Feasibility study applying a parametric model as the design … · 2018. 2. 9. · tutorial, including viewport navigation, poly-line drawing in Rhino and object setting in Grasshopper

RESEARCH Open Access

Feasibility study applying a parametricmodel as the design generator for 3D–printed orthosis for fracture immobilizationJianyou Li* and Hiroya Tanaka

Abstract

Background: Applying 3D printing technology for the fabrication of custom-made orthoses provides significantadvantages, including increased ventilation and lighter weights. Currently, the design of such orthoses is mostoften performed in the CAD environment, but creating the orthosis model is a time-consuming process thatrequires significant CAD experience. This skill gap limits clinicians from applying this technology in fracturetreatment. The purpose of this study is to develop a parametric model as the design generator for 3D–printedorthoses for an inexperienced CAD user and to evaluate its feasibility and ease of use via a training and designexercise.

Results: A set of automatic steps for orthosis modeling was developed as a parametric model using the VisualProgramming Language in the CAD environment, and its interface and workflow were simplified to reduce thetraining period. A quick training program was formulated, and 5 participants from a nursing school completedthe training within 15 mins. They verified its feasibility in an orthosis design exercise and designed 5 orthoseswithout assistance within 8 to 20 mins. The few faults and program errors that were observed in video analysisof the exercise showed improvable weaknesses caused by the scanning quality and modeling process.

Conclusions: Compared to manual modeling instruction, this study highlighted the feasibility of using a parametricmodel for the design of 3D–printed orthoses and its greater ease of use for medical personnel compared to the CADtechnique. The parametric model reduced the complex process of orthosis design to a few minutes, and a customizedinterface and training program accelerated the learning period. The results from the design exercise accurately reflectreal-world situations in which an inexperienced user utilizes a generator as well as demonstrate the utility of theparametric model approach and strategy for training and interfacing.

Keywords: 3D–printing, Orthosis, Parametric modeling, Fracture immobilization, Customization

BackgroundIntegration of 3D printing and medical image capturingtechnologies has been widely applied in medicine. CT orMRI anatomic imaging techniques can capture volumetricimages of bones and soft tissues, and these images can bematerialized by 3D printing devices as physical models toaid surgical planning or training [1–3]. Moreover, non-contact scanners based on a laser source and depth camerahave become an option to replace the use of a conventionalphysical casting to acquire the anatomic surfaces necessary

for the fabrication of orthoses or prosthetics. The 3Dscanning technique prevents patient discomfort andinduces less distortion of the target region [4, 5]. Inaddition to representing the anatomic form, the 3D print-ing technology also provides various physical properties tosupport the specific requirements of implants, orthotics,braces and prostheses via materials or structures that arebuilt by manual or computational design [6, 7].The same technology has been applied to fracture

immobilization of the upper limb, and related studies haverapidly increased in recent years [8–14]. The 3D–printedorthosis minimizes distortion during the healing processbecause of its best fit geometry. The highly ventilatedstructure provides hygienic benefits and is light weight,

* Correspondence: [email protected] School of Media and Governance, Keio University, 5322 Endo,Fujisawa-shi, Kanagawa 252-0882, Japan

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made.

Li and Tanaka 3D Printing in Medicine (2018) 4:1 DOI 10.1186/s41205-017-0024-1

http://crossmark.crossref.org/dialog/?doi=10.1186/s41205-017-0024-1&domain=pdfhttp://orcid.org/0000-0003-2766-3448mailto:[email protected]://creativecommons.org/licenses/by/4.0/

reducing the risk of cutaneous complications and poten-tially improving treatment efficacy and increasing patientsatisfaction [8]. The process of making a 3D–printedorthosis primarily consists of 3 digitalized phases [4], andstudies have mainly focused on the 3D scanning stage toincrease the precision and completeness of anatomicimage acquisition of the affected limb [15–19]. Reviewsrelated to the 3D printing stage are mainly devoted to thecomparison of material appropriateness, fabricationtechnology and manufacturing efficiency [14, 19], andseveral groups have reported a 3D modeling process ofwearable, ventilated and lightweight orthoses [8, 9, 12, 13].In the 3D modeling stage, the design task is not only togenerate a patient-specific shell according to the surfaceof the affected limb but also to control the density andthickness of the ventilated structure based on the surface.The structure and its volume impact the orthosis strengthand printing time. Additionally, the necessary wearabledesigns, such as flexible gaps, hinges or interlockingcomponents, are generated at this stage as well. It is oftena challenge for the clinician to achieve initial treatment,design, and modeling steps in a 3D virtual environment,and this challenge includes the required time for orthosismodeling and the significant learning period necessary toutilize the specific CAD tool.Currently, commercial CAD software is the main tool

for researchers and designers to interface with the scannedanatomic mesh, build the orthosis model and exportfabrication data [8]. The CAD tool provides completecommands and a 3D environment for researchers toexplore the process of orthosis design; thus, they candevelop stable command sequences as operable instruc-tions for clinicians to reference. Such exploiting processes

can be classified as Direct Modeling, an emerging tech-nical term in the CAD industry [20–24] that has appearedfrequently, in contrast to Parametric Modeling, for almosta decade. This flexible modeling process means that theuser has significant freedom to compose and modify thegeometric model directly without considering build historyand parent-child relationships between features [21, 23].Based on increasing numbers of approaches and proto-

types, Direct Modeling approaches have provided manysuccessful results. The modeling procedure for 3D–printed orthoses has become an execution of steady andcontinuous tasks, and several common steps haveemerged frequently from various approaches, such as cut-ting, thickening surface, and engraving shell [9]. The pos-sible modeling process of an orthosis can be predictedfrom orthosis features (Fig. 1). For example, for Activar-mor and Zdravprint splints (Fig. 1a) [25, 26], their spongybone-like structures can be developed from the poly-linenetwork along the limb surface, and the mesh appearancescan be smoothed by the Catmull–Clark algorithm or T-spline surface technique [27]. Other prototypes have uni-form thicknesses and obvious edges around their holes,such as the XKELET (Xkelet) or Cortex, designed by JakeEvill and Denis Karasahin’s Osteoid, (Fig. 1b) [28–30];their common processes may include partial surface thick-ening as a solid shell and engraving the lattice by cuttingor by Boolean operation. Although the orthosis modelgeneration procedure can be archived from the abovesequence, there is still concern regarding whether theclinician can learn and apply these modeling skills andperform them in clinical fracture treatment.In many studies, the CAD software has generally

received a negative evaluation based on its cumbersome

a. Smooth exoskeletons thatdeveloped from the poly line web.

b. Engraved shells with the uniformthickness.

Fig. 1 Different modeling approaches for orthosis prototypes. a Smooth exoskeletons. b Engraved shells

Li and Tanaka 3D Printing in Medicine (2018) 4:1 Page 2 of 15

interface. Because the software is designed for construct-ing multifaceted geometric forms for manufacturing orarchitecture purposes, the interface displays all icons,panels and information for constructing different em-bryos in the initial stage. Additionally, in the modelingprocess, each software has its own culture for providingrequests and feedback in the interactions with the user.The user should be familiar with the system’s communi-cation scheme and understand the related geometricprinciples and meaning of errors. Even for beginners indesign or engineering schools, the learning period forthe CAD tool usually involves weeks to months. Inaddition, although orthosis modeling is a fixed proced-ure, many variables change and impact the orthosis de-sign in each individual design execution, such as thescanning quality, physiologic differences of anatomiclimbs and fracture conditions. The clinician needs to reactto these changes during modeling by modifying theimmobilization region or lattice density, and these neces-sary reactions challenge the stability of fixed procedures. Ifthe orthosis model is not printable or fails based on ageometric error, the clinician must have enough geometricknowledge and skill to solve the situation. The revisedsolution will probably require more time and be morecomplex than the modeling procedure itself.Therefore, the developed modeling procedure is not

currently suitable for provision as operable instructionsfor clinicians. The required time for these proceduresusually ranges from tens of minutes to 3 h, dependingon the operator’s skill [8, 9]. In such long operations, thecomplex interface and system interactions may causeclinician to fail to obtain a printable design. Theseattempts at orthosis modeling belong to an exploratoryprocess that should only performed by a CAD expert forstudy purposes; for clinical treatment, this should beshifted to a teachable skill and an efficient tool formedical personnel in a medical context.Relative to the Direct Modeling’s advantages for

exploration, Parametric Modeling is suitable for fixedtasks to generate orthosis designs. In addition tomanaging dimensions, many modeling software pro-grams can now edit complex parametric models viaapplications of text or visual programing languages toorganize modeling steps, constraints and parametricrelationships [31]. For example, Rhino 3D (RobertMcNeel & Associates) works with Python or Grass-hopper 3D, and Fusion 360 (Autodesk) works with aDynamo plug-in. For reacting to variables in the orth-osis design and generating stable results automaticallyin real-time, the orthosis modeling steps should bereconstructed by parametric modeling technology andbecome a history-based model. Orthosis features arewell-generated by parameter-driven input, pre-definedalgorithms and parent-child relationships of geometry.

In this manuscript, a parametric model and its custom-ized interface were developed for clinicians to create print-able orthosis casts with minimal CAD skill required. Atraining tutorial was formulated and evaluated by inex-perienced users in an orthosis design exercise to deter-mine its feasibility and ease of use.

MethodIn this section, we describe the 5 stages of transferringthe results of a Direct Modeling approach to develop apractical parametric model (Fig. 2), as well as the volun-teers who participated in the subsequent training andevaluation. The points of each stage are listed as follows:

� Direct Modeling process of orthosis design: Acompact process developed based on the clinician,the inexperienced CAD user’s thinking and limitedgeometric knowledge.

� Parametric model: Reconstruction based on theresults of previous stages; the main parameters inevery step were collected and optimized in iterativetests.

� Interface customization: All unnecessary menu,toolbars and panels in the main CAD environmentwere removed, until only a basic interface remained.

� Quick training: Fundamental knowledge for utilizingthe parametric model was provided in a one-on-onetutorial, including viewport navigation, poly-linedrawing in Rhino and object setting in Grasshopper.Five nursing students who were familiar with ma-nipulating fracture immobilizations were invited toparticipate in the training.

� Orthosis design exercise: After the training, theparticipants performed a computer-based exercise todesign orthoses for 5 different arm models bythemselves, and their design processes on thescreens were recorded for further analyses.

Scanning processAlthough the 3D scanning process was not the mainpoint of this study, the scan quality and other limita-tions may impact the stability of the parametricmodel. Based on the literatures [9, 17], patients withacute fractures may have difficulty maintaining re-quired poses, or the clinician may be unable to movethe scanner to all the required positions around thelimb stably. To develop a complete mesh model of anaffected limb, we utilized the 3D scanner Sense(3DSystem) and a custom-made scanner mount(Fig. 3a) to collect limb samples. The scanner mountwas made by aluminum rail sticks and the scannerwas installed on its rotor arm. A support table wasplaced under the mount’s axis, and the patient’s upperarm for the affected limb was supported to align with


the axis. The scanner on the rotor arm could be ro-tated and moved along a circular path around thelimb smoothly by rotating the crank handle and wasmaintained at a stable distance from the limb. Theentire scanning process took approximately 20 s.However, the scanner always faced the axis line in the

process. A few detailed faces between the fingers andfingertips could not be reached (the yellow area inFig. 3b), and in subsequent definitions of theimmobilization area, the operator should avoid theseareas because these lost regions may lead to subse-quent failure of the modeling command.

transfer

(3)CustomizedInterface

(5) computer-basedexercise (2) Automatic generator for the treatment

Parametric Modeling methodby using Grasshopper

(1) Exploring process of the orthosis designDirect Modeling method

by using Rhino 3D

Study and compose

ClinicianInexperienced

CAD user

ResearcherCAD expert

(4)Training

formulation

Fig. 2 Development process for transferring the explored modeling process into the parametric model

Crankhandle

Balanceweight

AxisPlace the limbalign the axis

Sense3D scanner

movingcircularpath

Lost meshesbetween fingersand fingertips.

Fig. 3 3D scanner mount and scanning limitations


CAD software and parametric modeling toolConsidering the fitness and adjustability of the surface-based model, we utilized Rhino 3D Version 5 for themain modeling environment; this is recognized as a typ-ical non-parametric software. Hence, it has the flexibilityof Direct Modeling, and can address the simultaneousexistence of mesh and free-surfaces. Additionally, itallows the user to customize the interface and removeall unnecessary panels and tool bars. Moreover, its al-gorithm plug-in, Grasshopper 3D, is a widespreadVisual Programming Language among parametricmodeling tools [31–34], and it is complementary tothe flexible property of Rhino 3D. In the steps de-scribed below, we utilized Rhino 3D to simulate themodeling sequence directly and transferred it to anautomatic parametric model via the correspondingcomponents (graphic icon showing the program com-mand) in Grasshopper 3D.

Automatic arrangementWhen importing the scanned limb, the mesh model mayappear in the Rhino 3D space with random angles andpositions; therefore the clinician may need to move it to

the appropriate position and rotate it to the right anglebefore moving forward. To save the clinician from hav-ing to learn these commands, as well as reducing the op-erating time and the risk of the model becoming lost inCAD space, we developed an intelligent function forautomatic arrangement in Grasshopper (Fig. 4). Oncethe limb model is set to the mesh component and themodel data are imported to Grasshopper, the arrange-ment function can determine the mesh volume centralpoint P1, and locate the 2 mesh central points, P2 andP3, on the two ends of limb to form the P23 axis(Fig. 4a). Then, the angles between the axis line and 2planes, XY and XZ, are determined (Fig. 4b), which pro-vide automatic rotations in each plane to allow the limbto align with the X-axis. A set of cross-sections on thisarm will be determine by circles on 30 planes along theP23 axis, and the widest section curve is usually locatedon the palm (Fig. 4c). The two farthest points on thissection can be found using the cross-points from anarray of 40 lines. The angle between this line and the Y-axis could be applied to the final rotation on the YZplane, with palm facing up or down. Then, the armmodel is moved from P1 to a default position on the

1st rotation onthe XY plane

2nd rotation onthe XZ plane

The red line detected thefarthest points on the 2sides of cross-section.

3rd rotation onYZ plane.

Y axis

The widest cross-section on the YZ

plane.

P1, Volume centerof limb mesh

P2, the farthestpoint from P1

P3, the farthestpoint from P2

Set the limb model to the inputcomponent in Grasshopper

b

c d The final stateof limb model

a

Fig. 4 Working process of automatic arrangement in Grasshopper. a Import the limb model to Grasshopper. b Rotations on the XY and XZplanes. c Widest cross-section on the YZ plane. d Rotation on YZ plane


coordinates (300, 100, −50). The clinician can thendefine the immobilization area on this position using thetop view clearly.

Parametric modeling processIn the explorative process of Direct Modeling, all adoptedsteps should have their corresponding commands inGrasshopper and be transferable to the parametric model.However, a few steps are only operable in the program-ming language, e.g., the Voronoi pattern for the engravingoperation is difficult to generate manually in Rhino.Therefore, the process of parametric modeling is more

complete; we have described its detailed steps directly andexplained the related calculations in the program.First, the 3D–printed orthosis described in this

manuscript consisted of 2 pieces of engraved shellsfastened by 4 screws (Fig. 5a). The method to definethe immobilization area involved drawing a quadran-gle (Fig. 5b). According to the patient’s condition, theclinician can use the poly-line tool to draw the quad-rangle and overlay it on the affected limb in the topview in Rhino. Then, the quadrangle could be setwith the Curve Component in Grasshopper 3D andinput into the parametric model. When receiving

Immobilization area

a

b

2-parts set of Engraved shells

The parts areassembled by

4 screws

Set the immobilization area on the topview in Rhino, and then set the quadrangleto the component in Grasshopper.

Fig. 5 Features of orthosis in this manuscript and its defining method for immobilization area. a Assembly method. b The defining method forimmobilization area


input data, the program can recognize Sides A and Bof the quadrangle by sorting the coordinates of theirmidpoints on the X-axis, as well as generate a set oflines between the 2 sides with the Tween Curve com-mand (Fig. 6a). The number of lines is decided by thedistance between the midpoints of Sides A and B,and the insertion of one line every 15 mm over thisdistance is predicted. In this case, the distance was216 mm, and 14 lines were inserted. These lines wereprojected onto the limb model to obtain cross-sections.However, if the quadrangle includes the gap between

the palm and thumb, multiple cross-sections will appearon these projections (yellow curves in Fig. 6a). The pres-ence of multiple cross-sections will cause the next Net-work operation to fail because only a single cross-section is allowed in each projection to generate the sur-face. Therefore, a procedure was designed to mergethese cross-sections (Fig. 6b). When dual cross-sectionswere detected, a line will pass through the central pointsof the separate cross-sections. This will be offset on bothsides as a rectangle, and a new union shape will be

formed by the combination of the rectangle and the con-nected cross-sections. The union shape is thensmoothed by extracting points from itself and regener-ated a similar shape by the Interpolate Curve command.These union shapes will replace the multiple cross-sections, maintaining a single shape in each projection.Additionally, the design of this slim gap between thecross-sections can fix the location of the thumb fortreatment demands.Then, the extreme points on the Y-axis of all cross-

sections were located and connected as two red curves(V Curves in Fig. 6c), and cross-sections were divided bythe 2 curves into a green set and a blue set. With the Vcurves, these can form 2 separate surfaces (green andblue surfaces in Fig. 6d) via the Network command. Theabove sequence only took a few seconds to generate thesurfaces as an initial result, and we subsequently visual-ized the immobilization area as a 3D surface. After thecovering surface was generated, the limb display couldbe turned off to allow the clinician to evaluate the insideof the surface. If the covering surface fit the limb well,the clinician can then trigger the program to continue

Side A

Side B

ab2. A union shape is combinedby the cross sections and therectangle.

b3. Multiple points wereextracted from the unionshape and generated asmooth shape.

b1. A line connected thecentral points of dual sectionsand generated a rectangle.b

Separated cross-sections

central points

c

Curve V

Curve V

d e

Fig. 6 The process of merging multiple cross-sections, generating covering surface and thickening. a Multiple cross-sections appear between thepalm and thumb. b Merging procedure of the cross-sections. c Extreme points on all cross-sections and V curves. d 2 separate surfaces are gener-ated by the V curves and cross-sections. e Thickening operation

Duplicatedscrew seats

Tube edges Obstructingcylinders

Engravingpattern

Engravedshell ca b

Fig. 7 The process of generating engraved shells, screw seats and tube edges. a The engraving pattern and the engraved shells. b Positions ofthe screw seats and tube edges. c Combined result


the thickening operation by offsetting the surfaces with athickness between 3 and 5 mm (Fig. 6e), depending onthe whole orthosis area.For calculating the engraving pattern (Fig. 7a), a rect-

angle was generated with the pattern’s area - the widthand length of the rectangle were determined based onthe averages of the covering surface edge lengths. Weapplied the common Voronoi diagram for the engravingpattern. The amount of seed points of the Voronoi dia-gram is defined as between 40 and 80 and is directlyproportional to the rectangle area. The pattern wasmapped onto the inside and outside surfaces of the shell,and hollowed out for the holes. The last step was todevelop screw seats and tube edges to increase theorthosis’ ability to be worn (Fig. 7b); these were sub-sequently combined with the engraved shells (Fig. 7c).A screw seat was embedded in the parametric model’sinternal source and copied to the 4 positions on theV curves. The tubes were generated along the edgesof the covering surface to provide a comfortableinterface between the skin and the rigid shell, and 4obstructing cylinders were used to separate the tubesand ensure that each tube only interlocked with oneof the shells. All the tubes and screw seats were dis-tributed and combined to their related shell by theSolid Union command, and the shells were then readyto be exported as a STL file.

Workflow and customized interfaceFrom the above modeling procedure, the overallworkflow of the orthosis design is shown as a flowchart (Fig. 8). This represents the exact process thatthe operator will face, and all detailed modeling cal-culations are working behind this interface. Steps 1and 2 are in the input stage, and step 3 to 7 belongto the modeling calculation that is controlled by theoperator. Considering that several clinicians may lackrelated fabrication experience with 3D printing, themain parameters in steps 3 to 6, such as the thick-ness and amount of Voronoi seed points, were opti-mized in iterative tests to archive a balance betweenbasic strength and being lightweight.Based on the workflow, the integrated interface of

Rhino and Grasshopper was customized (Fig. 9), and itwas simplified to reduce the learning period for the clin-ician. All menu, toolbars and panels in the Rhino inter-face were removed, and only 4 viewports and the Linestool are required for clinician to draw a quadrangle andevaluate the model state visually (Fig. 9a). On the Grass-hopper interface, the main node-based program andmenu are hidden (Fig. 9b), and the clinician does notneed to know how the program works. There are 7 ne-cessary components, which are displayed and numberedaccording to the workflow. The first two componentscan receive the data from the limb model and the

1.Import the limb model into Rhinoand assign to Grasshopper

2.Draw the quadrangle andset to Grasshopper

5. Generate the engraved shells

6. Generate the tube edgesand screw seats

7. Bake the modle for exportingSTL

Redrawand assign

3. Generate the coveringsurfaces

4. Generate the thickness

Fig. 8 Operation workflow of orthosis design


quadrangle by setting them in their componentmenus, and then generate the covering surfaces. Thenext 5 True/False toggles control the arm display,main data flow of the model generation and finalmodel export, and Toggles 4 to 6 can output Truevalues and an updated orthosis model in the ordershown in Fig. 8, including thicken surfaces, engravedshells and final shells with screw seats and tubeedges. Each step takes less than 10 s to update themodel.We collected 10 different anatomic models of the

upper limb from adult volunteers, and then applied theparametric model to these arms to determine the pro-gram’s performance and stability with different meshconditions. The optimization of the main parameterswas also studied from these samples. 6 sets of limbs,orthoses and their immobilization settings are shown inFig. 10. These limb models were also used in the follow-ing training and design exercise.

TrainingBased on the above modeling process, workflow andinterface, a training program was formulated to teachclinicians to utilize this parametric model of orthosisdesign and export a printable model. The trainingcontent included an introduction to 3D–printed orth-osis, an operating tutorial and computer-based

practice. Five nursing students in their junior yearwere invited to undergo this training, and they thencompleted an orthosis design exercise to evaluate thefunction of the parametric model and training. Theparticipants had internship experience in the ortho-pedic department in the hospital and were familiarwith manipulating fracture immobilizations. Theywere capable of operating document software, internetbrowsers and apps on mobile device in daily life, butdid not have any CAD background or 3D printingexperience.The introduction included demonstrations of the

digital models and physical orthosis and explanationsof the orthosis design and 3D printing process to theparticipants. One of the prototypes was prefabricatedwith a FDM printer Quditech1 (Qiditech) (Fig. 11)for demonstration, and this took approximately 3 h.The participant could learn how the 3D–printed orth-osis was assembled, produced and functioned forpatient rehabilitation. Then, the computer-basedtutorial was provided one-on-one, and the necessaryoperational knowledge of the Rhino and Grasshopperprograms was summarized as the following points:

� Basic viewport navigation in Rhino: The viewpointoperations include: zoom in/out, rotate view, panmove and switch viewport. These are basic skills

a. Rhino V5interface Lines tool

Inputcomponents

Processtoggles

Grasshopperinterface

b.

Fig. 9 Customized interface of Rhino and Grasshopper. a Rhino V5 interface. b Grasshopper interface


necessary to identify the CAD space and evaluatethe limb or orthosis models from any viewpointfreely.

� Draw and fix the quadrangle: The drawing isoperated by setting 4 corner points of a quadranglein the top view, and accomplished atomically whenthe shape is closed. If the quadrangle does notmatch the expected immobilization area, theoperator can redraw it to replace a previous one.

� Select Rhino object and assign to Grasshopper:Selecting and setting objects are usually executedtogether in the input task. The operator needs to learnthe select, cancel selection, and delete commands andthe selected object state. After selecting the model orpoly-line, the operator can input or clear settingcontents in the input component menu inGrasshopper.

� Control data flow in Grasshopper: Clicking thetoggles can change its output (True/False) and thensend out the geometric data to next modelingprocess. The orthosis model will be updated byclicking toggles in order, and most toggles do notwork if the previous toggle produced a false value.

� Solve program error or software crash: Sometimes,because the immobilization area overlapped on thelimb model’s edge or a hole, the parametric modelmay generate a distorted surface, separate objects orhave no response when attempting to update amodel, even causing Rhino to crash. Correcting theimmobilization area from the edge or hole can avoidthese problems.

1 2 3 4 5 6

Fig. 10 Stability test results for 6 different limbs

Fig. 11 Physical orthosis prototype of limb sample 6


5 limb models were used during this tutorial. The tutorused 2 of these to demonstrate the process, and partici-pants followed the same steps. The participants could askthe tutor to repeat the process until they had memorizedthe whole procedure and its underlying logic. Then, an-other 3 limb models were provided to participants forpractice, and they were asked to design orthoses withoutthe tutor’s help. The participants were encouraged to solvethe problems that arose during the process by themselvesas much as possible, but they could ask the tutor for hintsas needed. The total time during the training wasrecorded after they accomplished the procedure.

Orthosis design exerciseAfter the training, the participants completed a trialto design orthoses for another 5 limb models on their

own. The limb models were saved in different layersof a file, and participants were asked to switch thelayers and design the orthoses in order.The whole design process was recorded from the

screen as video, and the video was monitored andmarked with labels to identify every operator’s oper-ational movement (event) on the timeline as a barchart (Fig. 12). This sample chart shows the videorecord of an orthosis design process, a completedworkflow that took 1 min and 51 s. We used 5 typesof color labels to indicate the participant’s differentpurposes and working interfaces for each event onthe timeline. The period of marking a label was initi-ated and completed according to the user’s mouseclicks and the working interface. The purple labelrepresents the drawing immobilization line, and we

Timeline

(First design completed)

Label bar and descriptionofoperator’s behavior event

Action in Rhino

Drawing in Rhino

Setting action inGrasshopper

Toggle action inGrasshopper

Eventclassification

(Start)

Program error oroperation fault

Action in Rhino

Setting action inGrasshopper

Toggle action inGrasshopper

Fig. 12 Visualized format of participant’s video record, labeled bar chart


determined how much time each participant spent ondrawing to evaluate the difficulty of the task. The yel-low label represents the waiting time after the partici-pant clicked the toggles to obtain an updated model,and this was equivalent to the overall time of themodeling calculation. Program errors of the paramet-ric model or operation faults are indicated by the redlabel, and the length of the red label represents therequired time that participants needed to solve thesituation and continue with the correct workflow.These labels helped us to visualize where and howthe errors and mistakes occurred and the timeexpended on each event. Thus, we could improve theparametric model or training program.After the participants completed the exercise, an

interview was held. If the participant’s intention forany event was not obvious enough to determine alabel, e.g., they were confused or forgot a step, thetutor should confirm what occurred with the partici-pant in the interview. However, the main purpose ofthe interview was to collect the participants’ opinionsregarding the parametric model and training programbased on their experience.

Results and discussionA parametric CAD model for 3D–printed orthosis de-sign was developed for clinicians who were inexperi-enced with CAD tools. The model utilized Rhino 3D V5,and its parametric program was constructed usingGrasshopper 3D. We input 10 different limb modelsinto this parametric model representing the patients’affected limb and used different immobilization areasto generate a printable model. Overall, the parametricmodel was stable if no hole or edge was present inthe immobilization area.The parametric model’s interface and operation work-

flow have been extremely simplified to reduce the learn-ing period required for the clinician. For evaluating theinterface and parametric model performance, a trainingtutorial combining computer-based practice was createdand held for 5 participants who were capable of execut-ing conversional fracture immobilization. In the training,all participants followed the tutorial, realized the oper-ation steps and accomplished practice within 15 mins.Then, the participants completed an orthosis design

exercise, and they were asked to complete 5 orthosisdesigns using parametric models on their own. Theirrecorded videos of the design process were labeled A toE and visualized using color labels as in the bar charts ofFig. 13. We differentiated each orthosis design period onthe timeline from the other designs by black lines, andmarked the precise time points when they finished eachdesign, i.e., participant A finished the first orthosis at 4

mins and 24 s, whereas participant B spent 4 m 14 s.From the video visualization, the participants finishedthe 5 designs in a period ranging from 8 to 21 mins, andeach design took between 1 and 7 mins. Compared tomanual modeling, the time required for the parametricmodel has been reduced dramatically. From the inter-view, most participants gave positive evaluations of thisdigital tool and training, and more practice and verifica-tion with a printed model could help them avoid mis-takes and improve their drawing skills to define theimmobilization area.In the video, we marked the following program error

and participant mistakes with red labels.

� Wrong operation: The participant took unnecessarysteps or missed an operation, such as moving thewrong objects or utilizing extra clicks, althoughthese negative faults did not impact the processcritically. We added these faults as frequently askedquestions in the updated tutorial, which allowedother beginners to avoid repeating them.

� Input failure or invalid model generated: Usuallythese program errors occurred after the inputsetting or during the modeling calculation. Theparametric model did not update the orthosis modelafter the toggles were activated, e.g., the thickeningor engraving function failed, or sometimes itgenerated a valid model that had a distorted surfaceor separate objects on the shell. These errorsindicated defects in the Grasshopper program orlimb model.

� Software crash: If the participant wasn’t aware of theboundary or lost the mesh on the limb model, theymay have set immobilization areas overlapping themodel’s edge or a hole. The model process maygenerate incomplete cross-sections or geometriesand cause Rhino to crash because they disturbed thedata tree and initiated massive numbers of calcula-tions instantly in Grasshopper. The participantneeded to restart the software and modify theimmobilization area again.

The frequency of red label marking was enumeratedfrom 25 orthosis designs in Table 1. Only 2 crashes oc-curred, and the participants learned to modify theimmobilization area to solved these issues by themselves.Very high fault times occurred in participant A’s record,although most faults were minimal, such as moving thewrong object or performing extra clicks absentmindedly.The faults did not obstruct the participant significantly,and the parametric model worked perfectly.Additionally, several interesting discoveries from the

video provided useful feedback regarding the partici-pant’s behavior. We extracted the total time expended


4:24

8:20

12:35

17:02

20:42

0:00

11:16

4:14

8:24

14:46

20:24

4:03

5:17

6:27

7:33

13:45

1:51

3:49

5:37

7:54

10:18

2:26

3:32

4:42

7:04

8:08

A B C D E

Orthosis 1

Orthosis 2

Orthosis 3

Orthosis 4

Orthosis 5

Timeline

Participant

Fig. 13 Labeled bar chart of 5 participants’ video records

Table 1 Accumulation of red labels

1. Operation fault 2. Input failure orinvalid generation

3. Software crash

A 11 0 0

B 0 2 0

C 1 2 1

D 1 1 0

E 1 1 1


from the event labels as well as their percentages relativeto the whole process for each participant, and presentedthem as a pie chart in Fig. 14. In the analyzed results ofparticipant B, we found the participant made almost nomistakes in the operation during the exercise and nocrash occurred. The blue labels represented 57% of herprocess; the observations were confirmed from thevideo, with a high blue percentage indicating she spentmost of the time checking the limb model’s appearanceand edges in the viewport and deciding how to draw theimmobilization area. Her drawing avoided the edges andholes of the model skillfully, and this was the factor thatallows the program to generate the orthosis model suc-cessfully. Long seconds of drawing were found in partici-pant C’s process, and repeated drawing occurred in thevideo when the immobilization area did not cover theexpected surface. Developing another input drawingmethod could solve this challenge, but this is usuallylimited by the available drawing command and necessarygeometric logic to generate cross-sections. Additionally,long periods of waiting for software calculations also ap-peared in participant A and D’s videos, and longcalculations usually occurred in the engraving pattern,especially when it worked on a deformed surface aroundthe wrist. A hexagon or diamond array would be morestable than the Voronoi diagram for the engraving task.The random points of the Voronoi algorithm do margin-ally increase the risk of generating tiny holes and failingthe projection on the surface.

ConclusionIn this study, to address the relative advantages anddisadvantages of Direct Modeling and Parametric

Modeling, a design generator of 3D–printed orthosisand its detailed modeling process were created anddeveloped by the Visual Programming Language in anengineering CAD environment. The interface andworkflow of the parametric model were successful foruse by clinicians who were inexperienced to CADsoftware.In the evaluation of the feasibility of this digitalized

tool, we received positive feedback from the training anddesign exercise in 3 target areas:

� Required period of beginner training: In the training,the simplified interface and workflow successfullyreduced the required geometric knowledge forparticipants and the required training period to 15mins.

� Operation efficiency for clinicians: In the orthosisdesign exercise, under the parametric model’sfacilitation, the required time for executing anorthosis design was dramatically reduced to a fewminutes.

� Performance stability of the parametric model: Thefrequency of software crashes and program errorswas acceptable for 25 designs and can likely beimproved with further study.

Finally, the process and results of the design exercisereflected the real-world situation of clinicians operatingthe parametric model for orthosis generation and alsorevealed unforeseen factors, such as participant operat-ing difficulty, personal behavior and reasons for programerrors or good performances. These discoveries provideda clear direction for improving the parametric model

B

viewport drawing setting calculation error

A C

D E

Fig. 14 Pie chart of event percentages and seconds spent in the participants’ videos. The pie charts are marked A to E to present theparticipant labels


and training program and helped advance this tool to-wards a more practical level for medical applications.

AcknowledgementsThe authors would like to thank Dr. Dinghau Huang of Ming Chi University ofTechnology and Prof. Pi-Hsia Liu of the Chang Gung University of Science andTechnology for their arrangement for voluntary participants in the orthosisdesign exercise, and great advice concerning the interviews.

FundingThe work described in this paper was supported by the Doctorate StudentGrant-in-Aid Program (Graduate School Recommendation, Shannon FujisawaCampus) 2017 of the Keio University, Japan.

Availability of data and materialsData are available by contacting the corresponding author.

DisclaimerThe conjecture about the modeling process of orthoses in Fig. 1 representsthe author’s viewpoint, and the description does not reflect the originalcreators’ methods.

Authors’ contributionsJL: Scanner mount, Parametric modeling, Training formulation, Designexercise, Revision of manuscript. HT: Study design, Methodology, Draftingand revision of manuscript. Both authors read and approved the finalmanuscript.

Competing interestsThe authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Received: 25 October 2017 Accepted: 26 December 2017

References1. Hieu LC, Sloten JV, Hung LT, Khanh L, Soe S, Zlato, N, Phuoc LT, Trung PD.

Medical reverse engineering applications and methods. 2nd InternationalConference On Innovations, Recent Trends and Challenges In Mechatronics,Mechanical Engineering and New High-Tech Products Development. 2010;232–46.

2. Rengier F, Mehndiratta A, von Tengg-Kobligk H, Zechmann CM,Unterhinninghofen R, Kauczor HU, Giesel FL. 3D printing based on imagingdata: review of medical applications. Int J Comput Assist Radiol Surg. 2010;5(4):335–41. https://doi.org/10.1007/s11548-010-0476-x

3. Hieu LC, Bohez E, Vander Sloten J, Phien HN, Vatcharaporn E, Binh PH, AnPV, Oris P. Design for medical rapid prototyping of cranioplasty implants.Rapid Prototyping J. 2003;9(3):175–86. https://doi.org/10.1108/13552540310477481

4. Mavroidis C, Ranky RG, Sivak ML, Patritti BL, DiPisa J, Caddle A, Gilhooly K,Govoni L, Sivak S, Lancia M. Patient specific ankle-foot orthoses using rapidprototyping. Neuroeng Rehabil J. 2011;8(1):1.

5. Negi S, Dhiman S, Sharma RK. Basics and applications of rapid prototypingmedical models. Rapid Prototyping J. 2014;20(3):256–67. https://doi.org/10.1108/RPJ-07-2012-0065

6. Faustini MC, Neptune RR, Crawford RH, Stanhope SJ. Manufacture of passivedynamic ankle–foot orthoses using selective laser sintering. IEEE TransBiomed Eng. 2008;55(2):784–90.

7. Salmi M, Tuomi J, Paloheimo S, Bjo R, Björkstrand R, Paloheimo M. Patient-specific reconstruction with 3D modeling and DMLS additivemanufacturing. Rapid Prototyping J. 2012;18(3):209–14. https://doi.org/10.1108/13552541211218126

8. Lin H, Shi L, Wang D. A rapid and intelligent designing technique forpatient-specific and 3D-printed orthopedic cast. 3D Print Med J. 2015;2:4.

9. Gabriele B, Sami H, Alberto S. A critical analysis of a hand orthosis reverseengineering and 3D printing process. Applied Bionics & Biomechanics J.2016;2016:8347478.

10. Palousek D, Rosicky J, Koutny D, Stoklásek P, Navrat T. Pilot study of thewrist orthosis design process. Rapid Prototyping J. 2014;20(1):27–32.

11. Kim H, Jeong S. Case study: hybrid model for the customized wrist orthosisusing 3D printing. J Mech Sci Technol. 2015;29(12):5151–6.

12. Amiri, A., Varghese, J. & Demurchyan, G. Technical Report of Politecnico diMilano 2016–17 Toward In-situ Realization of Ergonomic Hand / ArmOrthosis, A Pilot Study on the Process and Practical Challenges. 2017.https://doi.org/10.13140/RG.2.2.18381.84968

13. Paterson AM, Bibb RJ, Campbell RI. Evaluation of a digitized splintingapproach with multi-material functionality using additive manufacturingtechnologies. In: Proceedings of the 23rd annual international solid freeformfabrication symposium; 2012. p. 656–72.

14. Paterson AM, Bibb RJ, Campbell RI, Bingham G. Comparing additivemanufacturing technologies for customised wrist splints. Rapid PrototypingJ. 2015;21(3):230–43.

15. Bonarrigo F, Signoroni A, Leonardi R. Multi-view alignment with database offeatures for an improved usage of high-end 3Dscanners. J Adv SignalProcess. 2012;2012:148.

16. Bonarrigo F, Signoroni A, Botsch M. Deformable registration using patch-wise shape matching. Graphical Models J. 2014;76(5):554–65. https://doi.org/10.1016/j.gmod.2014.04.004

17. Paterson AM, Bibb RJ, Campbell RI. A review of existing anatomical datacapture methods to support the mass customisation of wrist splints. VirtualPhys Prototyping J. 2010;5(4):201–7.

18. Bibb R, Freeman P, Brown R, Sugar A, Evans P, Bocca A. An investigation ofthree-dimensional scanning of human body surfaces and its use in thedesign and manufacture of prostheses. Eng in Med J. 2000;214(6):589–94.

19. Salmi M, Paloheimo S, Tuomi J, Wolff J, Mäkitie A. Accuracy of medicalmodels made by additive manufacturing. Cranio- Maxillofac Surg J. 2013;41(7):603–9. https://doi.org/10.1016/j.jcms.2012.11.041

20. Renno F, Papa S. Direct modeling approach to improve virtual prototypingand FEM analyses of bicycle frames. Engineering Letters J. 2015;23:4.333–41.

21. Fu L, Kara LB, Shimada K. Feature, design intention and constraintpreservation for direct modeling of 3D freeform surfaces. 3D Res. 2012;3:3.https://doi.org/10.1007/3DRes.02(2012)3

22. Tornincasa S, Monaco F. The future and the evolution of CAD. 14thInternational Research/Expert Conference: Trends in the Development ofMachinery and Associated Technology, 2010.

23. Hamilton, P., (2013) Parametric modeling vs. direct modeling. http://www.metalformingmagazine.com/assets/issue/pdf/diedesign2013/direct_vs_parametric.pdf . Accessed 30 May 2013.

24. Harding JE, Shepherd P. Meta-parametric design. Design Studies J. 2017;52:73–95.

25. Activarmor, http://activarmor.com26. Zdravprint, http://zdravprint.ru27. Fantini M, Curto M, Crescenzio FD. A method to design biomimetic

scaffolds for bone tissue engineering based on Voronoi lattices. VirtualPhysical Prototyping J. 2016;11(2):1–14. https://doi.org/10.1080/17452759.2016.1172301

28. XKELET, https://www.xkelet.com29. CORTEX, http://www.evilldesign.com/cortex30. Osteoid, http://www.osteoid.com31. Krish S. A practical generative design method. CAD J. 2011;43(1):88–100.32. Aish R, Hana S. Comparative evaluation of parametric design systems for

teaching design computation. Design Studies J. 2017;52:144–72.33. Celani G, Vaz CEV. CAD scripting and visual programming languages for

implementing computational design concepts: A comparison from apedagogical point of view. Int J Architectural Computing. 2012;10(1):121–37.

34. Leitão A, Santos L, Lopes J. Programming languages for generative design:a comparative study. Int J Archit Computing. 2012;10(1):139–62. https://doi.org/10.1260/1478-0771.10.1.139


http://dx.doi.org/10.1007/s11548-010-0476-xhttp://dx.doi.org/10.1108/13552540310477481http://dx.doi.org/10.1108/13552540310477481http://dx.doi.org/10.1108/RPJ-07-2012-0065http://dx.doi.org/10.1108/RPJ-07-2012-0065http://dx.doi.org/10.1108/13552541211218126http://dx.doi.org/10.1108/13552541211218126http://dx.doi.org/10.13140/RG.2.2.18381.84968http://dx.doi.org/10.1016/j.gmod.2014.04.004http://dx.doi.org/10.1016/j.gmod.2014.04.004http://dx.doi.org/10.1016/j.jcms.2012.11.041http://dx.doi.org/10.1007/3DRes.02(2012)3http://www.metalformingmagazine.com/assets/issue/pdf/diedesign2013/direct_vs_parametric.pdfhttp://www.metalformingmagazine.com/assets/issue/pdf/diedesign2013/direct_vs_parametric.pdfhttp://www.metalformingmagazine.com/assets/issue/pdf/diedesign2013/direct_vs_parametric.pdfhttp://activarmor.comhttp://zdravprint.ruhttp://dx.doi.org/10.1080/17452759.2016.1172301http://dx.doi.org/10.1080/17452759.2016.1172301https://www.xkelet.comhttp://www.evilldesign.com/cortexhttp://www.osteoid.comhttp://dx.doi.org/10.1260/1478-0771.10.1.139http://dx.doi.org/10.1260/1478-0771.10.1.139

AbstractBackgroundResultsConclusions

BackgroundMethodScanning processCAD software and parametric modeling toolAutomatic arrangementParametric modeling processWorkflow and customized interfaceTrainingOrthosis design exercise

Results and discussionConclusionFundingAvailability of data and materialsDisclaimerAuthors’ contributionsCompeting interestsPublisher’s NoteReferences

Documents

Feasibility study applying a parametric model as the design … · 2018. 2. 9. · tutorial, including viewport navigation, poly-line drawing in Rhino and object setting in Grasshopper