Quantifying quality of 3D printed clay objects using a 3D structured light scanning system

Additive Manufacturing 32 (2020) 100987

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Full Length Article

Quantifying quality of 3D printed clay objects using a 3D structured light scanning system

T

Kwangwoo Wia, Vignesh Sureshb, Kejin Wanga,*, Beiwen Lib, Hantang Qinc a

Civil, Construction and Environmental Engineering, Iowa State University, 813 Bissell Rd., Ames, IA 50011, USA Mechanical Engineering, Iowa State University, 2043 Black Engineering, 2529 Union Dr., Ames, IA 50011, USA c Industrial and Manufacturing Systems Engineering, Iowa State University, 3004 Black Engineering, 2529 Union Dr., Ames, IA 50011, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: 3D printing Printing quality Structured light system 3D scan Geometry

Three-dimensional (3D) printing, or additive manufacturing, has been increasingly used in many fields, including the medicine, food, sensing, metal, automotive, and construction industries. Regardless of its growing applications, there are few of methods, guidelines, and specifications for measuring and quantifying the qualities of 3D printed objects. This is particularly so for objects those are too small, too large, and/or too fragile to be handled manually. In this study, for the first time, a non-contact, and non-destructive measurement method, a 3D structured light scanning system (3D-SLSS), was employed for evaluating the printing qualities of clay objects with different levels of visual defects (e.g., roughness and distortion). 3D scanned images of these clay samples were developed using 3D-SLSS. Then, they were sliced along their sides (perpendicular to the base) to generate a number of two-dimensional (2D) plots, from which various parameters (e.g., sample total height [Htotal], outer diameter [DMouter], layer thickness [TL], layer width, [(WL], surface angle [Sα], semi-cross-sectional area [XA], and surface roughness [R]) were measured. These measurements were then compared with the designed values. The percentages of the differences between the measured and designed values were used to develop a diagnosed area of deficiency, by which the overall qualities of the printed samples were quantified. The results illustrated that all the printed samples exhibited certain differences between their measured and designed values, even for those that appeared well printed. Compared with the designed object, the printed samples generally had reduced total height, diameter, and layer thickness; increased layer width; measurable distortion; and visible surface roughness. Many of these were largely because the freshly printed clay deformed under the weight of the layers above. The distortion angle and area are two necessary parameters for quantifying the degree of distortion of a printed sample. The diagnosed area of deficiency can well describe the overall qualities of the printed samples. 3D-SLSS is a relatively simple, fast, and inexpensive characterization method. Moreover, it can be conveniently extended to various industries for quality control of diverse 3D printing products.

1. Introduction 1.1. Research background Three-dimensional (3D) printing, or additive manufacturing (AM), is a revolutionary process that combines placing, casting, and finishing procedures into a one-step, layer-by-layer production process. It creates a 3D object from computer-aided-design (3D model data) with highspeed automation [1]. 3D printing technology has enabled the flexibility of complex design and customization; feasibility of restraint or danger accesses; improvement in safety and affordability; and reduction in the construction time, error, and cost [2–4]. 3D printing technology

has been increasingly used in many fields, such as the medicine, food, sensing, metal, automotive, and construction industries [5–7]. It has also attracted substantial attention for habitats in outer space [8]. There are many challenges in the applications of 3D printing technology. One of these is the evaluation of the 3D printing quality. First, various factors can substantially affect the printing quality, including the 3D printer types, printing speed, printing path, extrusion flow rate, and nozzle shape and size. Presently, there are few methods, guidelines, and specifications for measuring and quantifying the qualities of 3D printed objects [9,10]. Printing quality is commonly assessed based on the appearance of the printed products, rather than being quantified systematically. This research gap has substantially limited the

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Corresponding author. E-mail addresses: [email protected] (K. Wi), [email protected] (V. Suresh), [email protected] (K. Wang), [email protected] (B. Li), [email protected] (H. Qin). https://doi.org/10.1016/j.addma.2019.100987 Received 2 August 2019; Received in revised form 21 November 2019; Accepted 30 November 2019 Available online 23 December 2019 2214-8604/ © 2019 Published by Elsevier B.V.

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1.2. Objective of this study

advancement and applications of 3D printing technology in various fields. Printing quality would not be enhanced and neither would printing productiveness be improved unless the former is evaluated effectively. Secondly, not all materials can be conveniently used as an ink for 3D printing. For example, unlike polymers and metals, certain construction materials, such as adobe, plaster, clay, and concrete, need time to solidify and to be capable of holding the weight of the layers deposited above subsequently. In case of concrete, when cement is mixed with water, the paste is generally very fluid at the beginning. As cement hydrates, the paste starts to set, increase stiffness, and develop strength with time [11]. The stiffness and strength will affect the shape-holding ability (or distortion) of the printed concrete. Therefore, the quality of those printed objects is time-dependent. Similarly, freshly printed clay objects are also generally soft and easy to deform; but it hardens and develop strength when moisture is driven away with time. If the strength of these materials does not develop promptly, the printed objects may slump, distort, or collapse with time. This time-dependent material feature necessitates the development of new test methods for rapidly and effectively evaluating the quality of the printed products. Thirdly, it is challenging to evaluate the printing quality of a 3D printed object when the object is still in a plastic state (e.g., freshly printed clay pottery and concrete objects). As the objects can deform straightforwardly, certain test methods may damage the objects during measurement owing to physical contact with the printed objects [12]. Manual measurement of printing qualities is more challenging when the shape of the 3D printed object is complex and the sample is either too small or too large [13,14]. Therefore, it is necessary to explore a test method that can measure the printing quality of a printed object with high accuracy and without potential damage. A 3D structured light scanning system (3D-SLSS) — a non-contact, non-destructive measurement method as discussed in the present study — is capable for addressing this challenge. Various 3D scanning methods have already been used for inspection of the quality of 3D printed objects. Heralic at al. developed a laser scanner system to monitor the surface variations in a laser metal wire deposition, or printing, process [15], where the surface topography of deposited wire was examined layer-by-layer for flatness. Nuchitprasitchai et al. used a stereo vision system and performed 360° scan of an object. They obtained a 3D geometry of the entire object and then examined its surface defects [16]. Recently, Li et al. used 3D-SLSS to monitor the surface topography and fusion area of a 3D object printed with the powder bed fusion additive manufacturing process [17]. The major advantage of 3D-SLSS over other 3D scanning methods is its high speed for capturing quality 3D scans at high accuracy. The 3D scanning process is also easy to automate. In addition to the field of 3D printing, many researchers in other fields, such as medicine, entertainment and biometric authorization, have also used 3D-SLSS [18–20]. Drira et al. developed a 3D scanning framework to obtain geometry of facial shapes, and they detected the missing features [21]. Park and Chang extended this technology in reverse engineering by performing scans to fill missing areas in an object [22]. Zhan et al. used 3D-SLSS to inspect the available clearances in railway tunnels [23]. Although 3D-SLSS is not new, its application for quantitative evaluation of the quality of 3D printed objects with timedependent hardening behavior has been rarely reported. In this study, 3D-SLSS is used to assess the printing qualities of various clay objects (simple hollow cylinders with visible differences in the degrees of geometric distortions). After a sample was scanned, its 3D scanning image was developed and then sliced along its heights and sides to produce a number of 2D plots. Various parameters (e.g., overall geometry, layer height and thickness, and distortion angle and area) of the printed samples were measured from these 2D plots. The printing quality of the sample was quantified by comparing the measured values with the designed values. Details of the experimental work, results, and analysis are presented below.

The goal of the present study is to quantify the quality of freshly printed objects using a rapid, highly accurate, reliable, and non-damaging method. As freshly printed clay products are fragile and conveniently deformed and damaged, the new non-contact, non-destructive technique called 3D-SLSS measurement method has been employed. The following specific objectives were designed to achieve the defined goal: (1) To explore the potential of 3D-SLSS’s application in 3D printing quality evaluation, (2) To develop a test protocol for 3D scan and image analysis, (3) To identify parameters that govern 3D printing quality, and (4) To quantify the printing quality of the samples studied. It is worthwhile to mention that this characterization method can also be extended to quantify other 3D objects printed with materials exhibiting time-dependent behavior, e.g., freshly 3D-printed concrete objects, thus advancing 3D printing concrete technology. 2. Fundamentals of 3D-SLSS A 3D-SLSS operates very similarly to a stereo vision system and functions similarly to the human vision system to acquire 3D information of objects. The stereo vision uses two cameras, while a 3DSLSS uses a projector, which projects multiple phase shifted patterns in a sequential manner, and a camera, which captures images of the surfaces of the examined object rapidly, for acquiring the 3D image [24–27]. The principle of a typical stereo vision system is illustrated in Fig. 1. Here, OL and OR are the centers of projection of the two cameras, EL and ER are the epipolar points, and PL and PR are the 2D projections of the real world point P on the camera image planes. The projection line POL and POR can be viewed as a point PL (or PR ) on the left (or right) camera. The depth information can be calculated by forming a triangulation relationship between the points, PL , and PR . However, the following parameters are required to establish the triangulation relationship: (a) geometric parameters (e.g., focal lengths and principal points) of the camera, (b) spatial orientations of the two cameras, and (c) precise correspondence for each point in a camera image with the other camera image (i.e., the system must be capable of identifying that both the points PL and PR correspond to the real world point). However, correspondence detection becomes challenging in the case of objects with uniform or repetitive texture. In order to solve this problem, 3DSLSS replaces one of the two cameras with a projector. Fig. 2a represents the principle of 3D-SLSS. Here, A represents a projector pixel, D represents a camera pixel, and B is the object point being scanned. The projector projects coded fringe patterns on the object. The projected fringe patterns are distorted because of the

Fig. 1. Principle of stereo vision system. 2

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Fig. 2. Concept of 3D-SLSS.

the nozzle.

geometric variations on the surface of the object. The camera captures images of the object with the distorted fringe patterns. After determining the camera-projector correspondence pair, the 3D geometry can be reconstructed by using the triangulation relationship. The determination of the correspondence between the camera and projector points with the constraints of epipolar geometry and phase lines is illustrated in Fig. 2b. The codifications in the fringe patterns aid in establishing a correspondence between the projector and camera.

3.2. Image analysis 3.2.1. 3D-SLSS test setup The two optic devices of the 3D-SLSS used in the presented study were (1) a digital light processing projector (LightCrafter 4500 [29]), and (2) a high-speed camera (Phantom VEO 340L [30]) (Fig. 4). A microcontroller (Arduino [31]) was used to control these two optic devices simultaneously. The defocus projector and camera had a resolution of 912 × 1140 pixels and 1280 × 960 pixels, respectively, and they were calibrated using the method used by Li et al. [26]. The image acquisition rate was 166 Hz. Three-step phase-shifted patterns were used for phase retrieval, and another set of three-step phase-shifted binary dithered patterns were used for phase unwrapping. This 3D-SLSS could generate scans with approximately 100 μm point accuracy and more than a million data points for one object examined. The system is also robust to noise because it uses phase information (from the phaseshifted fringe patterns) to reconstruct 3D geometry, and it can also quickly scan complicated objects with a very good precision [32–34].

3. Experimental work 3.1. Materials and printing process In this study, a commercial pottery clay (PRAI 3D) was used for 3D printing, and its properties are listed in Table 1. Three clay samples, having good, median, and poor printing qualities evaluated based on the visual inspection of their surface roughness and shape distortion, were selected from 36 samples fabricated with various combinations of printing speed and extrusion flow rate and used for the 3D-SLSS tests and 3D imaging analysis in the present study. All clay samples were printed using a 3D pottery printer (3D Potter 7 [28], Fig. 3a). Table 2 shows the printing speeds (the moving speed of the printer head and platform) and extrusion flow rates of these three selected samples. A circular nozzle was used in the printing, and its diameter (Nd) was set at 5 mm. The stand-off distance (Sd), the distance from the platform to the nozzle, was set at 2 mm. Fig. 3b and c illustrates a 3D model image and a 3D printed object, respectively. The outer diameter and total height of all the samples were designed as 80 mm and 40 mm, respectively. Each printed sample consisted of 20 layers, and each layer had a designed thickness of 2 mm and a layer width was designed as 5 mm. A computer program, Simplify3D® version 4.1 (Simplify3D®), was used in this study to slice the 3D scanned object to be printed. Before printing a designed object, three outskirt layers were printed and discarded to secure a proper extrusion of clay. This is because it was necessary to press the materials for them to be extruded smoothly from

3.2.2. 3D image creation Images of each tested sample were captured from four different directions (at Positions 1 [0°], 5 [90°], 9 [180°], and 13 [270°]) using 3D-SLSS. Fig. 5 shows the side and top views of a typical 3D printed sample. 3.2.3. 2D plots and geometry characteristics After scanning and reconstructing the images of a sample, 16 2D cross-section plots (at Positions 1–16 in Fig. 5b), also called surface profiles, were extracted from the sides of the samples for quantifying the sample height (Htotal), layer thickness (TL), surface roughness (R), surface angle (Sα), and semi-cross-sectional area (XA) (Fig. 6a). Eight 2D cross-sectional plots (Sections 1–9, 2–10, … 8–16 in Fig. 5b) were extracted from the top view image for quantifying the sample outer diameter (DM outer) and layer width (WL) (Fig. 6b).

Table 1 Physical and chemical properties of clay (PRAI 3D) used in this study.

Clay

Water content (%)

Plasticity (IP Atterberg)

Carbonate content (%)

Drying shrinkage (%)

Porosity at Cone 10

25.42

16

0

8.5

0

3

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Fig. 3. 3D printing equipment, modeling, and printed sample.

cross-sectional area (XA), and (7) surface roughness (R). The average values measured from the 16 positions or eight cross-sections are presented as the final measurements describing the geometry characteristics of a sample.

Table 2 Printing parameters.

Sample 1 Sample 2 Sample 3

Printing speed (mm/s)

Extrusion flow rate (mL/s)

30 105 60

0.38 0.38 0.30

3.2.4. Distortion evaluation The geometry of a 3D printed object can be affected by many printing parameters, such as printing speed, extrusion flow rate, printer’s stand-off distance, nozzle shape and size, etc. In the present study, the printer’s stand-off distance and nozzle shape and size were fixed, but the printing speed and extrusion flow rate varied during the sample fabrication. As the purpose of the present study is to develop a method to quantify printing quality using 3D-SLSS, which is exclusively based on the geometric signatures of the printed objects, regardless the printing speed and extrusion flow rate. Therefore, as mentioned previously, only three representative samples, having distinguishing differences in their printing qualities (i.e., good, median, and poor printing qualities) based on their visible surface roughness and shape distortion, were selected for the printing quality characterization. Since printing quality is closely related to printing parameters (e.g., the printing speed and extrusion flow rate), their effects on the quality of the three samples evaluated are discussed here. In case that extrusion flow rate is too slow or too fast relatively to a given printing speed, the printed object may tilt, slump, or collapse with time, resulting in severe distortion, or poor printing quality. A task of this study is to find out if the 3D-SLSS technique could provide sufficient and accurate information on 3D printing quality. In the present study, the distortion of these printed samples was measured by a surface angle (Sα) and a semi-cross-sectional area (XA) calculated from the data points of the 2D plots, as shown in Figs. 8–10. As illustrated in Fig. 6c, the angle of the line of a surface profile, resulting from the linear regression of a 2D plot from an A–A’ cross-section (Line B5–B19, Fig. 6a), was defined as surface angle (Sα). The crosssectional area enclosed by the surface line B5–B19, radiuses across points B5 and B19, and central axis was defined as the semi-cross-sectional area (XA). The average values for the 16 2D cross-sectional plots were used as the final surface angle (Sα) and semi-cross-sectional area (XA) of each sample. In the present study, the designed object was a hollow cylinder to be printed perpendicularly to the base of the printer.

Fig. 4. 3D-SLSS test setup.

Thus, seven geometric values were measured from the 2D plots obtained from the 3D images scanned using 3D-SLSS (Fig. 6): (1) sample total height (Htotal), (2) layer thickness (TL), (3) layer width (WL), (4) outer diameter (DMouter), (5) surface angle (Sα), (6) semi4

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Fig. 5. Different views of sample for image analysis.

Sα. Moreover, the difference between the designed semi-cross-sectional area (1200 mm2) and measured semi-cross-sectional area (XA) is defined as the distortion area (ΔDA), i.e., ΔDA (mm2) = 1200 - XA.

Therefore, its surface angle should be 90°, and the semi-cross-sectional area should be 1200 mm2 if a sample is printed effectively. Otherwise, it can be concluded that distortion has occurred in the sample. The difference between the designed surface angle (90°) and measured surface angle (Sα) is defined as the distortion angle (ΔDα), i.e., ΔDα (°) = 90° -

Fig. 6. 2D plots of geometric characteristics, distortion, and surface roughness. 5

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these first few layers might have significantly affected the deposition of the following few layers, thus resulting in the visible distortion in the near bottom area. As the printing process continued, the deposition of layers became stabilized, and the distortion decreased thereafter. This implies that it is essential to ensure the deposition of stable layers at the beginning of a printing process. For a perfect cylinder, the top view of the sample should show a ring with uniform color and uniform thickness. This is because all the printed layers would overlap each other perfectly. Fig. 7 illustrates that the top-view 3D images of Sample 1 had the least differences in its ring color and width (except the location where a new layer started). Sample 3 exhibited the most dramatic variation in its ring color and width at the plane level, indicating the lowest printing quality among all the samples studied. It should be noted that protruded clay was present on the top surfaces of all the three samples studied. This was because the 3D printer extruded more clay than the designed amount when it finished printing. To overcome this problem, the amount of printing material should be controlled precisely to limit its effect on the geometry of the final products.

3.2.5. Surface roughness Surface roughness is a parameter for controlling the quality of manufactured products. It may be expressed in different ways by different industries. In this study, the arithmetic average height (Ra), root mean square roughness (Rq), and 10-point height (Rz) were used to quantify the surface roughness of the printed clay samples. This is because each of these methods exhibits a benefit unique to itself. It would also be effective to use more than one method owing to the uncertainty in the printing quality of 3D printed objects. The roughness values were calculated using Eqs. (1)–(3) and the data obtained from the A–A’ crosssections (i.e., B5–B19 in Fig. 6a) of a sample’s 3D scan images. The roughness values of the 16 2D cross-sectional plots captured by 3D-SLSS from each sample were calculated, and the average value was used as the final roughness value of the sample. n

Ra =

1 ∑ |y | n i=1 i

Rq =

1 ∑ y2 n i=1 i

(1)

n

(2) 4.2. Surface profiles

where yi is the distance from the outermost line to the mean line at point i. n is the number of points i.

Figs. 8–10 show the 2D cross-sectional plots (surface profiles) from the 16 positions illustrated in Fig. 5b, for Samples 1, 2, and 3, respectively. Only the 5th–19th layers (B5–B19) are shown in these plots considering the instability at the beginning and end of the printing process. In Figs. 8–10, the saw-tooth shaped lines represent the outer surface profiles of the samples studied, at specified positions. If a cylindrical sample was printed perfectly, the slopes of all these lines should be 90° because the side of the cylinder was set as being perpendicular to the base. Moreover, all the sawteeth should be of uniform size as the layer height and width were designed to be identical. Although the 3D images (Fig. 7) indicate that Sample 1 exhibited relatively good geometry characteristics, Fig. 8 reveals that the slopes of a few sample surface lines (e.g., positions 13–16) were not 90°, and the sawteeth of the lines were not identical. These signified measurable distortion, which might not be identified by a visual inspection. Figs. 9 and 10 show that compared with Sample 1, the slopes of the side surface lines of Samples 2 and 3 changed with the sample height much more significantly. The maximum difference from the bottom point to the top point of these samples’ surface lines was approximately 3.5 mm, 4.5 mm, and 5.5 mm for Samples 1, 2, and 3, respectively. The positions of these 2D plot lines (surface profiles) changed gradually for Sample 1, albeit quite substantially at the bottom few layers of Samples 2 and 3. Whereas these subtle differences in the sample geometry were not detected by visual inspection, the 2D plots generated from the SLT revealed them clearly. The accurate measurement of the geometry and surface characteristics is a key advantage of using an SLT for testing 3D printing quality. As explained in Sections 4.3 and 4.4, the geometric characteristics of the 3D printed samples can be further quantified using the 2D plots captured by 3D-SLSS.

5

1 Rz = ∑ R Pi − R vi 5 i=1

(3) th

where R Pi and R vi are the i tively.

highest peak and lowest valley, respec-

4. Results and discussions 4.1. 3D images Fig. 7 shows the 3D images of the three samples studied. As mentioned in Section 3.2.2, the images were captured from four sides and the top. The depth information of the 3D images can be interpreted by the color-bar located at the right corner of the figure. The yellow color signifies a short distance from the tested sample to the camera used, whereas the blue color signifies a large distance from the tested sample to the camera used. This color difference enables the identification of the subtle geometry changes in the test sample from its 3D images. As the 3D printed samples were cylinders, the color gradation in the 3D images of each sample captured from the four sides should be identical. Fig. 7 shows that the color gradations in the four side-view images of Sample 1 were more or less uniform. However, they were not so in those of Samples 2 and 3. The difference in color gradations in the four images captured from the four sides of a specified sample indicated that the shape of the printed sample was not perfectly cylindrical. For a perfect cylinder, the central area of the sample should exhibit a uniform yellow color along its height as it had the shortest distance from the camera. Moreover, the edges of the sample should exhibit a uniform blue color as they were at a larger distance from the camera. However, various gradations along the sample’s height is apparent in the four side-views of Samples 2 and 3. Herein, more of dark blue color can be observed in the central areas of Sample 3, indicating the most severe distortion among the samples studied. Distortion can be observed also in the side-views of the sample. As shown in Fig. 7, Sample 1 exhibited a relatively good cylindrical shape, whereas Samples 2 and 3 exhibited evident distortion in the near bottom area. Between these, Sample 3 appeared to exhibit the highest degree of distortion. This distortion could result from the first few layers where the printed material was not deposited in the positions as designed. It could be because the printing material was not loaded appropriately (not uniformly consolidated in the area close to the nozzle) or the nozzle was not in a right position due to some unexpected vibrations or disturbances during the printing process. The positions of

4.3. Geometric characterization The geometric parameters, e.g., total height (Htotal), outer diameter (DMouter), layer thickness (TL), and layer width (WL), of each printed cylinder sample were determined from the 2D plots of the sample. Three samples were analyzed, and their 2D plots shown in Figs. 8–10. Fig. 11 illustrates the comparison between the designed and measured values of each geometrical result. Evidently, all the measured values obtained by 3D-SLSS were not equal to the designed values. This indicated that none of the samples was printed perfectly, i.e., defects were present in the printed samples. It was observed that the measured total heights of Samples 1, 2, and 3 were 38.20, 38.30, and 37.79 mm, respectively. These were lower 6

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Fig. 7. 3D images of samples captured by different side and top views. (For interpretation of the references to color in the text, the reader is referred to the web version of this article.)

The measured layer thicknesses of Samples 1, 2, and 3 were 1.829, 1.865, and 1.838, respectively. These are smaller than the designed value of 2 mm (Fig. 11c). Similar to the aforementioned explanation, the printed layers were not likely to have withstood the weight of the layers above without deformation. However, it should be noted that although the layer thickness of Sample 3 was closer to the designed value than that of Sample 1, Sample 3 may not have been better printed than Sample 1 because its standard deviation is the largest among the three samples. The measured layer widths of Samples 1, 2, and 3 were 5.605, 5.985, and 4.837, respectively. These were larger than the designed value of 5 mm (Fig. 11d). The increased layer widths largely resulted from the layer slump, i.e., the deformation under the weight of the layers above. Fig. 11d shows that the layer width of Sample 2 was the

than the designed value of 40 mm (Fig. 11a). This may have occurred because the deposited clay layers had slumped as they could not withstand the weight of the layers above. The non-zero standard deviation of the heights measured from the 16 positions indicates potential distortion in the sample. The distortions that occurred at the lower layers could have resulted in the decreased sample height. The measured outer diameters of Samples 1, 2, and 3 were 71.67, 72.12, and 69.19 mm, respectively. These were smaller than the designed value of 80 mm (Fig. 11b). It may be attributed to either inaccurate nozzle movements or distortions of printed layers. The distortion, which occurred at the lower layers, was detected in the 3D images. Because the standard deviation of the outer diameters was not zero, one can infer that the shapes of the horizontal cross-sections of the samples were not perfect circles. 7

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Fig. 8. Surface profiles (2D plots) of Sample 1 (printing speed = 30 mm/s; extrusion flow rate = 0.38 mL/s).

data used for quantifying the distortion angle and area were those extracted from the 2D plots shown in Figs. 8–10. Table 3 presents the degree of distortion expressed as the differences in the measured and designed parameters: ΔDα = 90° - Sα, and ΔDA = 1200 mm2 - XA. ΔDα represents the extent of tilt of a 3D printed object, whereas ΔDA indicates the extent of distortion of the object. It is effective to use both of these to describe the degree of distortion. Based on the measured results, Sample 1 may have exhibited the best printing quality among the three samples studied because it had the least distortion angle and a distortion area very close to the least one. Sample 3 exhibited the worst printed quality as it had the largest distortion angle and distortion area. Moreover, for Sample 3, the standard deviations of these measurements were also the highest. The high inconsistency in their surface geometric parameters indicates a high degree of distortion. As indicated in Table 2, for Sample 3, the printing speed was 60 mm/s, which doubled that of Sample 1, whereas the extrusion flow rate was 0.30 mL/s, which was lower than that of Sample 1. This implied that when the printer moved too fast while extrusion flow rate was slow, the material that was extruded out was not sufficient to form a proper layer with uniform width and thickness as designed, thus causing distortion. Therefore, selection of a proper combination of the printing speed and extrusion flow rate is critical for the printing quality.

largest and that of Sample 3 was the smallest. As presented in Table 2, the extrusion flow rates of Samples 1 and 2 were equal, whereas the printing speed (or the moving speed of the printer’s nozzle and base) of Sample 2 was higher than that of Sample 1. It is likely that the faster printing speed facilitated the spread of the printing material. For Sample 3, although its printing speed was higher than that for Sample 1, smaller layers widths were measured because insufficient printing material was extruded at the low extrusion flow rate. 4.4. Distortion During a 3D printing process, distortion is likely when the printing materials are incorrectly deposited by undesirable external or internal forces, e.g., those exerted by entrapped air bubbles during loading of printing materials and unexpected vibrations and contacts during and after printing. In addition, as the previously deposited layers experience more of the weight of the subsequently deposited layers, compaction on the layers below may occur and cause distortion when the printed layers have not gained sufficient strength. Distortion significantly affects the appearance and accuracy of the printed products, making it essential to quantify the degree of distortion of 3D printed objects. Fig. 12 illustrates the comparison between designed and measured values of surface angle (Sα) and semi-cross-sectional area (XA). The 8

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Fig. 9. Surface profiles (2D plots) of Sample 2 (printing speed = 105 mm/s; extrusion flow rate = 0.38 mL/s).

The root mean square roughness (Rq), also known as RMS, is the standard deviation of the distributed surface heights [35,36]. Compared to Ra, Rq is a more suitable parameter when the surface has large deviations from the mean line [35]. The 10-point height (Rz) is also a roughness parameters that can be used (in place of Ra) in an object having occasional high peaks or deep valleys [35]. These three roughness indexes were selected to capture the various surface features of the 3D printed samples. Fig. 13 shows the different surface roughness indexes obtained from the three samples studied (Compared to Ra and Rz, the scale of Rq is small. Therefore, an enlarged figure of Rq is also displayed in Fig. 13). The figure illustrates that Sample 1 exhibited the smallest surface roughness index or the smoothest surface. Sample 3 exhibited the largest surface roughness index or the roughest surface, which may be related to the degree of distortion of the samples. Although different in value, all the surface roughness indexes displayed similar trend: Sample 1 exhibited the lowest index values and Sample 3 the highest index values. Rz had the largest values and Rq the lowest values for all the samples. Rz displayed the largest difference among the samples because it used the five highest and lowest points, resulting in significant differences among the samples. This implied that Sample 3 exhibited the largest variation in the surface profile curve, among the samples. These results indicate that Rz could be an optimal index for characterizing the surface condition of a sample with dramatic changes.

It is noteworthy that Sample 2 exhibited a distortion angle that is significantly higher than that of Sample 1, albeit a distortion area marginally smaller than that of Sample 1. This indicates that it is essential to evaluate both these distortion parameters, rather than one of these, as measuring only the distortion area may result in a false conclusion. As these two parameters yield different conclusions, a more sophisticated evaluation method (presented in Section 4.5) to determine the overall performance of the printing quality based on precise quantification should be explored. Table 3 also reveals that none of the samples studied had a distortion angle of zero or a distortion area near to zero. This implies that the printing quality of these samples was not adequate. This was largely because the total height (or layer thickness) and outer diameter of the samples were significantly smaller than the designed values, which caused the increased distortion area. 4.5. Surface roughness The surface conditions of the printed samples were evaluated by different roughness indexes (Ra, Rq, and Rz from Eqs. (1)–(3), respectively). Each index was calculated based on different features of the sample surfaces. The arithmetic average height (Ra) is the most commonly used roughness parameter because it is convenient to measure and provides a general description of the variances of heights [35,36]. 9

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Fig. 10. Surface profiles (2D plots) of Sample 3 (printing speed = 30 mm/s; extrusion flow rate = 0.30 mL/s).

printing quality. Table 4 presents the results of the diagnosed area of deficiency calculated from Fig. 14 for the three samples studied. The results indicate that all the three samples had a certain degree of deficiency in printing quality, even for the one that appeared very good visually (Sample 1). As the diagnosed area of deficiency increased from Sample 1 to Sample 3, one can conclude that Sample 1 exhibited the best overall printing quality, whereas Sample 3 exhibited the lowest overall printing quality among the three samples. Finally, the quantification techniques and methods used in this present study are relatively simple and inexpensive. It is likely that they can be rapidly extended to various industries for quantifying the printing quality of other 3D products, particularly those that require significant time to transfer from a fluid state to a solid state.

4.6. Integrated evaluation As mentioned in Section 4.4, the two measurements (ΔDα and ΔDA) had yielded different conclusions while comparing Samples 2 and 1. A similar issue occurred when the measured parameters in Fig. 11 were compared. To address this issue, an integrated evaluation method was used in the present study to evaluate the overall performance of the printed sample using the results of all the measurements. Fig. 14 demonstrates the method for evaluating the overall performance of all the printed samples. In the figure, six measurements were considered: (1) sample total height (Htotal), (2) layer thickness (TL), (3) layer width (WL), (4) outer diameter (DMouter), (5) surface angle (Sα), and (6) semi-cross-sectional area (XA). For each measurement, the percentage of difference between the measured value and designed value of each sample was plotted on the corresponding axis. Three data points, one for each printed sample, appeared on each of the axes. Because there is no designed or standard value for the surface roughness (R) of a printed object at present, the surface roughness was not included in the figure. After plotting the data on the corresponding axis, the data from each sample were connected using straight lines to form an enclosed area. As the data on each axis indicated the deficiency in the printed samples, the enclosed area of each sample in Fig. 14 is named as the “diagnosed area of deficiency.” (As the unit of each axis is percentage, no unit is provided for this area in the present study.) If the diagnosed area of deficiency for a sample is near to zero, it signifies that the quality of the printed sample is near to what was designed. Meanwhile, a large diagnosed area of deficiency signifies a low overall

5. Conclusions In this study, a non-contact and non-destructive measurement method, 3D-SLSS, was employed for evaluating the printing qualities of three representative clay objects (hollow cylinders) with different degrees of surface roughness and shape distortion. 3D scanned images of these clay samples were developed using 3D-SLSS, and they were then sliced along the sides (perpendicular to the bases) of the cylinder samples to generate a number of 2D plots. Various parameters (e.g., sample total height [Htotal], outer diameter [DMouter], layer thickness [TL], layer width [WL], surface angle [Sα], semi-cross-sectional area [XA], and surface roughness [R]) of the printed samples were measured 10

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Fig. 11. Comparison between designed and measured geometry of printed samples.

Fig. 12. Comparison between designed and measured surface angle and semi-cross-sectional area. 11

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Table 3 Distortion angle (ΔDα) and distortion area (ΔDA) of printed samples.

Designed Sample 1 Sample 2 Sample 3

Average STD Average STD Average STD

Surface angle (Sα) (°)

Semi-crosssectional area (XA) (mm2)

Distortion angle (ΔDα) (°)

Distortion area (ΔDA) (mm2)

90 91.0898 1.6030 94.2861 1.5512 96.7067 2.4848

1200 1,086.6637 40.31647 1,066.0502 39.77534 1,035.7724 53.2184

0 1.0898 1.6030 4.2861 1.5512 6.7067 2.4848

0 173.7501 40.3785 196.9582 52.1408 254.1021 82.1615

Table 4 Areas calculated from diagnosis plot.

Diagnosed area of deficiency

Designed

Sample 1

Sample 2

Sample 3

0

165.367

230.107

274.876

(1) 3D-SLSS is a simple, fast, and relatively inexpensive characterization method. The 2D plots sliced from 3D scanned images captured by 3D-SLSS provided various accurate data for defining characteristic parameters, measuring geometry, and quantifying the degree of distortion of 3D printed products. (2) None of the three samples examined had measured values equal to the designed ones, even for those that appeared well printed. This indicates that certain defects or deficiencies are always present in printed samples. Therefore, it is important to quantify the characteristic parameters for controlling printing quality. (3) Compared with the designed object, the printed clay samples generally exhibited reduced total height and diameter, reduced layer thickness (corresponding to the increased layer width), measurable distortion, and visible surface roughness. This is mainly because in a fresh state, printed clay material deformed with time under the weight of the layers above. Similar phenomena occur for other construction materials (e.g., concrete), which require a certain time for solidification. Therefore, quantifying the quality of these printed products is very important. (4) The degree of distortion (D) of printed objects can be evaluated by both the distortion angle (ΔDα = 90° (designed) - Sα) and distortion area (ΔDA = 1200 (designed) - Xα). These two parameters did not always correspond to each other. For example, Sample 2 had a distortion angle (ΔDα) higher than that of Sample 1, albeit a distortion area (ΔDA) marginally smaller than that of Sample 1. This indicates that it is essential to evaluate both these distortion parameters, rather than one. (5) The diagnosed area of deficiency, calculated from the multiple-axis chart, provided a quantitative evaluation for the overall qualities of the printed samples. Based on this calculation, the clay sample made with a low printing speed (30 mm/s) and marginally higher extrusion flow rate (0.38 mL/s) (Sample 1) exhibited the least area of deficiency, or the highest printing quality. The sample made with a higher printing speed (60 mm/s) and reduced extrusion flow rate

Fig. 13. Different surface roughness parameters of 3D printed objects.

from these 2D plots. The measurements were compared with the designed values. The differences (in percentages) between the measured and designed values were plotted in a multiple-axis chart, from which a diagnosed area of deficiency of each sample was computed. The overall qualities of all three printed samples were then quantified based on their diagnosed areas of deficiency. The following conclusions can be drawn from the present study:

Fig. 14. Diagnosis plot of printing deficiency. 12

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(0.30 mL/s) (Sample 3) exhibited the largest area of deficiency, or the lowest printing quality. The results indicate that the selection of a proper combination of the printing speed and extrusion flow rate is critical for the printing quality control. Further study is necessary to identify the best combination for various printing and material parameters.

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Declarations of conflict of interest None. Data availability statement All the data presented in this paper are available from the corresponding author upon request. The data includes (1) 3D scan image data; (2) 2D plot data; and (3) geometry, distortion, and surface roughness measurements. Authorship contributions (1) Kwangwoo Wi: Conducted 3D printing experiment and analyzed all test data. (2) Vignesh Suresh: Conducted 3D Structured Light Scan and analyzed the related data. (3) Kejin Wang: Principle Investigator of the research project and correcsponding author responsible for all experimental design and data analyses as well as the paper writing. (4) Beiwen Li: Provided significant inputs on the 3D structured light scan and data analysis and paper writing. (5) Hantang Qin: Provided significant inputs on the 3D printing and paper writing Acknowledgments This work is deviated from a research project on 3D printing concrete, the latter of which is sponsored by the Iowa Highway Research Board (Project no: TR-756), Iowa, USA. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.addma.2019.100987. References [1] ASTM International, Standard Terminology for Additive Manufacturing – General Principles – Terminology, ISO/ASTM 52900:-15, 2015, https://doi.org/10.1520/ ISOASTM52900-15. [2] F. Bos, R. Wolfs, Z. Ahmed, T. Salet, Additive manufacturing of concrete in construction: potentials and challenges of 3D concrete printing, Virtual Phys. Prototyp. 11 (3) (2016) 209–225, https://doi.org/10.1080/17452759.2016.1209867. [3] G. De Schutter, K. Lesage, V. Mechtcherine, V.N. Nerella, G. Habert, I. Agusti-Juan, Vision of 3D printing with concrete—technical, economic and environmental potentials, Cem. Concr. Res. 112 (2018) 25–36, https://doi.org/10.1016/j.cemconres. 2018.06.001. [4] E. Lloret, A.R. Shahab, M. Linus, R.J. Flatt, F. Gramazio, M. Kohler, S. Langenberg, Complex concrete structures: merging existing casting techniques with digital fabrication, Comput. Aided Des. 60 (2015) 40–49, https://doi.org/10.1016/j.cad. 2014.02.011. [5] T.T. Le, S.A. Austin, S. Lim, R.A. Buswell, R. Law, A.G. Gibb, T. Thorpe, Hardened properties of high-performance printing concrete, Cem. Concr. Res. 42 (3) (2012) 558–566, https://doi.org/10.1016/j.cemconres.2011.12.003. [6] C. Schubert, M.C. Van Langeveld, L.A. Donoso, Innovations in 3D printing: a 3D overview from optics to organs, Br. J. Ophthalmol. 98 (2) (2014) 159–161, https:// doi.org/10.1136/bjophthalmol-2013-304446. [7] L.E. Murr, Frontiers of 3D printing/additive manufacturing: from human organs to aircraft fabrication, J. Mater. Sci. Technol. 32 (10) (2016) 987–995, https://doi. org/10.1016/j.jmst.2016.08.011. [8] C.T. Mueller, 3D printed structures: challenges and opportunities, Structure (2016)

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