# Multiple Operational Status Classification Based on One-Versus-One SVM in FDM 3D Printer

Yebon Lee, Sujeong Baek

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Yebon Lee, Sujeong Baek. Multiple Operational Status Classification Based on One-Versus-One SVM in FDM 3D Printer. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.219-226, 10.1007/978-3-031-16407-1_26 . hal-05386066

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# Multiple Operational Status Classification Based on One-Versus-One SVM in FDM 3D Printer

Yebon Lee and Sujeong Baek

Hanbat National University, Daejeon 34158, Republic of Korea

sbaek@hanbat.ac.kr

**Abstract.** FDM 3D printers are commonly used in various fields because they can efficiently manufacture complex shaped products. However, producing even a small product is usually time consuming because the printer operation comprises several stages: heating, aligning, printing, and re-aligning. Working sensors and actuators also differ according to operational states; therefore, it is necessary to distinguish multiple operational states correctly using the sensor information. In this regard, we propose a sensor-signal based multiple status classification for distinguishing FDM 3D printer operations into four categories. To analyze the operational status, we install a total 51 sensors and record temperature, triaxial angular velocity, triaxial acceleration, triaxial geomagnetism, and current signals. For multi-classification, one-versus-one support vector machine algorithm is used, which shows good performance in class-imbalance. As a result, the proposed method shows a higher classification performance in every operational state. Using the findings of this study, the current operational information can be automatically identified, and our research outcome is expected to be used as a basis for effective monitoring of 3D printer operation, such as generating fault detection model of each operational status.

**Keywords:** FDM 3D printer · Operational status classification · Fault detection · SVM

## 1 Introduction

In recent years, the use of 3D printers in various industries, such as semiconductor manufacturing, space, automotive, and medical care, has increased because they can manufacture complex shaped products, which are impossible to manufacture using traditional subtractive manufacturing without assembly or bonding tasks [1–4]. Printers can be classified into several categories, depending on the applied material and printing method. Fused deposition modeling (FDM) is widely used for additive manufacturing, which is relatively inexpensive and has a high printing speed [5, 6]. FDM printers make products by melting thermoplastic filaments and stacking them layer-by-layer [7].

Whereas FDM 3D printers can be used for product fabrication relatively easily even by a beginner, producing even a small product is usually time consuming. The printers also usually require a long preparation time before main printing, including heating and
aligning; therefore, typical FDM printers possess several operational stages: heating, aligning, printing, and re-aligning. According to operational states, in addition to the different roles, moving actuators and activated sensors are also different. For example, a bearing nut or a stepper motor in an extruder is mainly operated in the printing stage [8, 9]. This kind of differences would negatively affect the effectiveness and efficiency of an entire machine monitoring and management. However, various studies have focused on the main printing stage only [10–13], since it is quite simple to conduct. That is, it will be helpful to distinguish multiple operational states correctly using the sensor information.

Thus, we propose operational status classification for an FDM printer using the one-versus-one (OVO) SVM algorithm, which shows good classification performance with class-imbalance. The developed classification model is applied to check what tasks the printer is performing when printing an object under normal operating conditions. To this end, sensors are installed at five points inside the 3D printer to collect time series data, and the entire normal operation process was classified into four states. To improve the accuracy of multiple state classification, the magnitude of the magnetic field and amount of current are measured concurrently, in addition to acceleration, angular velocity, and temperature. Accordingly, this paper is structured as follows. Section 2 describes the 3D printer and installed sensors used to collect data, Sect. 3 presents the classification algorithm and corresponding results, and Sect. 4 summarizes conclusions and future work.

# 2 Experimental Setting and Data Collection

## 2.1 The Used FDM 3D Printer and the Printed Object

Section 2 introduces the FDM printer and sensors, which are attached to the printer and used for data collection. The FDM 3D printer was Cubicon single, shown in Fig. 1-(a), and the dedicated sliding program, Cubicreator 4, was used to make the object. The printed object was set to a cube shape, as shown in Fig. 1-(b). The output material was polylactic acid (PLA), and the related printing parameters were optimized according to previous research, the objective of which was to obtain the optimal output conditions for a low-end FDM type 3D printer [14]: 30% internal density, 0.1 mm layer height, and two external wall lines.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** 3D printer and object printed in the experiment: (a) FDM 3D printer (Cubicon single), (b) a cube shaped object</figcaption></figure>
## 2.2 Sensor Data for Analyzing Printer Operation Status

In this study, an attitude and heading reference system sensor was used to measure the posture angle of the moving object and collect additional motion information. Sensors are manufactured by ROBOR, and each sensor measures 10 data types: temperature, three-axis angular velocity (roll, pitch, and yaw), three-axis acceleration (x, y, and z axis), and three-axis magnetometers (x, y, and z axis). Further details regarding the sensor information is listed in Table 1.

<table><caption>**Table 1.** The detailed specification of output sensor</caption><thead><tr><td>Measurement type in a sensor</td><td>Unit</td><td>Total number of sensor signals</td></tr></thead><tbody><tr><td>Temperature</td><td>°C</td><td>5</td></tr><tr><td>Acceleration (x-axis)</td><td>g</td><td>5</td></tr><tr><td>Acceleration (y-axis)</td><td>g</td><td>5</td></tr><tr><td>Acceleration (z-axis)</td><td>g</td><td>5</td></tr><tr><td>Geomagnetic (x-axis)</td><td>μT</td><td>5</td></tr><tr><td>Geomagnetic (y-axis)</td><td>μT</td><td>5</td></tr><tr><td>Geomagnetic (z-axis)</td><td>μT</td><td>5</td></tr><tr><td>Euler angle (roll)</td><td>deg</td><td>5</td></tr><tr><td>Euler angle (pitch)</td><td>deg</td><td>5</td></tr><tr><td>Euler angle (yaw)</td><td>deg</td><td>5</td></tr><tr><td>Current</td><td>A</td><td>1</td></tr></tbody></table>

To effectively analyze the operation status in the FDM printer, the attachment position of each sensor must be optimized. For example, there is a criterion for determining the optimal position of vibration sensors for condition monitoring, which includes sensitivity to generated vibrations and safe installation [15]. Based on the previous studies, we attached the sensors to the upper part of the nozzle head, side of the bed, and inner three surfaces of the chamber, as illustrated in Fig. 2. We also installed a current sensor to obtain the value of the current from an input power supply.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Sensor positions in the 3D printer</figcaption></figure>

# 3 SVM-Based Status Classification of FDM Printer

## 3.1 Data Collection and Preprocessing

There are various fault types, depending on the operational status of an FDM printer, such as blockage by unappropriated remaining filament and layer deformation during printing due to untightened screws. In addition, identical root causes can be regarded as different faults depending on the working state. Therefore, it is necessary to identify the operational status in consideration of these different characteristics, which are unique to an operating status, in an FDM printer. Therefore, we classified the 3D printer operation into four states based on the movement characteristics. First, ‘State 1’ is defined as a heating step, in which the temperature of the bed and nozzle are increased according to the user setting. ‘State 2’ is called an alignment step for adjusting the gap between the bed and the nozzle, and ‘State 3’ is regarded as the printing step, in which the filament is extruded to produce an output. ‘State 4’ is the last step, called the re-aligning step, in which each actuator is reset to its initial position.

While the printer was working, multiple sensor data was collected from States 1–4. The data collected every 0.5 s (at a sampling rate of 2 Hz) by the 51 types of sensors were automatically stored in the MySQL-based MariaDB table to efficiently manage the large amount of data. During the generation of one cube-shaped object with 20 repetitions, an average of 3,818 length data was generated (variance: 4,749.5); a total of 20 objects were made, thereby 20 datasets were collected. The amount of data for each state is proportional to the time required for each state, as shown in Table 2. In particular, the variance of the length of State 1 (Heating) is significantly larger than that of other states
<table><caption>**Table 2.** The average amount of data per operating status</caption><thead><tr><th rowspan="2">Operating status</th><th colspan="2">The length of each status (points)</th></tr><tr><th>Mean</th><th>Variance</th></tr></thead><tbody><tr><td>State 1. Heating</td><td>501.2</td><td>4037.4</td></tr><tr><td>State 2. Aligning</td><td>393.6</td><td>525.5</td></tr><tr><td>State 3. Printing</td><td>2873.0</td><td>290.8</td></tr><tr><td>State 4. Re-Aligning</td><td>52.1</td><td>1.8</td></tr></tbody></table>

because the initial temperature at the start of the printing operation is not always fixed. For example, a length of State 1 is usually shorter when 3D printing operations are continuously conducted. On the other hand, at the first operation of the day, the printer may consume a relatively longer time for achieving the pre-determined temperature of a heating bed and an extruder from the room temperature. In addition, all data sets were standardized with an average of 0 and a variance of 1.

## 3.2 Status Classification

The SVM algorithm is one of the supervised learning methods that construct an optimal hyperplane, which maximizes the margin between two classes. It has the advantage of being able to perform classification efficiently because it is generally less time-consuming than neural networks [16]. However, in this study, four status labels are required to be distinguished instead of two; Therefore, SVM method for multi-classification was used. The multiple classification method is largely divided into OVR (one-versus-rest) and OVO. OVR performs *k* (the number of state classes) binary classification for distinguishing one class from the rest. That is, a binary classification determines whether a specific measurement is included in a target class or not (for example, the measurement is labelled as class 1 or not). Contrarily, the OVO strategy consists of *k* · (k − 1)/2 classifiers that perform each binary classification [17]. As shown in Fig. 3, a specific measurement can have *k* · (k − 1)/2 decision results according to each hyperplane. For example, the ‘1’ measurement (in Fig. 3) is finally labelled as ‘yellow class’, because 2 out of 3 decision results were ‘yellow’ by hyperplane 1 and 3 (where *k* = 3). However, a measurement is not completely classified by the OVO SVM classifiers if it has different decisions from every hyperplane, which is observed as a gray area in Fig. 3. When the classes are unbalanced, OVR shows a relatively low performance due to simple classification methodology, while the computation speed is fast. Conversely, OVO with more classifiers shows a higher performance, but is computationally slow [18]. Considering the 3D printer operational status with class imbalance, an OVO SVM algorithm is adopted for the proposed multi-class classification.
<figure><img src="image_5.png" /><figcaption>Fig. 3. An example of the OVO SVM classification method</figcaption></figure>

In this study, the training and test datasets were split into a ratio of 9:1. The hyper-parameters were optimized based on a grid-search strategy for improving each state classification accuracy as shown in Table 3. We controlled Gamma and C, which indicate an amount of acceptable degree of a hyperplane curvature and classification error individually, with 3 levels. The finally derived hyper-parameter setting (C and Gamma) was chosen to 5 and 0.005, respectively. In this parameter setting, the training and testing accuracies of the constructed model were 99.6% and 99.3%, respectively. In particular, the classification accuracies for State 1, 2, 3, and 4 were 99.8%, 98.7%, 99.3%, and 99.1%, respectively. To consider the different number of measurements in each state, the F1-score was calculated to be 98.9%. In summary, the model accuracies of the training and testing phases were high enough, and the F1-score showed good performance despite the class imbalance among the four statuses [19].

<table><caption>**Table 3.** The F1-score according to hyper-parameters of the SVM classification model (Bold indicates the best accuracy in the experiment)</caption><thead><tr><th></th><th>C = 0.1</th><th>C = 1</th><th>C = 5</th></tr></thead><tbody><tr><td>Gamma = 0.1</td><td>87.7</td><td>91.8</td><td>91.9</td></tr><tr><td>Gamma = 0.01</td><td>95.9</td><td>98.3</td><td>98.8</td></tr><tr><td>Gamma = 0.005</td><td>96.2</td><td>98.4</td><td><b>98.9</b></td></tr></tbody></table>

## 4 Conclusion

In this study, the entire FDM printer operation was classified according to four different stages, that is, heating, aligning, printing, and re-aligning, to distinguish each operational
status by analyzing sensor data. Acceleration, angular velocity, temperature, magnetic field, and current data were collected for status classification. In addition, sensors were attached to five optimal locations, thereby collecting 51 sensor data. As a result, an F1-score performance of 98.9% was obtained when classifying the four normal operating states of the FDM printer using the OVO-SVM algorithm. Despite there being a class imbalance owing to the different working times among the four states, the proposed classification algorithm showed statistically high accuracy in every operational state.

However, it is difficult to generalize the classification results because we used a simple shaped object (the cube), which resulted in a simple movement of the nozzle. In the future, to overcome these limitations, we intend to classify the work states to be independent of the shape of the printed objects by using different shaped objects in which the nozzle head movement and printing time are different from those of the existing model. In addition, it would be helpful to perform machine maintenance and monitoring which is specialized for each operational state during 3D printer operation.

**Acknowledgement.** This work was supported by project for Industry-Academic Cooperation Based Platform R&D funded Korea Ministry of SMEs and Startups in 2020. (Project No. S3025721) and supported by project for Industry-Academic Cooperation Based Platform R&D funded Korea Ministry of SMEs and Startups in 2020 (Project No. S3025825).

This paper was also supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE). (P0012744, HRD program for industrial innovation).

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