A Monitoring Method for Viscosity and Print Quality in Screw-based Material Extrusion Additive Manufacturing

Article information

Int. J. Precis. Eng. Manuf.-Smart Tech.. 2025;3(2):133-141

Publication date (electronic) : 2025 July 1

doi : https://doi.org/10.57062/ijpem-st.2025.00101

Song Hyeon Ju ¹^,^*, Yunseok Lee ¹^,²^,^*, Kyeongeun Song ¹, Jungsoo Nam ¹

¹Industrial Transformation Technology Department, Korea Institute of Industrial Technology, 89 Yangdaegiro-gil, Ijang-myeon, Seobuk-gu, Cheonan-si, Chungcheongnam-do, 31056, Republic of Korea

²Department of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

Jungsoo Nam: rack1219@kitech.re.kr

These authors are contributed equally to this work.

Received 2025 April 16; Revised 2025 May 26; Accepted 2025 May 26.

Abstract

Screw-based material extrusion, an additive manufacturing (AM) technique utilizing thermoplastic pellets, provides notable advantages in material variety and industrial scalability compared to conventional filament-based AM. The viscosity of the molten polymer can significantly affect flow behavior, bead formation, and interlayer adhesion, thereby influencing the final part quality. Despite the importance of viscosity monitoring, real-time sensing technologies have rarely been applied to screw-based AM due to the complexity of the extrusion mechanism. In this study, a non-invasive, cost-effective method was proposed to estimate the viscosity of molten polymers and evaluate printing quality during extrusion. A Hall-effect current sensor was attached to the screw motor to measure real-time motor current, which was then correlated with screw torque and used to infer the polymer’s rheological properties. Experiments were conducted with two polymer types (amorphous and semi-crystalline) to validate the correlation between motor current and polymer viscosity as well as printed part quality. The proposed method demonstrated the feasibility of using motor current as a proxy for real-time viscosity monitoring in screw-based material extrusion. This approach contributes to the development of a reliable process monitoring framework for improving process stability and ensuring consistent part quality in industrial AM applications.

Keywords: Additive manufacturing; Material extrusion; Viscosity; Monitoring; Hall-effect current sensor

1 Introduction

With the advancement of industry, various manufacturing technologies have emerged, among which Additive Manufacturing (AM) has garnered significant attention from both industry and academia. As a result, extensive research has been conducted across diverse application areas. AM technologies are generally classified into several categories based on their operating principles [1].

Among these, the Material Extrusion technique, which is the focus of this study, can be broadly divided into two main types. The first utilizes filament-type materials, commonly represented by Fused Deposition Modeling (FDM) or Fused Filament Fabrication (FFF) [2,3]. These methods involve the thermal melting and extrusion of filaments and are predominantly used in small-scale 3D printers.

The second method utilizes pellet-type materials, which are melted and deposited through a single screw-based extrusion system. Since this method does not require the fabrication of filaments and directly uses pellets, it allows for the application of various materials and is advantageous for large-scale production due to its ability to handle high-volume supply [3–5]. For those reasons, this study focuses on the screw-based material extrusion process. However, screw-based extrusion processes may result in low-level print quality and mechanical properties if not properly controlled [6]. Inappropriate interlayer adhesion and bead bonding can lead to the formation of voids, which degrade mechanical performance [7]. In addition, it is challenging to optimize the process according to the characteristics of the polymer materials used [8]. Viscosity is one of the material properties that directly influence bead geometry and interlayer bonding. In the case of low-viscosity polymers, the molten material may spread due to gravity or drip from the nozzle immediately after extrusion, resulting in defects such as bead displacement or collapse [9]. Conversely, if the viscosity is too high, the flowability of the polymer is reduced, hindering the neck formation between adjacent beads and the diffusion of polymer chains, which in turn lowers interlayer bonding strength [10].

To address these issues, previous studies have taken three main approaches. First, experimental-based process optimization allows for extensive exploration of parameter combinations but is time-consuming and costly [11]. Second, simulation-based prediction offers efficient process forecasting but faces limitations in accurately reflecting the physical behaviors of actual materials, such as flow, viscoelasticity, and crystallization [12,13]. Third, sensor-based process monitoring, which has recently gained attention, enabled real-time detection of physical phenomena and offered high efficiency relative to cost, positioning it as a next-generation process control technology [14,15].

Despite its potential, sensor-based process monitoring in screw-based material extrusion is still in its early stages, with limited applications and implementations. Therefore, this study first reviews sensor-based monitoring techniques, which have been more actively studied in filament-based AM processes. To date, sensor-based process monitoring has primarily been developed for filament-based AM. For example, Borish et al. used a thermal camera to control deposition timing [16], while Ferraris et al. analyzed the correlation between temperature and bonding area of beads [17]. Bartolai et al. proposed a tensile strength prediction model based on such data [18], and Lin et al. and Faes et al. used laser sensors to detect underfill and overfill [19]. Tlegenov et al. analyzed nozzle clogging through vibration patterns using an accelerometer [20], while Li et al. [21] and Wu et al. [22] proposed anomaly detection systems using various sensors.

In contrast, only a few sensor-based monitoring studies have been conducted on screw-based pellet extrusion. Recently, Castillo et al. applied a Cyber-physical Production System (CPPS) to a dual-head system with pellet and filament extrusion, achieving a high accuracy in real-time defect detection through multi-sensor integration [23]. Caltanissetta et al. employed a thermal infrared sensor in a Big Area Additive Manufacturing (BAAM) process, showing improved process stability through layer-wise temperature profiling and automatic detection of hot and cold spots based on cooling histories [24].

Collectively, these studies reveal that existing sensor-based monitoring technologies have been concentrated on filament-based AM, with minimal application to screw-based processes. Furthermore, research on monitoring based on material characteristics, particularly viscosity, remains insufficient.

Therefore, this study conducted process monitoring in a screw-based material extrusion system using a hall-effect current sensor attached to the screw motor, considering the characteristics of the materials used. The central hypothesis was that the torque of the screw motor is correlated with the viscosity of the material. To verify this, two materials with different molecular structures - PETG (amorphous) and PLA (semi-crystalline) - were selected. The correlation between the current sensor signals and viscosity variation was analyzed, and the relationship between these signals and overall process quality was also examined.

2 Experimental Method

2.1 Viscosity of Polymer

Viscosity is a material property that represents a fluid’s resistance to deformation, and it is generally defined as the ratio of shear stress (τ ) to shear rate (γ̇), as shown below [25].

(1) η=τγ˙

In the case of molten polymers, viscosity exhibits a nonlinear dependence on shear rate, and such materials are classified as non-Newtonian fluids. Most molten polymers fall under this category and exhibit shear thinning behavior, where viscosity decreases with increasing shear rate. The most common model used to describe the behavior of such polymers is the power-law model [26].

(2) η(γ˙)=mγ˙n-1

Here, m is the consistency index, and n is the power-law index.

However, the viscosity of polymers is also influenced by temperature. This temperature dependence is generally described by the Arrhenius law [26], expressed as

(3) η(T)=η0exp(EaR(1T-1T0))

In this equation, E_a is the activation energy, R is the universal gas constant, and η₀ is reference viscosity at T₀. Since Eqs. (2) and (3) respectively represent the shear rate dependence and temperature dependence of the polymer, but each is only applicable under fixed shear rate and fixed temperature conditions.

Therefore, the more general form, which is dependent on both temperature and shear rate is as follows [26].

(4) η(γ˙,T)=mγ˙n-1exp(EaR(1T-1T0))

2.2 Viscosity in Screw Extrusion Process

In screw-based material extrusion additive manufacturing, the shear stress acting on the fluid inside the extruder can be derived from the torque required to rotate the screw. When the internal structure of the extruder is simplified as consisting of an inner and outer cylinder, as shown in Fig. 1, the fluid is assumed to reside in the annular space between the two cylinders. As the inner boundary moves relative to the outer boundary, drag flow occurs within the fluid, resulting in shear deformation. The shear rate within the fluid is defined as the velocity difference divided by the normal distance. When the radial clearance H is small compared to the cylinder radius R, the shear rate can be expressed as follows [26].

Fig. 1

Co-rotating cylinder model

(5) γ˙≅vo-viRo-Ri=2πRiNH

Here, v_o and v_i represent the velocities of the outer and inner cylinders respectively, N is the rotational speed. Thus, if the structural dimensions and rotation speed are known, the shear rate can be calculated. The shear stress acting on the fluid can be obtained from the torque T required to rotate the inner cylinder. The total shear force F on the surface of the inner cylinder is defined as the product of the shear stress (τ ) and the surface area 2πRi_L, and can be expressed as follows [26].

(6) F=TRi=2πRiLτ

The torque T is related to the force by the moment arm R₀ thus

(7) τ=T2πRi2L

From Eq. (1), viscosity η can be calculated as

(8) η=τγ˙=TH4πRi3LN

This co-axial cylinder model can also be applied to the actual screw geometry shown in Fig. 2. In the screw channel and clearance region, the shear rate can be described respectively as follows [26]. Based on the boundary conditions presented above, the viscosity of the fluid can be calculated.

Fig. 2

Screw extruder model considering clearance and channel

(9) γ˙ch≅π(D-2H)NH=πN(DH-2)

(10) γ˙cl=πDNδ

Therefore, by applying this model equivalently, it can be concluded that the viscosity of the fluid can be inversely estimated from the torque measured during the extrusion process.

2.3 Equipment Specification

The screw-based additive manufacturing system employed in this study is illustrated in Fig. 3. The system is configured as a three-axis gantry structure utilizing an orthogonal robotic setup. The extruder features three band-type heating zones, each capable of reaching temperatures up to 300°C. These zones are individually monitored and controlled in real time via a control panel, using K-type thermocouples for precise thermal feedback. Material is extruded through a 2 mm-diameter nozzle at a maximum rate of 2 kg/h. The extrusion screw is driven by a 400 W Panasonic A6-series AC three-phase servomotor, paired with a 30 : 1 servo gearbox, allowing for a maximum rotational speed of 100 RPM. This setup ensures sufficient torque for processing high-viscosity materials. The build volume of the system is 300 × 300 × 300 mm, providing a moderately large fabrication area. The build plate is equipped with a 1,000 W silicone rubber heater, enabling stable thermal conditions during printing and improved layer adhesion. Detailed specifications are summarized in Table 1.

Fig. 3

Screw-based material extrusion additive manufacturing equipment and flowchart of the experiment

Table 1

Equipment and sensor specification

2.4 Experimental Method

In this study, experiments were conducted using two polymer materials: PLA and PETG. To classify the viscosity characteristics of each material, their temperature-dependent viscosity profiles were utilized. For both materials, five different extrusion temperature conditions were selected to evaluate the effect of temperature on viscosity.

To eliminate the influence of shear rate, which can significantly affect viscosity measurements, the screw rotational speed was fixed at 20 RPM across all experimental conditions. The specific temperature settings applied to each material are summarized in Table 2. The printed specimens consisted of rectangular structures formed by depositing single beads in 10 layers, with 10 beads per layer, resulting in a total of 100 beads per specimen. To ensure consistent and high-quality prints, the bead width and height parameters were determined based on the results of prior studies [27], as also detailed in Table 2.

Table 2

Printing parameters

2.5 Data Acquisition Method

In this experiment, a Hall-effect current sensor was employed to monitor the current drawn by the servo motor that drives the extrusion screw. The output of a Hall-effect sensor is nearly linear over a substantial range, and its high sensitivity enables precise monitoring within the desired operating region [28]. The sensor was selected and configured to match the rated current input specifications of the servomotor used in the system. As illustrated in Fig. 4, the sensitivity of the Hall-effect sensor varies depending on its resistance value, which determines the measurable current range.

Fig. 4

Measurement range of the hall-effect current sensor

Based on preliminary tests using sensors with various resistance values, the FS4L120 sensor with a resistance of 120 Ω exhibited the most stable and accurate current detection performance. Consequently, it was selected for the main experiments. The sensor was installed on the three-phase power cable connecting the motor driver to the servomotor, enabling real-time monitoring of current time-series data.

Data acquisition was performed using a NI-DAQ system in conjunction with a LabVIEW environment running on a PC. Since each experimental condition involved the deposition of 100 beads, the collected time-series data were segmented to isolate the intervals corresponding to bead extrusion. Accordingly, 100 individual time-series segments were extracted and analyzed for each condition. A flowchart of the experimental procedure is presented in Fig. 3.

2.6 Printing Quality

To evaluate the quality of specimens printed under various viscosity conditions, this study employed cross-sectional area measurements. The specimens were sectioned perpendicular to the printing direction using an orthogonal cutting machine. To ensure clear visualization for analysis, the cut surfaces were subsequently grinded and polished using an automatic polishing machine. High-resolution images of the polished cross-sections were then acquired using a digital microscope (VHX-7000, KEYENCE). Finally, the cross-sectional areas were measured for each experimental condition using the accompanying KEYENCE analysis software.

3 Results and Discussion

3.1 Current Signal Result

The Hall-effect current sensor data collected for each temperature condition of PLA and PETG were analyzed using the root mean square (RMS) of the current time-series data. The RMS value was calculated as follows:

(11) RMS=1T∫0Ti(t)2dt

Here, T is the measurement time. Since the data obtained from the current sensor are discrete values in the form of {i₁, i₂, i₃, ..., i_N}, the RMS can be re expressed as

(12) RMS=1N∑j=1Nij2

The individual RMS values of each bead obtained under each condition are shown in Fig. 5(a) with 95% confidence interval error bars. Fig. 5(b) presents the temperature-dependent Arrhenius viscosity model for PLA and PETG materials in Eq. (3). The coefficients of the model were obtained through a literature review. Fig. 5(c) presents the total cross-sectional area of each specimen.

Fig. 5

Comparison of the (a) RMS data from the hall-effect current sensor, (b) Temperature-dependent Arrhenius model, and (c) Total cross-sectional area of the printed specimen

The RMS analysis results show that the current data as a function of temperature exhibits a similar trend to the temperature-dependent Arrhenius viscosity model. Both results show an exponential decrease with increasing temperature. These findings indicate the potential of the current-based viscosity monitoring approach proposed in this study.

For PLA, the variance of the RMS values of current tends to be relatively larger compared to PETG. This phenomenon is interpreted because of the semi-crystalline nature of PLA.

Most polymers are classified as either semi-crystalline or amorphous. Amorphous polymers exhibit a disordered structure in which polymer chains are entangled irregularly. Whereas semi-crystalline polymers have a complex structure composed of crystalline regions embedded within the amorphous matrix. Both types of polymers exhibit a glass transition temperature T_g, which marks the onset of molecular mobility in the polymer chains. However, semi-crystalline polymers also have a melting temperature point T_m above T_g, at which the crystalline regions begin to melt. In the temperature range between T_g and T_m, the viscosity behavior becomes complex. While the amorphous regions gain mobility, the crystalline regions remain fixed, restricting the chain movement. As a result, viscosity in this range becomes highly sensitive to temperature and exhibits complex behavior.

For PLA, the typical melting temperature is known to be around 150°C [29,30], although this value may vary depending on the material grade and specific processing conditions. The extrusion temperature of 150°C used in this study falls within this transition range, which explains the high variance observed in the RMS values under this condition. A decreasing trend in variance is observed as the temperature increases up to 180°C, supporting the plausibility of this interpretation. In contrast, PETG is an amorphous polymer with no crystalline regions, resulting in more stable viscosity behavior. According to Rauwendaal, C [26], the viscosity behavior of amorphous polymers is generally well-predicted at temperatures above T_g + 100°C. For PETG, with a T_g of around 75°C, [31,32] the extrusion temperatures above 190°C, used in this study fall within the appropriate range for this model. Therefore, PETG shows more predictable viscosity behavior in response to temperature changes, resulting in lower variance in RMS values compared to PLA.

3.2 Quality Result

Cross-sectional images of the printed specimens are presented in Figs. 6 and 7. As previously discussed, the decrease in viscosity with increasing temperature led to process defects in specimens printed under high-temperature conditions, where the bead shape could not be maintained. This phenomenon occurs because the gravitational force acting on each bead exceeds the surface tension of the polymer. Conversely, under low-temperature conditions, the high viscosity inhibited the formation of horizontal necks and bonding between beads, ultimately reducing the mechanical strength of the printed parts.

Fig. 6

Bead cross-sectional area of PETG at extrusion temperatures at 190, 200, 210, 220, and 230°C (from left to right)

Fig. 7

Bead cross-sectional area of PLA at extrusion temperatures at 150, 160, 170, 180, and 190°C (from left to right)

The cross-sectional shape provides insight into the observation that the RMS value at the highest temperature condition for each material (i.e., the lowest viscosity) is higher than that at the preceding temperature level, as shown in Fig. 5(a). At elevated temperatures, immediately after the polymer is extruded from the nozzle, deformation and detachment of the bead may hinder proper deposition onto the previous layer. This can lead to an increase in internal pressure within the extruder [33], resulting in greater load on the screw. Consequently, the RMS value increases despite the lower viscosity at higher temperatures.

The results of the area analysis using the digital microscope are presented in Fig. 5(c). Assuming an ideal bead shape with a perfectly rectangular cross-section identical to the CAD model, the theoretical cross-sectional area was calculated to be 464 mm². Based on this reference, the optimal quality range for practical applications was defined as 90–95% of the ideal cross-sectional area, as also indicated in Fig. 5(c).

Both PETG and PLA exhibited similar trends in their RMS values across the various temperature (viscosity) conditions. These results highlight the potential for real-time monitoring of RMS current values as an indicator of process stability and quality. This topic is explored in greater detail in the following section.

3.3 Quality Estimation

In this section, a linear regression model is proposed to describe the relationship between the RMS and the print quality. Regression analysis was conducted using 100 datasets comprising the current time-series data obtained under different temperature (viscosity) conditions and the corresponding bead cross-sectional area data extracted from the images. Eighty datasets were used for training and twenty for testing. A 5-fold cross-validation algorithm was applied to evaluate the generalizability of the model. The regression model is represented as follows, where A denotes the cross-sectional area of the bead and i denotes the RMS value of the current.

(13) APLA=0.005i4-0.19i3+1.54i2-4.24i+7.42

(14) APETG=0.35i2-2.69i+7.81

As a result of the regression analysis, the Mean Absolute Error (MAE) for PETG was 0.1676, while the MAE for PLA was 0.3088. These regression results are illustrated in Fig. 8. These values indicate a Mean Absolute Percentage Error (MAPE) of 7.95% for PLA and 4.21% for PETG.

Fig. 8

Regression analysis results for quality estimation of printed part (a) PLA and (b) PETG

In conclusion, this study demonstrates the potential of the proposed current-based viscosity monitoring method for predicting print quality.

In particular, the lower prediction error observed for PLA compared to PETG is interpreted as being related to the fact that semi-crystalline polymers like PLA exhibit greater sensitivity to viscosity changes near their glass transition temperature T_g.

This suggests that the effectiveness of the current-based monitoring method may vary depending on the thermal and structural properties of the material, indicating the need to consider such characteristics when applying the method to different types of polymers.

4 Conclusion

In this study, we proposed a current-based monitoring method to estimate the viscosity of thermoplastic materials during additive manufacturing and to evaluate its relationship with print quality. By leveraging the torque load applied to the screw and measuring servo-motor current via a Hall-effect sensor, we extracted RMS current values under varying temperature (i.e., viscosity) conditions and correlated them with bead geometry. Therefore, this study arrives at the following conclusions:

1) Viscosity Estimation via Motor Current

Demonstrated that screw torque (motor current) can serve as a reliable proxy for melt viscosity without dedicated rheometers.
2) Real-time Signal Capture

Employed a low-cost Hall-effect current sensor to obtain stable, high-sensitivity RMS current measurements during in-situ operation.
3) Quality Range Definition

Analyzed bead cross-sectional areas to define an optimal “quality window” and identified the corresponding RMS current interval.
4) Material-specific Behavior

Showed distinct current profiles and variances for PLA versus PETG, reflecting differences in crystallinity and thermal-viscosity response.
5) Predictive Regression Model

Established a quantitative linear relationship between RMS current and bead quality, enabling real-time prediction of print outcomes.

These findings confirm that current-based viscosity monitoring effectively captures material behavior and supports predictive process control in material-extrusion additive manufacturing. Because the method requires only a simple sensor retrofit, it is both practical and cost-effective for on-machine, real-time applications across diverse equipment configurations. Future work will focus on extending model robustness and generalizability to a wider range of polymers and more complex printing scenarios.

Notes

Acknowledgement

This research was supported through the Industry Technology Alchemist Project (No. 20025702, “Development of smart manufacturing multiverse platform based on multisensory fusion avatar and interactive AI”) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). This study has been conducted with the support of the Korea Institute of Industrial Technology as “Development of 3D printing commercialization technology for military parts and demonstration support technology (No. kitech EH-25-0006)”.

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Biography

Seong Hyeon Ju is a researcher at Korea Institute of Industrial Technology (KITECH). He obtained his M.S. degree in Mechanical Engineering from Chung-Ang University. His research focuses on advanced materials machining, composite additive manufacturing, and process monitoring.

Yunseok Lee is a researcher at the Korea Institute of Industrial Technology (KITECH) and a master’s student in the School of Mechanical Engineering at Yonsei University. His research focuses on additive manufacturing using the material extrusion method.

Kyeongeun Song is a Senior Researcher at the Korea Institute of Industrial Technology (KITECH). He obtained his Ph.D. in Mechanical Engineering from Purdue University. His research focuses on the analysis and modeling of composite machining and composite additive manufacturing, as well as sound-based process monitoring.

Jungsoo Nam is a Principal Researcher at the Korea Institute of Industrial Technology (KITECH). He obtained his Ph.D. in Mechanical Engineering from Sungkyunkwan University. He subsequently worked as a postdoctoral researcher at Purdue University. His research focuses on advanced materials machining, additive manufacturing, and process monitoring.

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Equipment
Build volume	300 × 300 × 300 (mm)
Actuation method	Three-axis orthogonal robotic system
Printing speed (Feed rate)	Max 6,000 (mm/min)
Extrusion rate	Max 2 (kg/hour)
Heated bed	Max 110 (°C)
Sensor	Specification	Objective
Band type Heater	Max 300 (°C)	Heating and melting the polymer
Thermocouple	K-type	Temperature feedback control
Screw motor	400 (W), 3-phase servomotor	For screw rotation
Servo gearbox	30 : 1 gear ratio, max 100 (RPM)	For higher torque generation

Material	Temperature (°C)	Material	Temperature (°C)
PLA	150	PETG	190
	160		200
	170		210
	180		220
	190		230
Printing speed (mm/min)		1,680
Screw rotation speed (rpm)		20
Nozzle diameter (mm)		2
Bead height (mm)		1.6
Bead width (mm)		2.9