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Causes of Desktop FDM Fabrication Failures in an Open Studio Environment

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The usage phase often dominates the lifetime environmental impact of energy and material consuming products. Improper human behaviors could increase the environmental impacts during the use stage of the product’s life cycle. The use phase of desktop fused deposition modeling (FDM) printers involves large energy and material consumption. In university makerspaces, many novices choose to use desktop FDM printers because of its easy operation, but novices often make improper design and operation decisions. The human errors increase the fabrication failures leading to higher material and energy consumption. Therefore, human behaviors should be investigated in order to identify the causes of fabrication failures in an open studio environment. Three types of failure causes are studied, which are designer error, operator error and machine error. In this research, computer-aided design (CAD) models, printing settings, user experience information and printing results were tracked and analyzed. In addition, the energy and material consumption were recorded from the printings. These data are analyzed to identify the key factors affecting printing failure rates and to improve the sustainability and efficiency of desktop FDM. From the collected data, a failure rate of 41.1% was observed. The failures caused by human error accounted for 26.3% of the total prints, which shows that human behaviors could influence the environmental impacts of FDM. For the factors impacting failure rates, user’s experience level and printing parameters were analyzed. We found that that experience did not result in higher expertise or lower failure rates.
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Procedia CIRP 00 (2017) 000–000
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2212-8271 © 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th C IRP Design Conference 2018.
28th CIRP Design Conference, May 2018, Nantes, France
A new methodology to analyze the functional and physical architecture of
existing products for an assembly oriented product family identification
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu
Abstract
In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of
agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production
systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to
analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and
nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production
system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster
these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable
assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and
a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the
similarity between product families by providing design support to both, production system planners and product designers. An illustrative
example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of
thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.
© 2017 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.
Keywords: Assembly; Design method; Family identification
1. Introduction
Due to the fast development in the domain of
communication and an ongoing trend of digitization and
digitalization, manufacturing enterprises are facing important
challenges in today’s market environments: a continuing
tendency towards reduction of product development times and
shortened product lifecycles. In addition, there is an increasing
demand of customization, being at the same time in a global
competition with competitors all over the world. This trend,
which is inducing the development from macro to micro
markets, results in diminished lot sizes due to augmenting
product varieties (high-volume to low-volume production) [1].
To cope with this augmenting variety as well as to be able to
identify possible optimization potentials in the existing
production system, it is important to have a precise knowledge
of the product range and characteristics manufactured and/or
assembled in this system. In this context, the main challenge in
modelling and analysis is now not only to cope with single
products, a limited product range or existing product families,
but also to be able to analyze and to compare products to define
new product families. It can be observed that classical existing
product families are regrouped in function of clients or features.
However, assembly oriented product families are hardly to find.
On the product family level, products differ mainly in two
main characteristics: (i) the number of components and (ii) the
type of components (e.g. mechanical, electrical, electronical).
Classical methodologies considering mainly single products
or solitary, already existing product families analyze the
product structure on a physical level (components level) which
causes difficulties regarding an efficient definition and
comparison of different product families. Addressing this
Procedia CIRP 80 (2019) 494–499
2212-8271 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/)
Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
10.1016/j.procir.2018.12.007
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/)
Peer-review under responsibility of the scientic committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2018) 000000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
doi:10.1016/j.procir.2017.04.009
26th CIRP Life Cycle Engineering (LCE) Conference
Causes of Desktop FDM Fabrication Failures in an Open Studio
Environment
Ruoyu Song, Cassandra Telenko*
George W. Woodruff School of Mechanical Engineering, Georgia Insitute of Technology, Atlanta GA 30332, USA
* Corresponding author. Tel.: +1-404-385-3801. E-mail address: cassandra@gatech.edu
Abstract
The usage phase often dominates the lifetime environmental impact of energy and material consuming products. Improper human behaviors
could increase the environmental impacts during the use stage of the product’s life cycle. The use phase of desktop fused deposition modeling
(FDM) printers involves large energy and material consumption. In university makerspaces, many novices choose to use desktop FDM printers
because of its easy operation, but novices often make improper design and operation decisions. The human errors increase the fabrication
failures leading to higher material and energy consumption. Therefore, human behaviors should be investigated in order to identify the causes
of fabrication failures in an open studio environment. Three types of failure causes are studied, which are designer error, operator error and
machine error. In this research, computer-aided design (CAD) models, printing settings, user experience information and printing results were
tracked and analyzed. In addition, the energy and material consumption were recorded from the printings. These data are analyzed to identify
the key factors affecting printing failure rates and to improve the sustainability and efficiency of desktop FDM. From the collected data, a
failure rate of 41.1% was observed. The failures caused by human error accounted for 26.3% of the total prints, which shows that human
behaviors could influence the environmental impacts of FDM. For the factors impacting failure rates, user’s experience level and printing
parameters were analyzed. We found that that experience did not result in higher expertise or lower failure rates.
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
Keywords: Additive manufacturing; fused deposition modeling; failure analysis, makerspaces, building cost
1. Introduction
Nowadays, people can engage in product design and
manufacturing easily because of the development of additive
manufacturing (AM). Compared to traditional manufacturing
processes, AM is more easily implemented in a home
environment and can minimize material waste, reduce
shipping costs and make parts more efficiently in a small
batch [1,2]. Therefore, AM machines have become popular in
makerspaces in universities and schools. However, the
environmental impacts of AM could increase dramatically due
to inexperienced users and malfunctions of inexpensive
machines.
Fused deposition modeling (FDM) is the most widespread
AM technologies in university makerspaces. Barrett et al.
found that desktop FDM machines such as MakerBots, are the
most common piece of equipment by studying 40
makerspaces that were identified from 127 top undergraduate
institutes in United States [3]. The FDM machine produces
parts by extruding molten material to form layers as the
material hardens. Desktop FDM printers are popular because
of compact sizes, affordable prices and low maintenance
costs. Desktop FDM printers are also novice-friendly because
of its easy operation. With free open-source slicer software
tools such as Cura, Slic3r and Repetier, the users can upload
their 3D files, set the printing parameters and print parts
easily.
Available online at www.sciencedirect.com
ScienceDirect
Procedia CIRP 00 (2018) 000000
www.elsevier.com/locate/procedia
2212-8271 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
doi:10.1016/j.procir.2017.04.009
26th CIRP Life Cycle Engineering (LCE) Conference
Causes of Desktop FDM Fabrication Failures in an Open Studio
Environment
Ruoyu Song, Cassandra Telenko*
George W. Woodruff School of Mechanical Engineering, Georgia Insitute of Technology, Atlanta GA 30332, USA
* Corresponding author. Tel.: +1-404-385-3801. E-mail address: cassandra@gatech.edu
Abstract
The usage phase often dominates the lifetime environmental impact of energy and material consuming products. Improper human behaviors
could increase the environmental impacts during the use stage of the product’s life cycle. The use phase of desktop fused deposition modeling
(FDM) printers involves large energy and material consumption. In university makerspaces, many novices choose to use desktop FDM printers
because of its easy operation, but novices often make improper design and operation decisions. The human errors increase the fabrication
failures leading to higher material and energy consumption. Therefore, human behaviors should be investigated in order to identify the causes
of fabrication failures in an open studio environment. Three types of failure causes are studied, which are designer error, operator error and
machine error. In this research, computer-aided design (CAD) models, printing settings, user experience information and printing results were
tracked and analyzed. In addition, the energy and material consumption were recorded from the printings. These data are analyzed to identify
the key factors affecting printing failure rates and to improve the sustainability and efficiency of desktop FDM. From the collected data, a
failure rate of 41.1% was observed. The failures caused by human error accounted for 26.3% of the total prints, which shows that human
behaviors could influence the environmental impacts of FDM. For the factors impacting failure rates, user’s experience level and printing
parameters were analyzed. We found that that experience did not result in higher expertise or lower failure rates.
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the scientific committee of the 26th CIRP Life Cycle Engineering (LCE) Conference.
Keywords: Additive manufacturing; fused deposition modeling; failure analysis, makerspaces, building cost
1. Introduction
Nowadays, people can engage in product design and
manufacturing easily because of the development of additive
manufacturing (AM). Compared to traditional manufacturing
processes, AM is more easily implemented in a home
environment and can minimize material waste, reduce
shipping costs and make parts more efficiently in a small
batch [1,2]. Therefore, AM machines have become popular in
makerspaces in universities and schools. However, the
environmental impacts of AM could increase dramatically due
to inexperienced users and malfunctions of inexpensive
machines.
Fused deposition modeling (FDM) is the most widespread
AM technologies in university makerspaces. Barrett et al.
found that desktop FDM machines such as MakerBots, are the
most common piece of equipment by studying 40
makerspaces that were identified from 127 top undergraduate
institutes in United States [3]. The FDM machine produces
parts by extruding molten material to form layers as the
material hardens. Desktop FDM printers are popular because
of compact sizes, affordable prices and low maintenance
costs. Desktop FDM printers are also novice-friendly because
of its easy operation. With free open-source slicer software
tools such as Cura, Slic3r and Repetier, the users can upload
their 3D files, set the printing parameters and print parts
easily.
Ruoyu Song et al. / Procedia CIRP 80 (2019) 494–499 495
2 Author name / Procedia CIRP 00 (2019) 000000
However, the environmental impact of FDM could
significantly increase due to human and machine errors.
Novice users, the major users of desktop FDM printers, are
often inexperienced in design and operation and can make
improper decisions leading to fabrication failures. In addition,
the inexpensive desktop FDM printers are more prone to
malfunctions which can also result into fabrication failures.
Our prior studies in two different university makerspaces
found that the material waste rates are from 35% to 45% [4].
These failures can increase the life cycle energy costs by 50%
or more [5].
Such failures may be useful in education because it can
help students to better understand the structures and
constraints of problems [6]. Embracing failure has also been
identified as one of the three guiding principles for an
educational makerspace [7]. However, youth can experience
the failures of makings as demoralizing [8]. In addition, since
there are always limited number of printers in a makerspace,
failures can result in inefficiency of the makerspace operation.
Many of the printing failures could be caused by user
behaviors. User behaviors could result into uncertainty and
variability when estimating environmental impacts of FDM
printers. For example, a makerspace that does not require any
training before using the printers could lead to higher
environmental impacts [4]. Investigating user behaviors to
reduce environmental impacts of FDM printers is especially
important because FDM is expected to make AM a tool for
everyday household life [9]. According to Wohlers Report
[10], more than 278,000 desktop 3D printers were sold
worldwide in 2015. The market of desktop 3D printers further
grew by 49.4% worldwide in 2016 [11]. Therefore, FDM
printers will consume large quantities of energy and material
if its adoption reaches that of inkjet and laser printers. For
inkjet and laser printers, Kawamoto et al. estimated that the
stock of laser printers was 28 million and the stock of inkjet
printers was 74 million at the end of 1999 consuming 6.23
TWh/year and 2.88 TWh/year, respectively [12].
Few studies look into the causes of fabrication failures in
makerspaces. User experience and expertise level may
influence the possibility of fabrication failures. Cerdas et al.
found that experienced users could better select printing
parameters that minimized waste [13]. This study aims to
investigate how failure rates change with user experience and
expertise level in university makerspaces. The printing
failures and daily users of various levels of experience were
studied in an open-access university makerspace. Specifically,
user experience level, computer-aided design (CAD) models,
printing parameters and results were tracked and analyzed.
2. Literature Review
This section reviews the background of the research in two
fields, which are FDM in makerspaces and fabrication failures
of FDM.
2.1. FDM in Makerspaces
FDM printers are the most common equipment in
university makerspaces [3]. Since AM can produce parts in a
small batch efficiently [1], students in the makerspaces can
use the FDM printers to build physical prototypes easily.
Integrating AM and makerspaces enables the democratization
of design and manufacturing [14].
Combining AM and makerspaces can also assist students
learning design and manufacturing. Practicing physical
prototyping can help students gain deeper understanding of
design requirements and features [15]. Hand-on experiences
in the makerspaces can help students realize the limitations of
FDM. From a survey in Georgia Tech Invention studio, more
than 80% of students self-reported a positive impact on their
manufacturing skills [16].
However, the environmental impacts of FDM increase
significantly in this type of open studio environment due to
fabrication failures. A life cycle inventory combining material
waste and energy consumption showed that the energy
consumption of FDM in an open-access makerspaces may be
50% more than under controlled experiments [5]. With the
development of AM, people may be able to fabricate products
by themselves at home, and FDM is expected to become a
tool for everyday household life [9]. FDM printers will
consume large quantities of material and energy. Therefore,
practices should be suggested to decrease the fabrication
failures and environmental impacts of FDM in open studio
environments.
2.2. Fabrication Failures of FDM
When evaluating the environmental impacts of AM, few
studies investigated the effects of fabrication failures. Most
studies focused on energy usage in successful and controlled
printing scenarios [1,17,18] and how to determine the optimal
printing orientations [1921]. Our previous work classified
nine types of AM failures by collecting failed AM prints from
a university makerspace, and categorized them into designer
error, operator error and machine error [22].
In addition, the relationships between part geometries,
printing parameters and manufacturing failures are explained
by a few studies. Seepersad et al. created a designers guide
for dimensioning and tolerancing selective laser sintering
(SLS) parts [23]. This research determined the limiting feature
sizes of various types of features including slits, holes, letters,
mating gears and shafts for SLS through a series of
experiments. Many other design for AM guidelines were
developed [2427]. However, Booth et al. argued that some
guidelines are more specific than most novice users need or
understand, several even tend to be out of the control of many
novices [28]. They developed a design for AM worksheet for
novice and intermittent users, which can decrease the failure
rate by 81.7%.
3. Methodology
For this research, one hypothesis was tested: failure rates
decrease if users have more experience and higher education
level. We examined the type and number of FDM failures and
user demographics in a free-access university makerspace, the
Invention Studio at the Georgia Institute of Technology. The
makerspace is run by trained volunteers who are
496 Ruoyu Song et al. / Procedia CIRP 80 (2019) 494–499
Author name / Procedia CIRP 00 (2019) 000000 3
undergraduate and graduate students. All students can use the
printers without training requirements. This study was
approved under protocol H17008 by Georgia Tech IRB.
The Invention Studio has a 3D printing makerspace with 9
Ultimaker 2+ printers in Fall 2017 semester, and 10 Ultimaker
2+ printers in Summer 2018 semester. The Ultimaker 2+
printer is a FDM printer with build volume of 223 x 223 x 305
mm and resolution of 0.02 to 0.60 mm. The printers used PLA
as the raw material.
From our previous research in this makerspace [5], we
collected failure rates every two weeks in one semester and
found that the failure rate for one time period was
significantly lower than other time periods. That time period
was spring break that the makerspace was closed to public
and only trained staff could access it and use the printers. In
addition, we found that the makerspace requires training
before using the printers lead to lower failure rates than the
makerspace does not require any training. Therefore, we
assume that user experience level may influence failure rates.
To test the hypothesis, we collected and analyzed CAD
files, failed parts and energy consumption within the
makerspace. For each CAD file, we recorded the source of the
part, if it was created by the user or downloaded from online
websites such as GrabCAD.com and Thingiverse.com.
Additionally, we observed individual students using the
printers and the decisions that they made. Users’ experience
information including education level and number of times
using CAD software and 3D printers was collected in order to
test the relationship between failure rates and users’ design
experience level, operation experience level and education
level. The following multiple-choice questions were used in
the survey:
1. For how many projects have you used CAD software?
a. <3
b. 3-5
c. 6-10
d. >10
2. How many different parts have you printed on a 3D
printer?
a. This is the first time
b. <5
c. 6-10
d. More than 10
3. How often do you use 3D printers in the Invention
Studio?
a. <4 times/semester
b. 1-3 times/month
c. 1-3 times/week
d. >3 times/week
4. Which year of study are you in?
a. Freshman
b. Sophomore
c. Junior
d. Senior
e. Masters
f. MS/PhD
g. PhD
The printing parameters set in the slicer software were
recorded including layer height, infill density, print speed and
support material settings. The slicer software used in the
makerspace is Ultimaker Cura. In addition, the users were not
allowed to alter the temperature of the extruder and the
printing bed.
For each printing job, we recorded if the print was a
success or a failure. We asked the users if they considered the
print a success or a failure and checked for physical defects. If
the print failed, the failure reason was recorded.
To measure the energy consumed by failed prints, HOBO
UX120-018 Plug Load Data Loggers were connected to 6
Ultimaker 2+ printers in the makerspace. The data logger
recorded the power in Watts at a sampling rate of 0.1 Hz. The
data from the loggers were exported and saved regularly.
Based on the collected information, the energy consumption
data for failed prints were extracted and recorded. The mass
of the failed prints was measured using a scale, accurate to 2g.
The collected data were analyzed to show the causes of
failures. Three sets of analysis of variance (ANOVA) were
done for the user’s experience level, the source of part and
printing settings.
4. Results
In total 95 sets of individual observations were recorded
with 39 failed prints. The overall failure rate was 41.1%. The
average energy consumption per part was 3.0 MJ. The
average mass of the failed parts was 28 g. The printing energy
intensity is 107.1 MJ/kg. Table 1 summarizes the failure
category, primary cause, detailed cause and number of failed
prints with respect to each failure cause. More explanation of
detailed causes can be found in our previous work [5].
Table 1. Failure reasons and number of prints
Category
Primary Cause
Detailed Cause
# of Prints
Designer
Support Material
Support cannot be removed
2
9
Feature Size
Complex features
3
Cannot assemble
2
Wrong part size
1
Wrong hole size
1
Operator
Printing Settings
Did not generate support
2
16
Printed out of area
1
Printer Operation
Loose calibration
8
Platform was moved
1
Printed wrong file
1
Out of filament
2
Filament tangled
1
Machine Machine
Malfunction
Skip layers
1
14
Nozzle clogged
10
Layer shift
3
Among the 39 failures, nine failures were caused by
designer errors. Two prints failed because the designers did
not consider support material removing process. Therefore,
the support material could not be removed and ruined the
Ruoyu Song et al. / Procedia CIRP 80 (2019) 494–499 497
4 Author name / Procedia CIRP 00 (2019) 000000
surface finish. Since support material is not needed for
traditional manufacturing processes, the designers may not
consider it. Nine prints failed because of improper feature
sizes. If the designers did not consider the resolutions of the
FDM printers, they may design too complex and small
features which cannot be printed. If the designers did not
consider the tolerances of the FDM printers, the mating parts
could not be assembled. In addition, two parts failed because
the designers specified wrong feature sizes for printing.
Sixteen failures were caused by operator errors including
improper printing settings and operations. Three prints failed
because the operators did not choose to generate support
material for parts with overhang structures. From the
observations, the operators did not click the option to generate
support material in the slicer software because they did not
know what the function of support material was. One part
failed because the operator placed the part out of the printable
area of the printer in the slicer software. Eight prints failed
because the printers were not calibrated properly. Therefore,
the prints could not adhere to the printing platform and
warped; the layers of the prints could not adhere to each other
either. One print failed because the printing platforms were
moved accidentally during the printing process. One print
failed because the operator uploaded or selected a wrong file
to print. Three parts failed because the operators did not check
the status of the filaments before printing. Among these three
failures, two prints failed because the remaining filament was
not enough for the parts. One print failed because the printer
was not able to extrude the tangled filament.
Fourteen failures were caused by machine errors. The
primary cause was nozzle clogging (71.4% of failures).
Nozzle clogging could be caused by incorrect temperature for
extruding, poor quality filament, tight calibration and printer
aging. To decrease the environmental impacts of failures
caused by nozzle clogging, printer should be stopped and
repaired timely. From the observations, sometimes the
printers ran without extruding any material to the end of the
programmed printing process, which wasted a large amount of
energy.
We have analyzed the material and energy loss due to each
failure cause and its uncertainty and variability in our
previous work [4,5,22]. In this paper, we analyzed the impacts
of user’s experience level and printing settings on fabrication
failures of FDM.
4.1. Impacts of User’s Experience Level
We expected that a user with higher experience level is less
likely to make failed prints. Therefore, data were analyzed to
show the relationship between user’s experience level and
failure rates. Table 2 shows the summary of the collected data,
which includes the failure rates caused by designer error,
operator error, overall failure rates and number of users for
each experience level respectively.
For designer errors, failures rates decreased as more CAD
projects have been done. The failure rates of designer errors
also decreased as more parts printed. Higher print frequency
increased the failures rates of designer errors. For the year of
study, the failure rate of Junior were four times as much as
that of senior and higher. Therefore, design experiences
gained in CAD projects, previous printed parts and knowledge
learnt from class could decrease the fabrication failures
caused by designer errors. Without such experience and
knowledge, higher print frequency cannot benefit the
designers.
Table 2. Summary of user’s experience level vs. failure rates
Category Experience Level
Failure Rates (%)
# of
Users
Designer
Error
Operator
Error Overall
Number
of CAD
projects
<3
9.5
23.8
52.4
21
3-5
9.1
13.6
36.4
22
6-10
27.3
9.1
45.5
11
>10
4.9
17.1
36.6
41
Number
of parts
printed
0
10.0
0
30.0
10
<5
14.3
14.3
32.1
28
6-10
5.9
11.8
41.2
17
>10
7.5
25.0
50.0
40
Print
frequency
<4/semester
6.4
10.8
27.0
37
1-3/month
12.5
31.3
68.8
16
1-3/week
4.5
9.1
18.2
22
>3/week
15.0
25.0
70.0
20
Year of
study
Freshman
50.0
50.0
100
2
Sophomore
0
25.0
50.0
4
Junior
43.5
21.7
34.8
23
Senior and higher
10.6
13.6
40.9
66
Not all parts printed were created by students themselves.
Some users downloaded parts designed by experts from
websites such as GrabCAD.com and Thingiverse.com. we
expected that failure rates for parts designed by experts are
lower than parts designed by novice designers. Among the 95
prints, 26 parts were CAD file downloaded from internet. The
other 69 parts were created by students themselves. We
consider the students as novice designers since they do not
have enough amount of design experiences. From calculation,
the failure rate for expert parts was 44.9%, and for novice
parts was 26.9%. Therefore, parts created by users with higher
level of design experience are less likely to fail. An ANOVA
was done for the source of part. However, the p-value is 0.22
which shows no statistical significance.
Improper part geometries could lead to fabrication failures.
When designing the parts, the designers should have ideas of
the printer specifications including resolutions and tolerances.
If the designed feature sizes are too small based on the given
printer resolutions, the features cannot be printed. If the
designers do not consider the tolerances of the printers, they
may create mating parts with same size and have risks that the
parts cannot be assembled. In addition, the design should
avoid large and flat area since they tend to warp. From the
observations, there were four failures caused by loose
calibration, but could also resulted from part geometry issues,
since all four parts had large and flat areas.
To reduce the fabrication failures caused by part geometry
issues, designers should know the printer specifications. If
498 Ruoyu Song et al. / Procedia CIRP 80 (2019) 494–499
Author name / Procedia CIRP 00 (2019) 000000 5
possible, test parts with different geometries and feature sizes
could be printed in order to have a deeper understanding of
the printer’s capacities.
For operator errors, the failure rates did not change
significantly with the number of CAD projects done. The
number of CAD projects relates to users’ design experience,
which should not impact the operator experience. The failure
rates decreased as higher year of study. In addition, the failure
rates increased with more parts printed. The failure rates did
not change significantly with higher print frequency. These
two observations were in contrast with our expectations.
An ANOVA was done for the statistical analysis of the
experience variables impacting the failures rates. However, no
statistical significance was shown for the results. The p-value
for CAD experience is 0.20, for parts printed is 0.10, for
printing frequency is 0.28. The result for year of study is not a
full rank (rank deficiency) which means the right observations
to fit the model are not in the data.
4.2. Impacts of Printing Settings
We expected that there should be a set of optimal printing
settings when using Ultimaker 2+, which can minimize the
failure rate. To figure out the optimal settings, the layer
height, infill density, infill pattern, print speed, support
material settings and build plate adhere type were
investigated.
Fig. 1 shows the failure rates versus four different printing
parameters: layer height, infill density, print speed and
support overhang angle. The support overhang angle is the
maximum angle of overhang structure for which support
material is added. The smaller the angle is, the more the
support material is added. From the diagram, the failure rates
increased with larger layer height. The infill density did not
show obvious relationship with the failure rates. When the
print speed was at 50 mm/s, the failure rates were at the
lowest point. The failure rates increased with higher support
overhang angle.
Fig. 1. Failure Rates vs. Printing Parameters
For the infill pattern, the failure rates for Cubic was 28.6%,
for Lines was 50.0%, for Grid was 50.0%, and for Triangles
was 50.0%. Therefore, the infill pattern did not have
significant influence of the failure rates. For the build plate
adhere type, the failure rates for Brim was 21.8%, for Raft
was 100%, for Skirt was 76.9%. Therefore, to decrease the
failure rate, Brim could be chosen as the build plate adhere
type.
An ANOVA was done for the impacts of printing settings
on the failure rate. However, no statistical significance was
shown for the results. The p-value for the layer height is 0.30,
and for the support overhang angle is 0.65. The results for
infill ratio and printing speed are not full ranks which means
the right observations to fit the model are not in the data.
5. Discussion
From the three sets of ANOVA, no statistical significances
were shown. We tested the hypothesis that failure rates
decrease if users’ amount of experience increases. From our
observations, it does not seem that experience results in
effective expertise, thus alternative hypotheses are:
Increased affordances in shops can reduce failures.
Dedicated training for operating FDM can reduce failures.
Design for FDM education can reduce failures.
Based on the observations in the makerspaces, users with
less operation experience tended to seek assistance from
trained staff. On the contrary, users with more printing
experiences tended to work independently. The assistance
provided by staff is a type of noise to the measurement. In the
future, the assistance should be measured in order to quantify
its impact to the failure rate.
Although the results do not show any statistical
significance when analyzing the experience and printing
factors influencing failure rates, the results do show that
human behaviors can affect the environmental impacts of
FDM. The fabrication failures caused by human errors
accounted for 26.3% of the total number of prints, which
increased the environmental impacts by around 35%. The
calculation methodology for the environmental impact is
presented in our previous work [5]. Therefore, solutions
should be provided to decrease the failures caused by human
errors. Education, training and assistance provided by
software tools could be solutions [29].
6. Conclusions
This study investigated three types of failure causes of
FDM, which are the designer error, operator error and
machine error. 95 sets of data were collected with a failure
rate of 41.1% observed. The average energy consumption per
failed part was 3.0 MJ. The average mass per part was 28 g.
The printing energy intensity is 107.1 MJ/kg. For the 39 failed
prints, nine were caused by designer errors, sixteen were
caused by operator errors, and fourteen were caused by
machine errors. The detailed failure causes are reported.
The impacts of user’s experience level and printing settings
on fabrication failures are investigated. Parts created by users
with higher level of design experience had lower rates of
failure but were not statistically significant or statistically
>
Ruoyu Song et al. / Procedia CIRP 80 (2019) 494–499 499
6 Author name / Procedia CIRP 00 (2019) 000000
different from the general population’s failure rate. We thus
distinguish between experience and expertise. Students can
gain design expertise through CAD projects, designed parts
and knowledge learnt in class, but must increase their skill
deliberately and with adequate supporting information. For
operators without training, the failure rates did not decrease
with printing experiences. For the printing settings of
Ultimaker 2+ printers, a small layer height, a small support
overhang angle and a print speed at 50 mm/s should be
adopted to reduce failure rates.
ANOVAs were done to test the influence of user’s
experience level and printing parameters to failures rates of
FDM. However, no statistical significances were found from
the results. The types of experience we measured are not
sufficient to explain the failure rates. Therefore, more work
should be done to understand actual variables affecting the
failures rates of FDM and human behaviors. Nevertheless, the
results still demonstrate that accounting for human behaviors
is critical when estimating the environmental impact of a
products.
Acknowledgements
The authors would like to thank the users who participated
in the research and staff who permitted experiments using
their machines and space in Georgia Tech’s Invention Studio.
References
[1] Telenko C, Seepersad C. A comparison of the energy efficiency of
selective laser sintering and injection molding of nylon parts. Rapid
Prototyp J 2012;18:47281.
[2] Huang SH, Liu P, Mokasdar A, Hou L. Additive manufacturing and
its societal impact: a literature review. Int J Adv Manuf Technol
2013;67:1191203.
[3] Barrett TW, Pizzico MC, Levy B, Nagel RL. A Review of
University Maker Spaces. 122nd ASEE Annu. Conf. Expo., Seattle,
WA: 2015.
[4] Song R, Clemon L, Telenko C. Uncertainty and Variability of
Energy and Material Use by Fused Deposition Modeling Printers in
Makerspaces. J Ind Ecol 2018.
[5] Song R, Telenko C. Material and energy loss due to human and
machine error in commercial FDM printers. J Clean Prod
2017;148:895904.
[6] Kapur M. Productive Failure. Cogn Instr 2008;26:379 424.
[7] Kurti RS, Kurti DL, Fleming L. The Philosophy of Educational
Makerspaces Part 1 of Making an Educational Makerspace. Teach
Libr 2014;41:811.
[8] Soep E. Participatory politics: Next-generation tactics to remake
public spheres. MIT Press; 2014.
[9] Xu T, Chen Z, Li J, Yan X. Automatic tool path generation from
structuralized machining process integrated with CAD/CAPP/CAM
system. Int J Adv Manuf Technol 2015;80:1097111.
[10] Wohlers T. Wohlers report 2016. Wohlers Associates, Inc; 2016.
[11] Wohlers T. Wohlers Report 2017. 2017.
[12] Kawamoto K, Koomey J G, Bru ce N, Brown RE, Piette MA, Ting
M, et al. Electricity used by office equipment and network
equipment in the US. Energy 2002;27:25569.
[13] Cerdas F, Juraschek M, Thiede S, Herrmann C. Life Cycle
Assessment of 3D Printed Products in a Distributed Manufacturing
System. J Ind Ecol 2017.
[14] Huang Y, Leu MC, Mazumder J, Donmez A. Additive
Manufacturing: Current State, Future Potential, Gaps and Needs,
and Recommendations. J Manuf Sci Eng 2015;137:014001.
[15] Kim MJ, Maher M Lou. The Impact of Tangible User Interfaces on
Designers Spatial Cognition. HumanComputer Interact
2008;23:10137.
[16] Forest CR, Moore RA, Jariwala AS, Fasse BB, Linsey J, Newstetter
W, et al. Advances in Engineering Education The Invention Studio:
A University Maker Space and Culture. Adv Eng Educ 2014;4:32.
[17] Baumers M, Tuck C, Wildman R, Ashcroft I, Rosamond E, Hague
R. Transparency Built-in: Energy Consumption and Cost
Estimation for Additive Manufacturing Baumers et al. Energy and
Cost Estimation for Additive Manufacturing. J Ind Ecol
2013;17:41831.
[18] Kreiger M, Pearce JM. Environmental life cycle analysis of
distributed three-dimensional printing and conventional
manufacturing of polymer products. ACS Sustain Chem Eng
2013;1:15119.
[19] Cheng W, Fuh JYH, Nee AYC, Wong YS, Loh HT, Miyazawa T.
Optimization of Part- Building Orientation in Stereolithography.
Rapid Prototyp J 1995;1:1223.
[20] Das P, Chandran R, Samant R, Anand S. Optimum Part Build
Orientation in Additive Manufacturing for Minimizing Part Errors
and Support Structures. Procedia Manuf 2015;1:34354.
[21] Karim KF, Hazry D, Zulkifli AH, Ahmed SF, Razlan ZM, Wan K,
et al. Feature Extraction and Optimum Part Deposition Orientation
for FDM. Appl Mech Mater 2015;793:6426.
[22] Song R, Telenko C. Material Waste of Commercial FDM Printers
under Realstic Conditions. Proc. 27th Annu. Int. Solid Free. Fabr.
Symp., Austin, Texas, USA: Laboratory for Freeform Fabrication
and University of Texas at Austin; 2016, p. 121729.
[23] Seepersad CC, Govett T, Kim K, Lundin M, Pinero D. A
Designers Guide for Dimensioning and Tolerancing SLS parts.
23rd Annu. Int. Solid Free. Fabr. Symp., Austin, TX: 2012, p. 921
31.
[24] Becker R, Grzesiak A, Henning A. Rethink assembly design.
Assem Autom 2005;25:2626.
[25] Atzeni E, Iuliano L, Minetola P, Salmi A. Redesign and cost
estimation of rapid manufactured plastic parts. Rapid Prototyp J
2010;16:30817.
[26] Adam GAO, Zimmer D. Design for Additive Manufacturing
Element transitions and aggregated structures. CIRP J Manuf Sci
Technol 2014;7:208.
[27] Lieneke T, Denzer V, Adam GAO, Zi mmer D. Dimensional
Tolerances for Additive Manufacturing: Experimental Investigation
for Fused Deposition Modeling. Procedia CIRP 2016;43:28691.
[28] Booth JW, Alperovich J, Chawla P, Ma J, Reid TN, Ramani K. The
Design for Additive Manufacturing Worksheet. J Mech Des
2017;139.
[29] Song R, Telenko C. Towards Sustainable Additive Manufacturing
in University Makerspaces. Proc. Constr. 2018 Conf., Atlanta,
Georgia, USA: 2018.
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