Fig. 7.
figure 7

ROC curve (left) and confusion matrix (right) of the lab-scale setup

Full size image

As it can be observed from the experimental results, the chatter detection system using the data from the sensory milling vice has similar chatter detection performance with the lab-scale setup. This shows that the sensory milling vice can be considered as a robust and reliable data source, compared to an expensive lab-scale monitoring system. Apart from the reduced cost, the significantly lower integration complexity and increased durability of the sensory milling vice can render it as a promising sensor integrated tooling solution for smaller machine shops and SMEs.

6 Conclusions

The scope of this study was the development of a low-cost sensor integrated tooling system for milling, suitable for industrial implementation. The system was based on low-cost monitoring equipment (sensor and DAQ system) and a commercial, general-purpose milling vice. Based on the results derived from this study the following conclusions can be drawn:

  • The simulation of the dynamic behavior of the sensor integrated tooling system is a crucial element of the development phase, since it can enable the correct selection of the sensor integration approach

  • The experimental modal analysis of the sensor integrated vice has validated the predicted dynamic behavior

  • The performance of the sensor integrated vice as a data source for chatter detection has been validated against an expensive, lab-scale monitoring setup

Future work should include the integration of an edge computer to eliminate the need for a personal computer and provide a plug-and-play solution. Moreover, advanced signal processing algorithms should be employed to eliminate the noise existing in the signal. Finally, closing the loop with the machine tool is another important aspect to enable real-time process control and chatter suppression.