1
DEVELOP OF A SMART DECISION SUPPORT SYSTEM INTEGRATING
COMPUTATIONAL AIDED ENGINEERING (CAE) AND ARTIFICIAL
INTELLIGENCE (AI) FOR MICRO-INJECTION MOLD DESIGN
Juan P. Guzman1
1Universidad
, M. Chaves2
Catolica De Colombia, jpguzman39@ucatolica.edu.co
Catolica De Colombia, mlchaves@ucatolica.edu.co
2Universidad
Corresponding author: Juan P. Guzman, jpguzman@ucatolica.edu.co
Abstract. Micro-injection molding (µMI) is the most common manufacturing method
for plastic parts with micro-features. Micro-mold design is a high complexity task
that involves viscous flow, geometric variables, rheology, and part particular
characteristics due to its functionality, to design it correctly, it relies on the
expertise of designers, an inappropriate mold design could lead to increase
injection cycle time, material consumption, as well the amount of microplastic parts
with defects. This paper presents a smart decision support system integrating
Computational Aided Engineering (CAE) and artificial intelligence (AI) for microinjection mold design. Using data provided by CAE, from simulation of a standard
part set, integration with AI methods was achieved, developing a specialized
software dedicated to find the best feature configuration for micro-injection mold
design. The system was validated through a process index that relates shear rate,
pressure drop, filling time, and feed system volume, and thanks to the data
provided by the smart system, a validation test runs of the simulations were
executed, accomplishing a reduction up to 50% of the process index.
Keywords: micro-injection, injection-molding, mold design, polymer, artificial
intelligence, CAE
DOI:
1
INTRODUCTION
Micro-injection molding (µMI) is the most extended method for the manufacture of plastic parts
with dimensions, in some of its characteristics, about 100 µm or with high precision requirements,
this dedicated method solve more successfully the peculiarities of µMI, that the common injection
molding does. Has been demonstrated that µMI grant accuracy and precision, for parts with this
requirements [1], also the waste of plastic material is reduced, since the volume for filling system
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is inferior, and, for large production system with replications capabilities, this could mean a great
difference in its rentability.
Plastic parts with micro-features are required for extensive types of industries, in many
application fields, that includes micro-mechanics parts, medical equipment, optical and electronics,
micro sampling cells, micro heat exchangers, micro pumps, wave guides, optical elements, etc.,
according to [2], also there are examples really familiar, as the pin connector for the mobile phone
[3]. Nowadays this demand for micro-parts is increasing and extend to other applications like
optics, microfluidics and biochips, therefore a lot of effort has been made to reduce the negative
impact; about 380 million tons of plastics are produced annually worldwide, and just <20% is
typically recycled [4], this work aim to a more sustainable plastic consumption.
The process to make a micro-part by µMI depend on mold features, like type and size of
runners, gate and sprue, mold material, design it properly, require a coarse knowledge of heat
transfer, rheology, and polymer properties as well as expertise acquire through diverse injection
scenarios; also, some phenomena that are considered negligible at regular part size, takes major
importance when the channel dimension are smaller, like the superficial roughness [5] specially
with dimensions near to 50 µm , to overcome the challenge that this complex process put forward,
and to take it into new efficiency level, this research offers a novel smart system, that links
engineering techniques like FEM and FVM analysis to AI developments.
Cost of every part made by standard injection molding depend on, material, processing and
mold machining, the percentage of mold cost into the total plastic part, could go from 17% up to
40%, depends on how complex the part is and what mold type have been chosen [6], this
proportion could be extend to µMI with some reasonable error, therefore, develop a smart system
that allows reduce mold cost will have a big repercussion in final part cost, this potential increase
the chances for take this research to industry.
This research is articulated with a major research project call it “Design of smart system for
micro-injection of plastic”, in the research line of smart software and technological convergence
from the Universidad Catolica de Colombia, this research project has as main objective, design of
integrated smart system for the µMI, including both aspects, first the job previously done for
process parameters [7] and hence micro-mold is liable of many aspect for the process success and
quality part, develop of smart system for the micro-mold design is a crucial aspect to
accomplishment the mayor research project’s main objective.
2
DESIGN OF STANDARD DATA SET
A set of standard parts worked as a base for the entire project, each part has a specific micro
feature, for typical plastic parts. Specialized software, (Solidworks), was used for the 3D modeling
and simulation runs.
2.1
Wall thickness
Best design practice is an uniform and symmetric wall thickness [8], but this is not always possible,
due to a functionality requirement or architecture one, it could be inevitable to design a part
without a wall thickness change. For changes in the cross-sectional area, a smooth transition is
the better choice, also in the case of a change direction, where is required to eliminate or reduce
the impact of stress concentrator, changes with a sharp angle will lead to stress increase and
possible failure. To understand this design challenges for micro plastic parts, and its effects on
mold design, two parts were developed, first standard part with curves surfaces and wall
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transitions, part name: micro-curves and the second one, where the wall thickness go down up to
0,05 mm part name: micro thin wall, figure 1 and 2 respectively.
8 mm
Figure 1: Micro-curves standard part.
8 mm
Figure 2: Micro-thin wall standard part.
2.2
Ribs
In favor of increase the strength of stiffness in a plastic part, an appropriate design of structural
ribs could be done without increase the wall thickness, certain general rules are applicable as a rib
base between 40% - 50% of the base plate [8], a standard part named as micro-ribs, with the
same thickness of the base plate with ribs at different distances between each other, was made,
this due to the limitations of the machining process commonly available, since the smallest
diameter for machining mill is 0.5 mm. Other standard part named as micro-grating, change
distances between longitudinal openings in the same plane of the base plate, going from openings
of 0,5 mm up to the smallest one of 0.05 mm, figure 3, shows micro-midrib standard part design
and figure 4 the one for micro-grid.
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8 mm
Figure 3: Micro-midrib standard part.
8 mm
Figure 4: Micro-grid standard part.
2.3
Bosses
There are several applications where a mechanic assembly between two or more parts are
required, this union could be made through screws that are placed in special cylinders attached to
the part wall, if the wall is too thin, it will fail to the action of the force or torque applied in the join,
but in the opposite case, if the wall is too thick, sink marks could appear after the part is injected,
so, to avoid those problem this cylinder need to be attached to structural reinforcement and have
the appropriate inner diameter for the standard screw and outside diameter, to provide enough
straight to the joint. To be able to reproduce at micro scale this particular, but common geometry,
a standard part was design, name it as micro mounting boss. For the mold, this will be a change in
the cross-sectional area of cavity, figure 5, shows the design of this micro standard part.
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8 mm
Figure 5: Micro-mounting boss standard part.
2.4
Cavities
Parts with cavities like lids and boxes are often required in several applications, like, closures
between lid and body of a container, small joint elements, and so on, to avoid defects like
warpage and increase dimensional control, is critical use curvature in the inner corners, implement
the draft angle at both side of walls, in this way the lateral wall will be slimer at the end, for this
purpose a part named micro closure was designed, figure 6 shows its design.
8 mm
Figure 6: Micro-closure standard part.
2.5
Transmission elements
Gears are used to transmit power or angular motion, in any case, the complex gear teeth geometry
is key to a successful transmission, high precision is required avoid excess bending loads, vibration,
and excessive friction. Some rules apply for metal and plastic gears as well, elimination of
undercuts will increase tooth strength [8], but the thermal expansion is more crucial in plastic
gears and specially in those with micro-dimensions.
Additionally, to mechanical performance a good gear design should consider injection
characteristics, like gate type and location, uniform thickness on the walls, if the rim wall is too
thin, it could induce an inefficient filling, a hub wall to thickness could lead to void and then
warpage avoiding the polymer get packed in that area. A pin point gate located in the hub will
distribute the melt flow in a better way that one gate located at the edge of a gear tooth, but the
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pin point gate will required a three plate mold, in order to be pragmatical, a segment of spur rack
gear was placed in a plate of 8 mm x 8 mm, to evaluate the capability to duplicate the tooth
geometry, this will lead probably, to reduce mechanical resistance in the orthogonal direction of
the flow, but will help to a better understanding of replication capabilities of micro injection for
micro-gears.
To manufacture micro-gears, there are available a set of conventional and more novel process,
each of them has their own advantages and disadvantages, as micro-milling can be slow and leave
tool marks, micro-metal injection process has higher cost and necessitates sintering after metal
injection process [9], here was used µMI to replicate the geometry of a DIN gear through standard
part name it micro-gear figure 7, with a uniform thickness and that away keep the simple two
plate mold.
8 mm
Figure 7: Micro-gear standard part.
2.6
Circular channels
Radial and circular features are typical in several applications, as for ventilation and filters, this
geometry represents a challenge for the uniform filling, is common to have welding lines
undesirable areas where it is required a high strength performance, due to the micro dimensions
the shear rate is high, but also the area available for transfer heat is increases, that could lead to
an inefficient part filling, to evaluate this behavior a standard part named micro vent was
designed, its design is showed in figure 8.
8 mm
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Figure 8: Micro-vent standard part.
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RECOLECTION OF DATA THROUGH CAE SIMULATION
Data base was built through data give by the FEM and FVM simulation, the steps to make the study
are:
•
•
•
•
•
•
Selection of the features for the study, for the propose of this research, is
mandatory include design of feed system and cooling system, as well the virtual
mold generation
From a previous draw sketch, and entering diameter info for each component
(gate, runner, sprue, and cooling channel if apply), feed system and cooling
channels are defined
Set the dimension of the mold in each direction, X, Y, Z.
Check the domains to mesh, and proceed with a previous of mold, cavity, feed
system and cooling channel mesh, figure 9 shows an image of mold + part + feed
system + cooling channels mesh
A hexagonal mesh was selected for all iterations, due to be the more effective to fill
without issues all the micro plastic part.
After defining mesh parameters, mold material, injection point, vent location
among other mold related parameters were defined and then execute simulation.
Additional data like process parameters are required, but in this case, in all
iterations this data was always placed by default and always the same, just
modifying the mold related parameters.
Figure 9: Micro part configuration for simulation.
Total of 124 simulations were made, to provide the necessary data for the applications of IA
methods, figure 10 shows an example of this simulation with the results of filling time for one of
the parts from the standard set.
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Figure 10: Filling time result from FVM simulation.
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SELECTION AND APLICATION OF AI METHODS
First task was fit micro part among the 8 standard parts, how much the micro part has in common
with each of the categories; maybe the predominant feature of part are micro ribs, or maybe
circular channels, therefore, it is required applied techniques of image recognition, which should
need to have high accuracy, and provided information for similarity of all the categories. In any
case, it is necessary a data base with all the images of the standard parts, different views of the
parts, like top, bottom, isometric and detail views were extracted from the 3D file, and built up a
data base, storing the images inside files with the names of categories, a total of 176 images make
up this data base.
Using Alexnet convolutional network, a type of pre-trained neural network usually known as
transfer learning, since can use the features defined from huge images data set, and use it in a
new task, with a smaller set of images, that worked perfectly for this specific application. Some
requirements and steps are necessary before running the convolutional network, images must be
for a specific size, 227 by – 227 by – 3, an image data store was used to get the images from
specify folder and their labels, next split the data between train and validation, for this case 70%
of the images were used for training and the remain 30% for validation.
Figures 11 and 12 shows the results, where the validation set goes up to 87,5%, the loss is
reduced significantly in each epoch. This tendency of improvement can be also observed in the
training data, given an overal adequate performance.
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Figure 11: Progress of Alexnet deployment.
Figure 12: Results using Alexnet.
After categorizing the part between the 8 categories, more geometric information was needed,
was essential got information like volume, superficial area, distances, and any other information
that helped the smart system to get a better understanding of the micro part. Thanks to the
information stored in 3D files, it was possible to use different techniques to obtain this information,
with the creation of a PDE model, that is usually used to make simple analysis, therefore, it has
geometry and mesh information, and just loading a .STL file of the micro part, a mesh will be
created and 3D model will display, allowing initial verification of the model loaded correspond to
the images from the previous step, also, a volume will be calculated from the PDE model.
Now, other command and method was applied to obtain the superficial area, with the option of
read a .STL file, and a mesh surface, that divide the 3D model in triangles, using a loop function
was possible to find the total superficial area, this is possible thanks to the fv.Point and
fv.Connectivity list that is produced, also, this code will allow to calculate the ratio between volume
and superficial area, crucial information to understand the complexity of the part been studying,
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this is also stored it in the work space of the software, to be used in posterior calculations and by a
neural network, this information also will be display in the user interface, so the user will be able
to confirm the information provided.
Next step, was taking advantage of the matrices with information stored in the software, that
could be seen as a cloud of points, thanks to the command stlread, returns a triangulation object,
using the fvpoints matrix to get the vector for x,y and z points, is possible to calculate minimum
and maximum distance in each direction, store it and show it in the user interface, this data will be
part of the input data base for a neural network, as well, this data works to fitting a surface over
the 3D points, through polynomial regression, this surface is unique for each micro part and the
equation will have a constant and eight indices that describe the 3D part, this info will be also used
as part of the input data base. Image 13 shows the result.
Figure 13: Fitting surface to a point data cloud from 3D model.
Finally, to complete the information that is going to be in as input information for the
upcoming neural network, a manual information will be required to put by the user.
Having collected all the 33 defined input data, it is time to deploy a neural network capable of
understand the underlain connections between input and output data, thusly estimate mold
parameters for a generic part. The data was divided in training 70%, validation 15%, and testing
15%. After several iterations, the best results regarding correlation coefficient R, that shows how
strong is the relation between outputs and inputs, and error histogram, a network training using
Bayesian Regularization showed the best result, in figure 14 correspond to the architecture of the
network, figure 15 shows the error histogram, where is possible to appreciate an error around 0
and figure 16 shows the correlation in all data sets is greater than 80%, exhibit a strong
correlation among the chosen variables.
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Figure 14: ANN architecture.
Figure 15: Error histogram.
Figure 16: Correlation for data set.
User interface was divided in four groups of output data, first the system will display the feed
system information, in second place the mold dimensions and material, a third one will show the
data related with the options for cooling system and finally the fourth one, information relative to
the injection point and vent quantity and location.
For feed system, as the most critical of all results, was improve trough an optimization
process, applying an multi-objective optimization algorithm know it as patternsearch, this method
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find local minimum of the objective function, Equation (4.1), with starting point the a vector with
the information provided by the neural network, and delimited by the constrain function, in this
particular case, the goal will be minimize the material consumption in the feed system, according
with this research guidelines and looking for make the micro injection process more
environmentally conscious, making the constrain function pressure drop in the feed system,
Equation (4.2), this pressure could not exceed the 50% of the maximum pressure give it by the
injection machine, for the propose of this study, a machine with a maximum of 100 MPa was
selected, so the top drop pressure in the feed system was stablished at 50 MPa.
(4.1)
Where:
(4.2)
Cause final dimension now are known, Ra value for the portions of the feed system and mold
cavity can be proposed, based on the research made by Bellantone, Vincenzo [5], higher Ra values
actually helps to the molt flow to reach all mold cavity, due to several different phenomena, that
becomes critical at micro scale, first though could lead to think that more heat exchange can occur
due to major heat transfer area, but at contrary, the roughness keep small amount of air that
avoid excessive heat transfer, also the small dimension, allow the increase of shear rate, therefore
the melt velocity, also the wall slip become more important at this scale.
To summarize the results, the last window will provide the option of export the feed system
optimized information to a .xlsx file, so the user will have an easy way to get the access this info
and stored it.
5
SMART SYSTEM VALIDATION
Reduction in time cycle implies cost diminish, increase in productivity, the time that takes to the
melt flow going through the feed system then becomes crucial, a small change in the time per part,
when the batch production could be hundreds of thousands, and the financial impact could be the
major difference for the business success.
The wasted polymer in an oversize feed system is also a source of over cost in the
microinjection process, be able to define its geometry and dimension in such a way that
accomplishes diminish the volume of material, while keeping the pressure drop and shear rate in
adequate values. Due to small diameters could produce high pressure drops therefore incomplete
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filling and short shots will appear, at the same time, huge shear rates leading to material
degradation, jetting into melt mold cavity, splay, and other visual defects.
For the previous reasons, the validation will investigate these main aspects, how much time is
the filling taking, feed system volume, pressure drop, and shear rate. A ratio between feed system
volume and part volume is proposed herein Equation (5.1), due to the particularities of this
process, the volume in the feed system for a single cavity is particularly high compared with a
standard process, so, for micro injection process is even more critical achieve a minimum material
consumption. It is also a major concern from the environmental point of view, plastic is part of our
everyday living, but it is mandatory to make our process more efficient and reduce the impact on
our planet.
(5.1)
For an overall validation and analysis, figure 17 shows the comparison between, the volume
rate from the database and a set of simulations that was run with the eight standard micro parts
and three parts from the previous process parameters investigation, the simulation was set with
the mold parameters given by the smart system for micro mold injection process, for ABS and
PP+PE polymer.
A total of twenty simulations were carried on, providing data for the validation process, this
data allowed the comparison with the previous data recollected by iterations on the simulations,
the first set of simulations was carried on and just the ones that were successfully filled were
stored.
Figure 17: Filling time comparison.
SOURCE
DATA BASE
SMART SYSTEM ABS
SMART SYSTEM PP+PE
SMART SYSTEM AVERAGE
PERCENTAJE
FILLING TIME [s]
0,95
0,41
0,44
0,42
45%
Table 1: Overall reduction of filling time.
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A reduction of the average value for the filling time of 55% is evident in table 1, showing the
huge impact in the values that the smart system is providing, and from figure 17, is also evident
how the distribution of the general times is reduced significantly for all the micro parts with both
materials, this will lead to an improved microinjection process. From the material consumption
point of view, figure 18 shows how the relation between the volume of the feed system vs part
volume behaves with the parameters given by the smart system, and table 2 also shows the
average values for the database and the validation test simulations, in this case, there is no need
to separate data from ABS with PE+PP material.
Figure 18: Filling time comparison.
SOURCE
DATA BASE
SMART SYSTEM
RATE VOL SYS/VOL
PART
1,91
1,06
55%
Table 2: Overall reduction of rate between feed system volume and part volume.
To investigate the pressure drop and shear rate, since the simulation only will provide the
required pressure at the final of the injection process, here was used again, the power-law model,
equation 4.2, to estimate the amount of pressure required for the melt flow to go through the feed
system. Figure 19 shows the average behavior obtained from the database and from the validation
test simulations, also table 3 shows the average drop pressure values.
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Figure 19: Pressure drop comparison.
SOURCE
DATA BASE ABS
SMART SYSTEM ABS
PERCENTAJE CHANGE ABS
DATA BASE PP + PE
SMART SYSTEM PP+PE
PERCENTAJE CHANGE PP+PE
AVERAGE DATA BASE ALL
SMART SYSTEM AVERAGE ALL
CHANGE AVERAGE ALL
PRESSURE
[MPa]
DROP
6,28
7,61
121%
2,11
2,57
122%
4,34
5,09
117%
Table 3: Pressure drop.
Pressure drops for both data set, look similar and without a major change, even the database
shows lower pressure drop values, this is, of course, coherent, cause the melt flow is going
through smaller spaces, but accomplish to keep reasonable low pressure drops, even for the out
layers is under 20 MPa, according to with the multi-objective optimization, was set to a maximum
of 50 MPa. From an average perspective, the pressure drop was increased by 17% from the results
provided by the smart system vs the database, this is a reasonable minor increase, keeping the
micro injection process under standard and viable parameters.
Shear rate information was recollected as well, excessive values could lead to major problems
and defects in the microplastic part. Shear rate is increasing every time the cross-section area or
any of its dimensions is too small, for microinjection process this is critical due to the nature of the
process itself, for almost every micro part, the biggest shear rate is located at the gate of the feed
system, so the relationship with the length and the diameter changes from the runner and sprue
need to be carefully designed.
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Figure 20 shows the shear rate values for both data base, and table 4 the average values for
both materials individually and its join behavior, in this case a differential behavior will be also
observed between both materials.
Figure 20: Shear rate comparison.
SOURCE
DATA BASE ABS
SMART SYSTEM ABS
PERCENTAJE CHANGE ABS
DATA BASE PP+PE
SMART SYSTEM PP+PE
PERCENTAJE CHANGE PP+PE
AVERGAE DATA BASE ALL
SMART SYSTEM AVERAGE ALL
CHANGE AVERAGE ALL
SHEAR RATE [1/s]
5881,43
4283,75
73%
5258,57
6478,45
110%
5591,48
5381,10
91%
Table 4: Pressure drop.
At high shear rates the viscosity decrease, so this leads to increase velocity helping to
overcome the usual issue of high area-volume ratio for micro parts, but on the other hand, if the
shear is too high the problems previously mentioned will occur, so maintain a certain balance in its
value is crucial, this value must always be under the maximum permissible. For ABS the maximum
shear rate is 50000 1/s and for PP+PE 100000 1/s.
To finish the analisys of validation test, an index of general process was proposed, considering
the major variables, as follow:
(5.2)
Equation 5.2, defines and index for shear rate, making a comparison between the shear rate
obtained from the simulation, and the max permissible shear rate of each material
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(5.3)
Equation 5.3, defines an index for pressure drop, using the pressure drop through power-law
method, equation 4.2, and the maximum drop pressure for defined previously, 50 MPa.
The process index (PI) then was defined as follows, equation 5.4, multiplied by the shear
index, pressure drop index, the ratio between feed volume system and volume part, equation 5.1,
and finally the filling time (Ft).
(5.4)
Filling process, therefore, mold design will be less efficient at higher values of PI, cause an
increase on any of the variables, will imply a poor design. Has been seen, higher filling times,
pressure drops, filling times, or material consumption, are symptoms of a bad definition for the
mold feed system.
Figure 21: Process index comparison.
PROCESS INDEX
DATA BASE
0,0054
SMART SYSTEM
0,0027
CHANGE
50%
Table 5: Process index.
As figure 21 and table 5, shows a great improvement in process behavior, thanks to the
solution proposed here, a reduction of overall 50% is accomplished, this means a great reduction
in time, material, and energy consumption.
6
DISCUSSION AND FUTURE WORK
These results are evidence of the novelty grade of the current project, given that improvement of
µMI process index, which implies the reduction of cost for the final user, like, patients with
procedures that require bio micro parts, electronic devices, computational industry, research on the
field like microfluidics and so on, also a significant reduction in the plastic scrap production,
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diminishing environmental negative impact from the use of plastic. But, more significantly the
reduction of micro molds designers time consumption, they will be able to define a functional and
efficient mold design in less time, with the certain, that the mold proposed has and improve the
use of material and will require a minimum of filling time, avoiding excessive pressure drop with a
lower probability of parts defects due to high
Future works include the machining process of the mold; a micro mold design with this proposal
was made, machining process could be simulated through CAM tools, and process parameters
could be defined and then link it to IA methods to find the optimal parameters. Using the advanced
techniques on micromachining, (Dr. Liz Katherine Rincon Ardila et al., 2015), an optimal parameter
could be found, mixing with CAM simulation, a support system could be added to this research.
7
CONCLUSION
•
A smart system that allows a 50% reduction in the process index was developed, through a
combination of CAE simulation and AI techniques
•
• This development has the potential of cost reduction, diminishing the time required for an
efficient mold design, improving the performance of mold manufacture.
•
• Due to the significant reduction in filling time, up to 55%, the total cycle time of each micro
part will be also reduced, making the process more competitive.
•
• A feed system with minimum dimension is accomplished, thanks to, the multi-objective
optimization method applied here, thus, a reduction of 45% in plastic consumption is also
accomplished, diminishing the negative environmental impact of plastic consumption.
•
• Values of pressure drop were maintained in standard ranges, so the smart system does not
increase the cost of more potent machine requirements.
•
• Probability of part defects due to high shear rates was avoided by the smart system,
keeping the values under the maximum permissible for each material, avoiding batch of parts
rejected by low quality.
•
• This smart system has the potential of bringing beneficent to mold manufacturers, the
microplastic part industry, and the final user through the potential cost reduction.
8
ACKNOWLEDGMENT
The authors would like to thank the direction of master’s degree of Universidad Catolica de
Colombia for provided the necessary computational tools to develop this research.
9
REFERENCES
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[2]
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