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Surface roughness of 3D printed materials: Comparing physical measurements and human perception
Materials Today Communications 19 (2019) 300–305
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Materials Today Communications journal homepage: www.elsevier.com/locate/mtcomm
Surface roughness of 3D printed materials: Comparing physical measurements and human perception
T
⁎
Jess Hartcher-O’Briena, , Jeremy Eversb,c, Erik Tempelmanb a
Perceptual Intelligence Labs, Faculty of Industrial Design Engineering, Delft University of Technology, the Netherlands Advanced Manufacturing Group, Faculty of Industrial Design Engineering, Delft University of Technology, the Netherlands c Materials Department, Ultimaker B.V., Watermolenweg 2, 4191 PN, Geldermalsen, the Netherlands b
A R T I C LE I N FO
A B S T R A C T
Keywords: FDM Surface roughness Discrimination thresholds Haptics Designed materials
This study concerns the perceived roughness of 3D printed material samples (print process: fused deposition modelling, or FDM), generated across changes in print speed, build angle, and layer height. Physical sample surface roughness parameters Ra and Rq were first obtained via optical scanning. Next, using a custom-designed apparatus, surface roughness perception was assessed via a psychophysical procedure that identified the just noticeable difference in roughness through the sense of touch alone. By comparing both data sets, this study concludes that for FDM-printed materials, objective surface roughness parameters (Ra, Rq) cannot adequately predict users’ haptic experience. This finding is of importance for all 3D printing applications where equally perceptible roughness is desired. As a whole, the study highlights the role of 3D printing as a new tool for the science of haptics and as a means for generating new material qualities by design.
1. Introduction To successfully manipulate an object, an estimate of the object's surface texture is useful: consider how lifting an object of a given weight and size requires less grip force if the object's surface is rough rather than smooth. Surface texture also informs perception. In fact, for tactile, a.k.a. haptic discrimination, surface roughness has been suggested to be the critical parameter, with other parameters, i.e. compliance, viscosity, or thermal properties, being of secondary importance at best [1–5]. Surface roughness perceptions have primarily been quantified for materials for which surface properties can be varied systematically [6–8,4]. For man-made materials, the acuity with which differences in roughness can be perceived has been found to be within a few microns [8,9]. Roughness perception of natural materials has also been explored [1], suggesting that the detection threshold for a change in surface roughness is in the range of 1 μm for active interactions with these surfaces, increasing slightly for passive exploration. Improvements in 3D printing technology allow fabrication of entirely novel stimuli, including those with designed surface textures. This unlocks a new range of “designed material surfaces” with which to probe the human somatosensory system [9,10]. The present study makes use of such novel surfaces, and examines the relationship between physical roughness and perceived surface roughness, when surface production is determined by specific 3D print parameter changes. ⁎
3D printing, more formally known as “additive manufacturing” is characterized by its incremental addition of material in a particular build direction, usually the vertical, or z-direction, under full digital control, in order to create three dimensional objects [11]. Especially for low-end use, the variety known as fused deposition modelling (FDM; also known as “material extrusion”) is currently common. In FDM, lines of molten thermoplastic are deposited next to one another in layer upon layer, with line width, deposition or print temperature and speed, and layer height being the key process parameters. Line width and layer height have an obvious direct influence on roughness: wider lines and thicker layers, while reducing printing time, will result in a coarser print. Print temperature and speed also effect roughness, as these parameters jointly decide how much heat is put in per unit of time, and hence to what extent the molten material can settle. Next, print orientation i.e. surface angle can affect how the final part will look and feel. Tempelman et al. [11], defined five quality attributes for manufacturing processes, including 3D printing: geometric tolerance, surface roughness, localized defects, material properties; and reproducibility. Surface roughness then refers to the topographical structure of a surface part, which can range from smooth to coarse, depending on build material and printer settings. The available literature confirms that print speed, specifically extrusion speed, indeed plays a significant role in the printed surface's physical roughness [12], as well as its overall quality [13]. Print
Corresponding author at: Building 32, 15 Landbergstraat, 2628CE, Delft, the Netherlands. E-mail address: [email protected] (J. Hartcher-O’Brien).
https://doi.org/10.1016/j.mtcomm.2019.01.008 Received 11 September 2018; Received in revised form 6 January 2019; Accepted 7 January 2019 Available online 21 February 2019 2352-4928/ © 2019 Elsevier Ltd. All rights reserved.
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[20].
orientation also affects surface roughness [14], creating additional irregularities on the 3D printed surface in case of steep overhangs. Results for the influence of layer height [15–17] are rather mixed, with some studies finding layer height to have a large impact on surface roughness [16,17], while another study suggests its influence to be minimal [15]. Changes in layer height has also been found to provide highly variable results, primarily with respect to material compliance, rather than surface properties [18]. Interestingly, studies so far have focussed on objectively measured roughness attributes resulting from changes in print parameters, but not on the perceptual evaluation of the surface resulting from systematic changes in print parameters [19]. To address this issue, we set out to measure roughness discrimination thresholds as the perceptual attribute of 3D print samples manufactured across a range of printing conditions. We manipulated printer settings individually for print speed, build angle, and layer height. Next, we objectively identified the surface roughness for step changes in these print parameters, using optical scanning. Then, a dedicated psychophysical apparatus and test set-up was used to ensure a purely tactile experience in test subjects (N = 9), unbiased by sound and vision. Comparison of the two data sets allowed us to see to what extent perceptual and objective measurements correlate.
2.2.1. Sample preparation All samples were printed on the centre of the Ultimaker 3 build plate. The models were placed at this position, as build plate orientation has been shown to exert an influence on the surface roughness. The technical limitations of the Ultimaker 3 were taken into account when determining the range of print parameters and their step size changes used in the current study. All parameters for printing were controlled and adjusted in Ultimaker’s slicing software Cura version 2.4. The layer height was measured in millimeters(mm), temperature in Celsius(°C), speed in millimeter per second(mm/s), surface angle in degree(°) and line width in millimeter(mm). The printer settings used were based on the standards used by Ultimaker B.V. (see Table 1) for their Ultimaker 3 desktop printer. The standard values for non-manipulated parameters were determined by Ultimaker Cura v. 2.4 standards, which equate to a print angle, layer height and speed of 40˚, 0.2 mm and 60 mm/sec respectively. Stimuli were thus printed standing upright at said angle; to avoid vibrations of stimuli during printing, they were printed with narrow sidewalls for support. These were broken off after printing, without damage to the actual sample surface area under consideration. The parameter bounds were set such that the print process would result in a reliable surface finish for the FDM part. Exceeding this range is possible however it drastically decreases the reliability of the 3D printer and parts. For print layer height the lower limit was set to 006 mm and the upper limit to 0,2 mm. For print temperature the range was between 250 °C and 280 °C. This is optimal for the Polycarbonate (PC) material used. The limits for print speed were set to 20 mm/s for the lower and 100 mm/s for the upper limit. In the Cura profiles for the material PC the cooling is not active, but to investigate the effect the parameter was included and set to 50%. The angle at which the surface is printed can significantly influence the surface roughness as it directly affects the buildup of layers. The lower limit of the angle orientation was set at 15°(from the horizontal plane). From this point the layers of the overhanging surface start to print far enough from the adjacent layers to begin to detach and form loose lines. The upper limit was set to 75°. For this parameter we included an additional angle: 90°. This was done to inform Ultimaker about the effects of an additional upper limit for this print parameter. For print speed, the lower limit was set to 20 mm/s with an upper limit of 100 mm/sec. Each parameter range is divided into 5 samples to see the change of roughness per step size. These settings are summarized in Table 1. To determine the physical roughness for the current samples, optical imaging measurements were performed for each sample thrice using a Micro-epsilon 2910-10BL laser line scanner. This device uses optical triangulation and has a resolution of approximately 1 μm – too low for high-precision parts, but more than adequate for FDM prints. The scanner was encapsulated in a custom-built test box to ensure reproducible test results, and fully automated (incl. data processing) for ease of use. The optical scanner imaged the 40 mm sample in 10 mm scan windows. For each surface, estimates were derived from three scan repetitions which were used to estimate the physical surface roughness (Ra and Rq), with Ra calculated as:
2. Method 2.1. Observers Nine volunteers took part in each part in each condition of this study. The mean age of the observers was 26 years and all had normal somatosensory processing according to self-report. Almost half of the volunteers for the study were employees of Ultimaker B.V. and therefore had ample experience with 3D print samples. Participants gave their informed consent prior to taking part in the study. 2.2. Apparatus and stimuli The stimuli were flat, rectangular 3D printed samples measuring 40 by 20 by 3 mm produced under different sets of print parameter conditions (see Table 1). The shape and size of these stimuli was designed so that both an optical scanner and an index fingertip could easily scan the surface to extract an estimate of surface roughness. The thermoplastic material used for the stimuli was PC (polycarbonate). Compared to the more common PLA and ABS materials, PC material has the advantage of being highly robust with respect to print overhang angle Table 1 FDM Surface roughness μm) values and print parameters used to produce them. Roughness estimates Ra (and Rq) were quantified by optical scanner measurements (with Ra and Rq calculated according to Eqs. (1) and (2) respectively), for the 3-D printed surfaces under different print speed (mm/sec), surface angle (°) and layer height (mm) conditions. Upward
Parameter
Sample
Ra (μm)
Rq (μm)
Speed (mm/sec)
20 40 60 80 100 15 30 45 60 75 90 0.06 0.10 0.13 0.17 0.20
B5 B4 B3/B3 (ref) B2 B1 C6 C5 C4/C4 (ref) C3 C2 C1 D5 D4 D3/D3 (ref) D2 D1
29 27 26 25 24 30 29 24 19 14 11 15 17 27 28 34
35 33 32 31 30 35 34 30 24 16 14 18 21 33 34 41
Angel (°)
Layer height (mm)
l
Ra =
1 ∑ | Z(x ) | l x=1
(1)
where l is the evaluation length of the surface and Z(x) is the profile height function. Rq, an estimate of the root mean square, is calculated as: l
Rq =
301
1 ∑Z 2 l x=1 x
(2)
Materials Today Communications 19 (2019) 300–305
J. Hartcher-O’Brien, et al.
The primary aim of the study was to understand how sensitive human subjects are to changes in surface roughness resulting from altering 3D print parameters. Surprisingly, the discrimination thresholds produced by altering print parameters were found to be significantly different from one another, F(2, 16) = 8.68, p < 0.01. The thresholds for print speed were found to be significantly lower than the same thresholds produced by changing build angle, t(8) = -7.73, p > 0.05 and print layer height, t(8) = -11.63, p > 0.05. The difference in haptic discrimination thresholds between build angle and layer height however were not significant, t(8) = 1.26, p > 0.05. Given the overlap in the surface roughness values (Ra) as objectively assessed via optical scanning, it is clear that these discrimination thresholds cannot depend on these physical roughness value estimates per se. The smallest change in physical estimates of surface roughness relative to the reference stimulus was 1 μm for speed, 5 μm for build angle, and 1 μm for layer height (Fig. 3). Given the non-linear changes in the comparison stimuli these values represent the closest correspondence to the perceptual discrimination thresholds of surface roughness, as estimated via the psychophysical procedure reported here. In order to try and account for the differences observed, these values were cross-referenced with the perceptual thresholds. However, for print speed the difference thresholds were not found to be significantly correlated (r = -0.57, p > 0.05), nor were they for build angle (r = -0.03, p > 0.05) or layer height (r = -0.50, p > 0.05). So, it seems that not only do scanners and humans assess the same stimuli differently, but humans apparently sense differences caused by print speed, build angle or layer height with different acuity – an unexpected result.
Ra and Rq are therefore objective models of the physical surface roughness of the FDM samples produced for changes in print speed, surface angle and layer height. The stimulus roughness values (μm) can be seen in Table 1 for printer speed, surface angle and layer height respectively. These samples form the basis for the psychophysical procedure used to estimate haptic roughness discrimination thresholds. 2.3. Procedure A two-alternative forced choice procedure was used to identify the discrimination threshold, quantified as the change in roughness needed to correctly identify the rougher sample 75% of the time. Observers were blindfolded and sat in front of a table positioned at elbow height. The observer's index finger rested in a custom-built finger brace to ensure that exploration height remained constant throughout the task. In addition to the blindfold, observers also listened to noise via headphones, to exclude the possibility of acoustic feedback cues affecting the results. On each trial observers used bare-finger contact to compared two stimuli presented via the custom-built apparatus which moved the samples at a rate of 40 mm/sec across the volar surface of the observer's index fingertip. The stimulus always returned to the central start position at the end of the scan interval. The fingertip scanned the 40 mm dimension of the surface. Stimuli were cleaned with acetone on removal from the setup to minimize microstructure changes of the surface due to bare finger interaction. The reference stimulus, (see Table 1) was presented on every trial, and its position varied randomly on a trial-by-trial basis (either the first or second sample explored). Observers were not told that the reference stimulus was used in all trials and they did not receive any feedback with respect to their responses. The observer's task was simple: to indicate which of two consecutive surfaces felt rougher, with 'the first' or 'the second' sample experienced (see Fig. 1). The physical roughness (μm) of the comparison stimulus varied according to a one-up, one-down, interleaved staircase procedure, which terminated after fifteen reversals.
4. Discussion 4.1. Significance of results The key finding to emerge from this study is that the perceptual discrimination thresholds of surface roughness cannot be accounted for, and hence cannot be predicted, by either Ra or Rq values of the physical roughness of the FDM samples. This can be seen in the comparison of thresholds across print parameters where Ra and Rq values are equitable while the perceptual thresholds are definitely not. Incidentally, our findings show that Rq and Ra are well-correlated; in fact, on average, we consistently found Ra = 0.8·Rq. This suggests consistency and high reproducibility of the printed surfaces with respect to surface topography, as the presence or absence of a few large defects would affect Rq much more than it would affect Ra. Methodological factors that could potentially cause such a difference in discrimination thresholds across conditions are: the number of comparison samples used to test the discrimination thresholds, which in turn influences the how well the function fit represents the proportion rougher response data; the variability across individual observer estimates and the resolution of the optical scanner. If we consider the
3. Results The data for each condition and each observer were fit with a cumulative Gaussian function to extract the discrimination threshold (Fig. 2 a, b, c) for print speed, angle and layer height respectively. Given we did not change conditions within a print parameter we did not expect any bias and thus the point of subjective equality, or PSE, was set to the reference stimulus value for each print parameter. Given the staircase procedure to acquire the data, the psychometric function fits were weighted for the number of repetitions of each stimulus during the measurement procedure. The comparison values for angle and layer height are a function of non-equal stimulus step changes in physical roughness which correspond to linear step changes in the print parameter inputs. For speed, the roughness estimates are close to linear with respect to the input print parameters.
Fig. 1. Apparatus and stimuli. a) The custom-built apparatus mechanically moved the two stimuli consecutively across the observer's finger pad at 40 mm/sec for each trial. The observer indicated which surface was rougher, that experienced in the first or second interval. b) Example stimulus range decreasing in surface roughness μm) with the reference stimulus present on every trial. 302
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Fig. 2. Perceived surface roughness data from an example subject. Proportion rougher data and psychometric fits as a function of changes in (a) speed (b) build angle and (c) layer height. Roughness estimates were acquired using a staircase procedure.
Fig. 3. Physical and relative roughness discrimination threshold estimates. Average physical roughness estimates (diamonds) and average (across observers) discrimination threshold (bar) as a function of changes in print a) speed, b) build angle and c) layer height. The corresponding change in print parameter equivalent to the perceptual threshold (error bar), for a discriminable change in parameter is illustrated (dotted line).
between the change in surface roughness, obtained from optical scanning, and the perceptual discrimination thresholds for changes in print speed, angle or layer height, the small, primarily equal changes in surface roughness produced by print speed give rise to a low threshold suggesting observers are sensitive to changes produced under speed change conditions. The changes in surface roughness spanned by changes in print speed is in the range of 5 μm. For print angle and layer height changes in the surface encompasses a difference of approximately 20 μm. One potential way to interrogate the differences in discrimination thresholds for the different parameters would therefore be to print samples resulting from changes in angle and layer height which span this approximately 5 μm space. Whatever the precise nature of the cue being used, for changes in the print speed parameter, surface roughness is reliably discriminated with the topographical changes identified at the limit of the optical scanner (1 μm). For print angle and layer height the topographical changes, quantified via Ra or Rq, are much larger across samples, as a function of change in print parameter. Exploring these samples with the fingertip results in higher discrimination thresholds which could potentially be the result of non-Ra/Rq changes to surface topography, primarily the steepness of the slope of changes in the height of the surface. It is important to note that Ra values (as well as Rq) are singleparameter models of surface roughness, not physical roughness values themselves. Surface profiles that are actually very different can generate the same Ra. As such, two surfaces with identical Ra values can have markedly different topographies, given that Ra is an average model of the surface profile. This could account for the differences in perceptual thresholds measured during the experiment. It would suggest that neither Ra nor Rq are the best descriptors of roughness as assessed via the haptic processing of materials. A similar mismatch
potential contribution of each factor, the large difference in discrimination thresholds observed across parameters is unlikely to result from the minimal number of comparison stimuli used given that in all conditions the comparison stimulus set was small, however large differences in thresholds were observed for speed and layer height. The threshold differences observed were not the result of poor fit approximations to the proportion rougher data (see supplementary data). Furthermore, the discrimination thresholds are also within a similar range for all observers, suggesting that the perceptual outcome is not the result of a few observers being unable to identify the available cues to surface roughness. The optical scanner provided Ra estimates of surface topography for a resolution of 1 μm. While this resolution is high enough to obtain a reasonable estimate of surface roughness, it is an order of magnitude lower than that of the somatosensory system for active exploration. The passive tactile interaction method used for the perceptual estimates in the current study was designed to bring both systems (the optical scanner and perceptual) into the same ball park of discrimination of surface properties. However, it is still possible that the information available during interaction had a higher resolution for the perceptual system than that obtainable during the optical scanning. One way to address this issue in future research would be to rescan with a higher resolution optical scanner. 4.2. Searching for an explanation at the stimulus level Changes in print speed, angle and layer height produce samples that span relatively similar Ra ranges (11–36 μm), yet that have significantly different discrimination thresholds. The difference between these conditions in terms of the physical roughness of the samples is the size and linearity of the step changes in surface roughness resulting from a step change in print parameter. Although there is no significant correlation 303
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between physical descriptors and perceptual estimates of material properties have been identified for woods, polymers and metals [21].
parameters) which results in an approximate 1.5 μm change in surface roughness is reliably discriminated by observers.
4.3. Implications for haptic research
5. Conclusion
Our results suggest that the discrimination thresholds for the novel stimuli are in line with the roughness discrimination thresholds in the literature for speed, where the value is within a micron of topographical surface changes. For the other two parameters tested the values were somewhat higher than those observed in the literature [8,9,22]. The observed discrimination threshold differences which were derived from comparable physical surface roughness estimates, pose the question of what other factors might be contributing to roughness discrimination of these FDM parts. Recently 3-D printed parts have begun to be used to probe somatosensory processing of surface properties [9,10]. Yet our attempt to link discrimination thresholds to changes in print parameters via surface roughness has exposed the fact that fine texture surfaces of relatively equivalent roughness in terms of Ra and Rq values are not necessarily perceived as equal. The ability to produce novel material samples and stimuli in a relatively fast and controlled fashion may seem like a lottery win with respect to being able to create all manner of stimuli. However, our results offer a cautionary note suggesting that it is important to consider finger pad conditions as well as the physical characterization of the stimuli when understanding discriminatory touch. For example, haptic cues to surface roughness, such as friction, and indirectly epidermal moisture [23,24] may be contributing to the differences we observe, as these factors have been shown to increase perception of surface roughness. From the perspective of the perceptual discrimination thresholds for 3D print samples, the research conducted was exploratory in that the parameters and test conditions were primarily set by the industry involved (Ultimaker BV.). In future endeavors, in order to be able to make comparisons across print parameters independent of other factors it would be ideal to have the standard stimulus in all print parameter conditions having the same Ra/Rq value. This would allow us to confirm our prediction that the difference in thresholds that we see are not due to Ra/Rq value differences but arise from differences in surface profiles not captured by these models. Several studies assume that differences in physical surface roughness should translate into differences in perceived roughness, and in fact consider this to be the sole determinant for physical factor contributions [25,26,4,8,10]. However, as far as the authors are aware, this has never been shown, and in fact there are many studies that would suggest the contrary (although this statement is more an inference from the literature than a topic tested in isolation). As such our results are not surprising, though they are a first indication that we need to act with caution when assuming that measuring the physical surface characteristics will result in reproducible tangible experience of the printed product, given that perceived roughness will depend on the precise microgeometry of a surface, not just its physical surface roughness as modelled by Ra and Rq.
This study sought to identify the discrimination thresholds corresponding to changes in print parameter values used in the 3D printing process, FDM variant. It established that even small changes in printing speed will produce detectable differences in surface roughness, as modelled by Ra and Rq values, even for small speed differences, while changes in print angle and layer height leave a lot more room for discretion. This stands in clear contrast to surface roughness as objectively measured using (in this study) optical scanning, for which a difference of 5 μm is the same, regardless of the source of change. Importantly, this study established that human test subjects can reliably sense differences between various samples even if their Ra and Rq values are comparable. While the psychophysical procedure provides an estimate of roughness discrimination, the lack of correspondence to the physical roughness estimates per se suggests that an alternative technique might be better suited for evaluating the ability to discriminate between changes in surface roughness resulting from print parameter variations. One such method might be the more subjective rating of samples from roughest to smoothest. Data availability The raw data required to reproduce these findings are available to download from the TU Delft data repository 4TU. Centre for research data. The DOI is: doi:10.4121/uuid:ae990e57-babb-4698-b65ef161e7530e36 Acknowledgments The authors would like to thank David Gergely for support with the 3D printing and Oscar van de Ven for guidance during the optical scanning procedure. This work was initiated and funded by Ultimaker B.V. as part of their on-going collaboration with Delft University of Technology. J.H.O was funded by the Delft Technology Fellowship. The authors claim no other competing interests. References [1] W.M. Bergmann Tiest, A.M.L. Kappers, Analysis of haptic perception of materials by multidimensional scaling and physical measurements of roughness and compressibility, Acta Psychol. 121 (1) (2006) 1–20. [2] M. Hollins, S. Bensmaïa, K. Karlof, F. Young, Individual differences in perceptual space for tactile textures: evidence from multidimensional scaling, Percept. Psychophys. 62 (8) (2000) 1534–1544. [3] X. Chen, C.J. Barnes, T.H.C. Childs, B. Henson, F. Shao, Materials’ tactile testing and characterisation for consumer products’ affective packaging design, Mater. Des. 30 (December (10)) (2009) 4299–4310. [4] S.J. Lederman, Tactile roughness of grooved surfaces: the touching process and effects of macro- and microsurface structure, Percept. Psychophys. 16 (March (2)) (1974) 385–395. [5] S.J. Lederman, R.L. Klatzky, Relative availability of surface and object properties during early haptic processing, J. Exp. Psychol. Hum. Percept. Perform. 23 (December (6)) (1997) 1680. [6] M. Hollins, S.R. Risner, Evidence for the duplex theory of tactile texture perception, Percept. Psychophys. 62 (4) (2000) 695–705. [7] M. Holliins, R. Faldowski, S. Rao, F. Young, Perceptual dimensions of tactile surface texture: a multidimensional scaling analysis, Percept. Psychophys. 54 (6) (1993) 697–705. [8] X. Libouton, O. Barbier, L. Plaghki, J.-L. Thonnard, Tactile roughness discrimination threshold is unrelated to tactile spatial acuity, Behav. Brain Res. 208 (2) (2010) 473–478. [9] K. Drewing, Low-amplitude textures explored with the bare finger: roughness judgments follow an inverted U-Shaped function of texture period modified by texture type, Haptics: Perception, Devices, Control, and Applications, (2016), pp. 206–217. [10] C. Tymms, D. Zorin, E.P. Gardner, Tactile perception of the roughness of 3D-printed textures, J. Neurophysiol. (2017) jn.00564.2017, Nov.
4.4. Implications for materials and design For changes in individual print parameters, our results offer tolerance margins with respect to reliably producing a specific desired surface roughness for the perceiver. As regards tolerance to changes in print angle and layer height, our results suggest that manufacturers, whether private users or companies, have considerable room for adjusting printing parameters within the current range, without affecting the (tactile, not visible) perceived surface roughness. In short: observers are touch-insensitive to changes in surface roughness under 5–7 μm. This translates to print parameters. For print speed on the other hand, a difference of 15 mm/sec (current step change in Ultimaker print 304
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[19] Y. Li, B.S. Linke, H. Voet, B. Falk, R. Schmitt, M. Lam, Cost, sustainability and surface roughness quality – a comprehensive analysis of products made with personal 3D printers, Cirp J. Manuf. Sci. Technol. 16 (2017) 1–11 Jan. [20] O.A. Mohamed, S.H. Masood, J.L. Bhowmik, Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS, Materials 9 (November (11)) (2016) 895. [21] S. Wongsriruksa, P. Howes, M. Conreen, M. Miodownik, The use of physical property data to predict the touch perception of materials, Mater. Des. 42 (2012) 238–244 Dec. [22] R.L. Klatzky, S.J. Lederman, C. Reed, There’s more to touch than meets the eye: the salience of object attributes for haptics with and without vision, J. Exp. Psychol. Gen. 116 (4) (1987) 356–369. [23] L.-C. Gerhardt, V. Strässle, A. Lenz, N.D. Spencer, S. Derler, Influence of epidermal hydration on the friction of human skin against textiles, J. R. Soc. Interface 5 (November (28)) (2008) 1317–1328. [24] A.R. Gwosdow, J.C. Stevens, L.G. Berglund, J.A.J. Stolwijk, Skin friction and fabric sensations in neutral and warm environments, Text. Res. J. 56 (September (9)) (1986) 574–580. [25] W.M. Bergmann Tiest, A.M.L. Kappers, Haptic and visual perception of roughness, Acta Psychol. (Amst) 124 (February (2)) (2007) 177–189. [26] C.J. Cascio, K. Sathian, Temporal cues contribute to tactile perception of roughness, J. Neurosci. 21 (July (14)) (2001) 5289–5296.
[11] E. Tempelman, H. Shercliff, B.N. van Eyben, Manufacturing and Design: Understanding the Principles of How Things Are Made, Elsevier, 2014. [12] O. Luzanin, D. Movrin, M. Plancak, Experimental investigationn of extrusion speed and temperature effects on arithmatic mean surface roughness in FDM-built specimens, J. Technol. Plast. 38 (2013) Apr. [13] J. Žarko, G. Vladić, M. Pál, S. Dedijer, Influence of printing speed on production of embossing tools using FDM 3d printing technology, J. Graph. Eng. Des. 8 (June (1)) (2017) 19–27. [14] D. Ahn, J.-H. Kweon, S. Kwon, J. Song, S. Lee, Representation of surface roughness in fused deposition modeling, J. Mater. Process. Technol. 209 (15) (2009) 5593–5600. [15] R.I. Campbell, M. Martorelli, H.S. Lee, Surface roughness visualisation for rapid prototyping models, Comput. Des. 34 (10) (2002) 717–725. [16] B. Vasudevarao, D.P. Natarajan, M. Henderson, A. Razdan, Sensitivity of RP surface finish to process parameter variation, Solid Freeform Fabrication Proceedings, (2000), pp. 251–258. [17] C.J.L. Pérez, Analysis of the surface roughness and dimensional accuracy capability of fused deposition modelling processes, Int. J. Prod. Res. 40 (12) (2002) 2865–2881. [18] M. Grasso, M. Martorelli, A. Lanzotti, G. Staiano, The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer, Rapid Prototyp. J. 21 (August (5)) (2015) 604–617.
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Materials Today Communications journal homepage: www.elsevier.com/locate/mtcomm
Surface roughness of 3D printed materials: Comparing physical measurements and human perception
T
⁎
Jess Hartcher-O’Briena, , Jeremy Eversb,c, Erik Tempelmanb a
Perceptual Intelligence Labs, Faculty of Industrial Design Engineering, Delft University of Technology, the Netherlands Advanced Manufacturing Group, Faculty of Industrial Design Engineering, Delft University of Technology, the Netherlands c Materials Department, Ultimaker B.V., Watermolenweg 2, 4191 PN, Geldermalsen, the Netherlands b
A R T I C LE I N FO
A B S T R A C T
Keywords: FDM Surface roughness Discrimination thresholds Haptics Designed materials
This study concerns the perceived roughness of 3D printed material samples (print process: fused deposition modelling, or FDM), generated across changes in print speed, build angle, and layer height. Physical sample surface roughness parameters Ra and Rq were first obtained via optical scanning. Next, using a custom-designed apparatus, surface roughness perception was assessed via a psychophysical procedure that identified the just noticeable difference in roughness through the sense of touch alone. By comparing both data sets, this study concludes that for FDM-printed materials, objective surface roughness parameters (Ra, Rq) cannot adequately predict users’ haptic experience. This finding is of importance for all 3D printing applications where equally perceptible roughness is desired. As a whole, the study highlights the role of 3D printing as a new tool for the science of haptics and as a means for generating new material qualities by design.
1. Introduction To successfully manipulate an object, an estimate of the object's surface texture is useful: consider how lifting an object of a given weight and size requires less grip force if the object's surface is rough rather than smooth. Surface texture also informs perception. In fact, for tactile, a.k.a. haptic discrimination, surface roughness has been suggested to be the critical parameter, with other parameters, i.e. compliance, viscosity, or thermal properties, being of secondary importance at best [1–5]. Surface roughness perceptions have primarily been quantified for materials for which surface properties can be varied systematically [6–8,4]. For man-made materials, the acuity with which differences in roughness can be perceived has been found to be within a few microns [8,9]. Roughness perception of natural materials has also been explored [1], suggesting that the detection threshold for a change in surface roughness is in the range of 1 μm for active interactions with these surfaces, increasing slightly for passive exploration. Improvements in 3D printing technology allow fabrication of entirely novel stimuli, including those with designed surface textures. This unlocks a new range of “designed material surfaces” with which to probe the human somatosensory system [9,10]. The present study makes use of such novel surfaces, and examines the relationship between physical roughness and perceived surface roughness, when surface production is determined by specific 3D print parameter changes. ⁎
3D printing, more formally known as “additive manufacturing” is characterized by its incremental addition of material in a particular build direction, usually the vertical, or z-direction, under full digital control, in order to create three dimensional objects [11]. Especially for low-end use, the variety known as fused deposition modelling (FDM; also known as “material extrusion”) is currently common. In FDM, lines of molten thermoplastic are deposited next to one another in layer upon layer, with line width, deposition or print temperature and speed, and layer height being the key process parameters. Line width and layer height have an obvious direct influence on roughness: wider lines and thicker layers, while reducing printing time, will result in a coarser print. Print temperature and speed also effect roughness, as these parameters jointly decide how much heat is put in per unit of time, and hence to what extent the molten material can settle. Next, print orientation i.e. surface angle can affect how the final part will look and feel. Tempelman et al. [11], defined five quality attributes for manufacturing processes, including 3D printing: geometric tolerance, surface roughness, localized defects, material properties; and reproducibility. Surface roughness then refers to the topographical structure of a surface part, which can range from smooth to coarse, depending on build material and printer settings. The available literature confirms that print speed, specifically extrusion speed, indeed plays a significant role in the printed surface's physical roughness [12], as well as its overall quality [13]. Print
Corresponding author at: Building 32, 15 Landbergstraat, 2628CE, Delft, the Netherlands. E-mail address: [email protected] (J. Hartcher-O’Brien).
https://doi.org/10.1016/j.mtcomm.2019.01.008 Received 11 September 2018; Received in revised form 6 January 2019; Accepted 7 January 2019 Available online 21 February 2019 2352-4928/ © 2019 Elsevier Ltd. All rights reserved.
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[20].
orientation also affects surface roughness [14], creating additional irregularities on the 3D printed surface in case of steep overhangs. Results for the influence of layer height [15–17] are rather mixed, with some studies finding layer height to have a large impact on surface roughness [16,17], while another study suggests its influence to be minimal [15]. Changes in layer height has also been found to provide highly variable results, primarily with respect to material compliance, rather than surface properties [18]. Interestingly, studies so far have focussed on objectively measured roughness attributes resulting from changes in print parameters, but not on the perceptual evaluation of the surface resulting from systematic changes in print parameters [19]. To address this issue, we set out to measure roughness discrimination thresholds as the perceptual attribute of 3D print samples manufactured across a range of printing conditions. We manipulated printer settings individually for print speed, build angle, and layer height. Next, we objectively identified the surface roughness for step changes in these print parameters, using optical scanning. Then, a dedicated psychophysical apparatus and test set-up was used to ensure a purely tactile experience in test subjects (N = 9), unbiased by sound and vision. Comparison of the two data sets allowed us to see to what extent perceptual and objective measurements correlate.
2.2.1. Sample preparation All samples were printed on the centre of the Ultimaker 3 build plate. The models were placed at this position, as build plate orientation has been shown to exert an influence on the surface roughness. The technical limitations of the Ultimaker 3 were taken into account when determining the range of print parameters and their step size changes used in the current study. All parameters for printing were controlled and adjusted in Ultimaker’s slicing software Cura version 2.4. The layer height was measured in millimeters(mm), temperature in Celsius(°C), speed in millimeter per second(mm/s), surface angle in degree(°) and line width in millimeter(mm). The printer settings used were based on the standards used by Ultimaker B.V. (see Table 1) for their Ultimaker 3 desktop printer. The standard values for non-manipulated parameters were determined by Ultimaker Cura v. 2.4 standards, which equate to a print angle, layer height and speed of 40˚, 0.2 mm and 60 mm/sec respectively. Stimuli were thus printed standing upright at said angle; to avoid vibrations of stimuli during printing, they were printed with narrow sidewalls for support. These were broken off after printing, without damage to the actual sample surface area under consideration. The parameter bounds were set such that the print process would result in a reliable surface finish for the FDM part. Exceeding this range is possible however it drastically decreases the reliability of the 3D printer and parts. For print layer height the lower limit was set to 006 mm and the upper limit to 0,2 mm. For print temperature the range was between 250 °C and 280 °C. This is optimal for the Polycarbonate (PC) material used. The limits for print speed were set to 20 mm/s for the lower and 100 mm/s for the upper limit. In the Cura profiles for the material PC the cooling is not active, but to investigate the effect the parameter was included and set to 50%. The angle at which the surface is printed can significantly influence the surface roughness as it directly affects the buildup of layers. The lower limit of the angle orientation was set at 15°(from the horizontal plane). From this point the layers of the overhanging surface start to print far enough from the adjacent layers to begin to detach and form loose lines. The upper limit was set to 75°. For this parameter we included an additional angle: 90°. This was done to inform Ultimaker about the effects of an additional upper limit for this print parameter. For print speed, the lower limit was set to 20 mm/s with an upper limit of 100 mm/sec. Each parameter range is divided into 5 samples to see the change of roughness per step size. These settings are summarized in Table 1. To determine the physical roughness for the current samples, optical imaging measurements were performed for each sample thrice using a Micro-epsilon 2910-10BL laser line scanner. This device uses optical triangulation and has a resolution of approximately 1 μm – too low for high-precision parts, but more than adequate for FDM prints. The scanner was encapsulated in a custom-built test box to ensure reproducible test results, and fully automated (incl. data processing) for ease of use. The optical scanner imaged the 40 mm sample in 10 mm scan windows. For each surface, estimates were derived from three scan repetitions which were used to estimate the physical surface roughness (Ra and Rq), with Ra calculated as:
2. Method 2.1. Observers Nine volunteers took part in each part in each condition of this study. The mean age of the observers was 26 years and all had normal somatosensory processing according to self-report. Almost half of the volunteers for the study were employees of Ultimaker B.V. and therefore had ample experience with 3D print samples. Participants gave their informed consent prior to taking part in the study. 2.2. Apparatus and stimuli The stimuli were flat, rectangular 3D printed samples measuring 40 by 20 by 3 mm produced under different sets of print parameter conditions (see Table 1). The shape and size of these stimuli was designed so that both an optical scanner and an index fingertip could easily scan the surface to extract an estimate of surface roughness. The thermoplastic material used for the stimuli was PC (polycarbonate). Compared to the more common PLA and ABS materials, PC material has the advantage of being highly robust with respect to print overhang angle Table 1 FDM Surface roughness μm) values and print parameters used to produce them. Roughness estimates Ra (and Rq) were quantified by optical scanner measurements (with Ra and Rq calculated according to Eqs. (1) and (2) respectively), for the 3-D printed surfaces under different print speed (mm/sec), surface angle (°) and layer height (mm) conditions. Upward
Parameter
Sample
Ra (μm)
Rq (μm)
Speed (mm/sec)
20 40 60 80 100 15 30 45 60 75 90 0.06 0.10 0.13 0.17 0.20
B5 B4 B3/B3 (ref) B2 B1 C6 C5 C4/C4 (ref) C3 C2 C1 D5 D4 D3/D3 (ref) D2 D1
29 27 26 25 24 30 29 24 19 14 11 15 17 27 28 34
35 33 32 31 30 35 34 30 24 16 14 18 21 33 34 41
Angel (°)
Layer height (mm)
l
Ra =
1 ∑ | Z(x ) | l x=1
(1)
where l is the evaluation length of the surface and Z(x) is the profile height function. Rq, an estimate of the root mean square, is calculated as: l
Rq =
301
1 ∑Z 2 l x=1 x
(2)
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The primary aim of the study was to understand how sensitive human subjects are to changes in surface roughness resulting from altering 3D print parameters. Surprisingly, the discrimination thresholds produced by altering print parameters were found to be significantly different from one another, F(2, 16) = 8.68, p < 0.01. The thresholds for print speed were found to be significantly lower than the same thresholds produced by changing build angle, t(8) = -7.73, p > 0.05 and print layer height, t(8) = -11.63, p > 0.05. The difference in haptic discrimination thresholds between build angle and layer height however were not significant, t(8) = 1.26, p > 0.05. Given the overlap in the surface roughness values (Ra) as objectively assessed via optical scanning, it is clear that these discrimination thresholds cannot depend on these physical roughness value estimates per se. The smallest change in physical estimates of surface roughness relative to the reference stimulus was 1 μm for speed, 5 μm for build angle, and 1 μm for layer height (Fig. 3). Given the non-linear changes in the comparison stimuli these values represent the closest correspondence to the perceptual discrimination thresholds of surface roughness, as estimated via the psychophysical procedure reported here. In order to try and account for the differences observed, these values were cross-referenced with the perceptual thresholds. However, for print speed the difference thresholds were not found to be significantly correlated (r = -0.57, p > 0.05), nor were they for build angle (r = -0.03, p > 0.05) or layer height (r = -0.50, p > 0.05). So, it seems that not only do scanners and humans assess the same stimuli differently, but humans apparently sense differences caused by print speed, build angle or layer height with different acuity – an unexpected result.
Ra and Rq are therefore objective models of the physical surface roughness of the FDM samples produced for changes in print speed, surface angle and layer height. The stimulus roughness values (μm) can be seen in Table 1 for printer speed, surface angle and layer height respectively. These samples form the basis for the psychophysical procedure used to estimate haptic roughness discrimination thresholds. 2.3. Procedure A two-alternative forced choice procedure was used to identify the discrimination threshold, quantified as the change in roughness needed to correctly identify the rougher sample 75% of the time. Observers were blindfolded and sat in front of a table positioned at elbow height. The observer's index finger rested in a custom-built finger brace to ensure that exploration height remained constant throughout the task. In addition to the blindfold, observers also listened to noise via headphones, to exclude the possibility of acoustic feedback cues affecting the results. On each trial observers used bare-finger contact to compared two stimuli presented via the custom-built apparatus which moved the samples at a rate of 40 mm/sec across the volar surface of the observer's index fingertip. The stimulus always returned to the central start position at the end of the scan interval. The fingertip scanned the 40 mm dimension of the surface. Stimuli were cleaned with acetone on removal from the setup to minimize microstructure changes of the surface due to bare finger interaction. The reference stimulus, (see Table 1) was presented on every trial, and its position varied randomly on a trial-by-trial basis (either the first or second sample explored). Observers were not told that the reference stimulus was used in all trials and they did not receive any feedback with respect to their responses. The observer's task was simple: to indicate which of two consecutive surfaces felt rougher, with 'the first' or 'the second' sample experienced (see Fig. 1). The physical roughness (μm) of the comparison stimulus varied according to a one-up, one-down, interleaved staircase procedure, which terminated after fifteen reversals.
4. Discussion 4.1. Significance of results The key finding to emerge from this study is that the perceptual discrimination thresholds of surface roughness cannot be accounted for, and hence cannot be predicted, by either Ra or Rq values of the physical roughness of the FDM samples. This can be seen in the comparison of thresholds across print parameters where Ra and Rq values are equitable while the perceptual thresholds are definitely not. Incidentally, our findings show that Rq and Ra are well-correlated; in fact, on average, we consistently found Ra = 0.8·Rq. This suggests consistency and high reproducibility of the printed surfaces with respect to surface topography, as the presence or absence of a few large defects would affect Rq much more than it would affect Ra. Methodological factors that could potentially cause such a difference in discrimination thresholds across conditions are: the number of comparison samples used to test the discrimination thresholds, which in turn influences the how well the function fit represents the proportion rougher response data; the variability across individual observer estimates and the resolution of the optical scanner. If we consider the
3. Results The data for each condition and each observer were fit with a cumulative Gaussian function to extract the discrimination threshold (Fig. 2 a, b, c) for print speed, angle and layer height respectively. Given we did not change conditions within a print parameter we did not expect any bias and thus the point of subjective equality, or PSE, was set to the reference stimulus value for each print parameter. Given the staircase procedure to acquire the data, the psychometric function fits were weighted for the number of repetitions of each stimulus during the measurement procedure. The comparison values for angle and layer height are a function of non-equal stimulus step changes in physical roughness which correspond to linear step changes in the print parameter inputs. For speed, the roughness estimates are close to linear with respect to the input print parameters.
Fig. 1. Apparatus and stimuli. a) The custom-built apparatus mechanically moved the two stimuli consecutively across the observer's finger pad at 40 mm/sec for each trial. The observer indicated which surface was rougher, that experienced in the first or second interval. b) Example stimulus range decreasing in surface roughness μm) with the reference stimulus present on every trial. 302
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Fig. 2. Perceived surface roughness data from an example subject. Proportion rougher data and psychometric fits as a function of changes in (a) speed (b) build angle and (c) layer height. Roughness estimates were acquired using a staircase procedure.
Fig. 3. Physical and relative roughness discrimination threshold estimates. Average physical roughness estimates (diamonds) and average (across observers) discrimination threshold (bar) as a function of changes in print a) speed, b) build angle and c) layer height. The corresponding change in print parameter equivalent to the perceptual threshold (error bar), for a discriminable change in parameter is illustrated (dotted line).
between the change in surface roughness, obtained from optical scanning, and the perceptual discrimination thresholds for changes in print speed, angle or layer height, the small, primarily equal changes in surface roughness produced by print speed give rise to a low threshold suggesting observers are sensitive to changes produced under speed change conditions. The changes in surface roughness spanned by changes in print speed is in the range of 5 μm. For print angle and layer height changes in the surface encompasses a difference of approximately 20 μm. One potential way to interrogate the differences in discrimination thresholds for the different parameters would therefore be to print samples resulting from changes in angle and layer height which span this approximately 5 μm space. Whatever the precise nature of the cue being used, for changes in the print speed parameter, surface roughness is reliably discriminated with the topographical changes identified at the limit of the optical scanner (1 μm). For print angle and layer height the topographical changes, quantified via Ra or Rq, are much larger across samples, as a function of change in print parameter. Exploring these samples with the fingertip results in higher discrimination thresholds which could potentially be the result of non-Ra/Rq changes to surface topography, primarily the steepness of the slope of changes in the height of the surface. It is important to note that Ra values (as well as Rq) are singleparameter models of surface roughness, not physical roughness values themselves. Surface profiles that are actually very different can generate the same Ra. As such, two surfaces with identical Ra values can have markedly different topographies, given that Ra is an average model of the surface profile. This could account for the differences in perceptual thresholds measured during the experiment. It would suggest that neither Ra nor Rq are the best descriptors of roughness as assessed via the haptic processing of materials. A similar mismatch
potential contribution of each factor, the large difference in discrimination thresholds observed across parameters is unlikely to result from the minimal number of comparison stimuli used given that in all conditions the comparison stimulus set was small, however large differences in thresholds were observed for speed and layer height. The threshold differences observed were not the result of poor fit approximations to the proportion rougher data (see supplementary data). Furthermore, the discrimination thresholds are also within a similar range for all observers, suggesting that the perceptual outcome is not the result of a few observers being unable to identify the available cues to surface roughness. The optical scanner provided Ra estimates of surface topography for a resolution of 1 μm. While this resolution is high enough to obtain a reasonable estimate of surface roughness, it is an order of magnitude lower than that of the somatosensory system for active exploration. The passive tactile interaction method used for the perceptual estimates in the current study was designed to bring both systems (the optical scanner and perceptual) into the same ball park of discrimination of surface properties. However, it is still possible that the information available during interaction had a higher resolution for the perceptual system than that obtainable during the optical scanning. One way to address this issue in future research would be to rescan with a higher resolution optical scanner. 4.2. Searching for an explanation at the stimulus level Changes in print speed, angle and layer height produce samples that span relatively similar Ra ranges (11–36 μm), yet that have significantly different discrimination thresholds. The difference between these conditions in terms of the physical roughness of the samples is the size and linearity of the step changes in surface roughness resulting from a step change in print parameter. Although there is no significant correlation 303
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between physical descriptors and perceptual estimates of material properties have been identified for woods, polymers and metals [21].
parameters) which results in an approximate 1.5 μm change in surface roughness is reliably discriminated by observers.
4.3. Implications for haptic research
5. Conclusion
Our results suggest that the discrimination thresholds for the novel stimuli are in line with the roughness discrimination thresholds in the literature for speed, where the value is within a micron of topographical surface changes. For the other two parameters tested the values were somewhat higher than those observed in the literature [8,9,22]. The observed discrimination threshold differences which were derived from comparable physical surface roughness estimates, pose the question of what other factors might be contributing to roughness discrimination of these FDM parts. Recently 3-D printed parts have begun to be used to probe somatosensory processing of surface properties [9,10]. Yet our attempt to link discrimination thresholds to changes in print parameters via surface roughness has exposed the fact that fine texture surfaces of relatively equivalent roughness in terms of Ra and Rq values are not necessarily perceived as equal. The ability to produce novel material samples and stimuli in a relatively fast and controlled fashion may seem like a lottery win with respect to being able to create all manner of stimuli. However, our results offer a cautionary note suggesting that it is important to consider finger pad conditions as well as the physical characterization of the stimuli when understanding discriminatory touch. For example, haptic cues to surface roughness, such as friction, and indirectly epidermal moisture [23,24] may be contributing to the differences we observe, as these factors have been shown to increase perception of surface roughness. From the perspective of the perceptual discrimination thresholds for 3D print samples, the research conducted was exploratory in that the parameters and test conditions were primarily set by the industry involved (Ultimaker BV.). In future endeavors, in order to be able to make comparisons across print parameters independent of other factors it would be ideal to have the standard stimulus in all print parameter conditions having the same Ra/Rq value. This would allow us to confirm our prediction that the difference in thresholds that we see are not due to Ra/Rq value differences but arise from differences in surface profiles not captured by these models. Several studies assume that differences in physical surface roughness should translate into differences in perceived roughness, and in fact consider this to be the sole determinant for physical factor contributions [25,26,4,8,10]. However, as far as the authors are aware, this has never been shown, and in fact there are many studies that would suggest the contrary (although this statement is more an inference from the literature than a topic tested in isolation). As such our results are not surprising, though they are a first indication that we need to act with caution when assuming that measuring the physical surface characteristics will result in reproducible tangible experience of the printed product, given that perceived roughness will depend on the precise microgeometry of a surface, not just its physical surface roughness as modelled by Ra and Rq.
This study sought to identify the discrimination thresholds corresponding to changes in print parameter values used in the 3D printing process, FDM variant. It established that even small changes in printing speed will produce detectable differences in surface roughness, as modelled by Ra and Rq values, even for small speed differences, while changes in print angle and layer height leave a lot more room for discretion. This stands in clear contrast to surface roughness as objectively measured using (in this study) optical scanning, for which a difference of 5 μm is the same, regardless of the source of change. Importantly, this study established that human test subjects can reliably sense differences between various samples even if their Ra and Rq values are comparable. While the psychophysical procedure provides an estimate of roughness discrimination, the lack of correspondence to the physical roughness estimates per se suggests that an alternative technique might be better suited for evaluating the ability to discriminate between changes in surface roughness resulting from print parameter variations. One such method might be the more subjective rating of samples from roughest to smoothest. Data availability The raw data required to reproduce these findings are available to download from the TU Delft data repository 4TU. Centre for research data. The DOI is: doi:10.4121/uuid:ae990e57-babb-4698-b65ef161e7530e36 Acknowledgments The authors would like to thank David Gergely for support with the 3D printing and Oscar van de Ven for guidance during the optical scanning procedure. This work was initiated and funded by Ultimaker B.V. as part of their on-going collaboration with Delft University of Technology. J.H.O was funded by the Delft Technology Fellowship. The authors claim no other competing interests. References [1] W.M. Bergmann Tiest, A.M.L. Kappers, Analysis of haptic perception of materials by multidimensional scaling and physical measurements of roughness and compressibility, Acta Psychol. 121 (1) (2006) 1–20. [2] M. Hollins, S. Bensmaïa, K. Karlof, F. Young, Individual differences in perceptual space for tactile textures: evidence from multidimensional scaling, Percept. Psychophys. 62 (8) (2000) 1534–1544. [3] X. Chen, C.J. Barnes, T.H.C. Childs, B. Henson, F. Shao, Materials’ tactile testing and characterisation for consumer products’ affective packaging design, Mater. Des. 30 (December (10)) (2009) 4299–4310. [4] S.J. Lederman, Tactile roughness of grooved surfaces: the touching process and effects of macro- and microsurface structure, Percept. Psychophys. 16 (March (2)) (1974) 385–395. [5] S.J. Lederman, R.L. Klatzky, Relative availability of surface and object properties during early haptic processing, J. Exp. Psychol. Hum. Percept. Perform. 23 (December (6)) (1997) 1680. [6] M. Hollins, S.R. Risner, Evidence for the duplex theory of tactile texture perception, Percept. Psychophys. 62 (4) (2000) 695–705. [7] M. Holliins, R. Faldowski, S. Rao, F. Young, Perceptual dimensions of tactile surface texture: a multidimensional scaling analysis, Percept. Psychophys. 54 (6) (1993) 697–705. [8] X. Libouton, O. Barbier, L. Plaghki, J.-L. Thonnard, Tactile roughness discrimination threshold is unrelated to tactile spatial acuity, Behav. Brain Res. 208 (2) (2010) 473–478. [9] K. Drewing, Low-amplitude textures explored with the bare finger: roughness judgments follow an inverted U-Shaped function of texture period modified by texture type, Haptics: Perception, Devices, Control, and Applications, (2016), pp. 206–217. [10] C. Tymms, D. Zorin, E.P. Gardner, Tactile perception of the roughness of 3D-printed textures, J. Neurophysiol. (2017) jn.00564.2017, Nov.
4.4. Implications for materials and design For changes in individual print parameters, our results offer tolerance margins with respect to reliably producing a specific desired surface roughness for the perceiver. As regards tolerance to changes in print angle and layer height, our results suggest that manufacturers, whether private users or companies, have considerable room for adjusting printing parameters within the current range, without affecting the (tactile, not visible) perceived surface roughness. In short: observers are touch-insensitive to changes in surface roughness under 5–7 μm. This translates to print parameters. For print speed on the other hand, a difference of 15 mm/sec (current step change in Ultimaker print 304
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[19] Y. Li, B.S. Linke, H. Voet, B. Falk, R. Schmitt, M. Lam, Cost, sustainability and surface roughness quality – a comprehensive analysis of products made with personal 3D printers, Cirp J. Manuf. Sci. Technol. 16 (2017) 1–11 Jan. [20] O.A. Mohamed, S.H. Masood, J.L. Bhowmik, Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS, Materials 9 (November (11)) (2016) 895. [21] S. Wongsriruksa, P. Howes, M. Conreen, M. Miodownik, The use of physical property data to predict the touch perception of materials, Mater. Des. 42 (2012) 238–244 Dec. [22] R.L. Klatzky, S.J. Lederman, C. Reed, There’s more to touch than meets the eye: the salience of object attributes for haptics with and without vision, J. Exp. Psychol. Gen. 116 (4) (1987) 356–369. [23] L.-C. Gerhardt, V. Strässle, A. Lenz, N.D. Spencer, S. Derler, Influence of epidermal hydration on the friction of human skin against textiles, J. R. Soc. Interface 5 (November (28)) (2008) 1317–1328. [24] A.R. Gwosdow, J.C. Stevens, L.G. Berglund, J.A.J. Stolwijk, Skin friction and fabric sensations in neutral and warm environments, Text. Res. J. 56 (September (9)) (1986) 574–580. [25] W.M. Bergmann Tiest, A.M.L. Kappers, Haptic and visual perception of roughness, Acta Psychol. (Amst) 124 (February (2)) (2007) 177–189. [26] C.J. Cascio, K. Sathian, Temporal cues contribute to tactile perception of roughness, J. Neurosci. 21 (July (14)) (2001) 5289–5296.
[11] E. Tempelman, H. Shercliff, B.N. van Eyben, Manufacturing and Design: Understanding the Principles of How Things Are Made, Elsevier, 2014. [12] O. Luzanin, D. Movrin, M. Plancak, Experimental investigationn of extrusion speed and temperature effects on arithmatic mean surface roughness in FDM-built specimens, J. Technol. Plast. 38 (2013) Apr. [13] J. Žarko, G. Vladić, M. Pál, S. Dedijer, Influence of printing speed on production of embossing tools using FDM 3d printing technology, J. Graph. Eng. Des. 8 (June (1)) (2017) 19–27. [14] D. Ahn, J.-H. Kweon, S. Kwon, J. Song, S. Lee, Representation of surface roughness in fused deposition modeling, J. Mater. Process. Technol. 209 (15) (2009) 5593–5600. [15] R.I. Campbell, M. Martorelli, H.S. Lee, Surface roughness visualisation for rapid prototyping models, Comput. Des. 34 (10) (2002) 717–725. [16] B. Vasudevarao, D.P. Natarajan, M. Henderson, A. Razdan, Sensitivity of RP surface finish to process parameter variation, Solid Freeform Fabrication Proceedings, (2000), pp. 251–258. [17] C.J.L. Pérez, Analysis of the surface roughness and dimensional accuracy capability of fused deposition modelling processes, Int. J. Prod. Res. 40 (12) (2002) 2865–2881. [18] M. Grasso, M. Martorelli, A. Lanzotti, G. Staiano, The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer, Rapid Prototyp. J. 21 (August (5)) (2015) 604–617.
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