ME 338 Term Paper
Robotic additive Manufacturing
Aryan Kumar
210100030
1. Introduction
Robotic additive manufacturing is a process of creating
an object by adding material layer by layer using
different materials, such as plastics, metals, and
ceramics. The inspiration for RAM procedures comes
from Chuck Hull's invention of the novel technique
stereolithography in the 1980s. He developed this
technique after getting frustrated with the lengthy
production periods of prototyping, which uses UV
lasers to build 3D objects, layer by layer. Now it is
referred to as “3D printing”. This process was first
employed to create conceptual models. It is the main
aspect of discussion of design concepts, and for
applications involving the process, or for the creation
of architectural & anatomical models.
1.1. Why?
This process is defined for the production of any
object in any shape without the need of tools. In this
process, simple and cost-effective construction of
complex objects in a single step can take place.
Because these procedures require fewer tools and
equipment, inventory costs, manufacturing time,
material usefulness, and production waste are all less
as compared to subtractive manufacturing
AM technologies offer us benefits like flexibility of
design, fast time for market, environmentally friendly
manufacturing practices, and industry-driven assets.
AM offers more advantages in terms of money because
it doesn't rely on complex geometries to produce
goods.
from the material deposition system transforms a
digital model into a physical component through a
series of coordinated and controlled movements. An
example of AM in the aerospace industry is GE
Aviation's fuel nozzle tip for the ‘Leading-Edge
Aviation Propulsion’ engine. Reduction of 25% in
mass of fuel nozzle is seen when Am processes are
used, this is considered a breakthrough in metal
processing.
As the industrial robot energy consumption rises, it is
becoming very important to consider energy efficiency.
Also, when making part placement decisions for
sustainable production.Research on energy efficiency
in industrial robots for conventional manufacturing has
been conducted. for example, for this, a shortest path
and energy consumption-based particle swarm
optimization technique for spot welding path
optimization was proposed. Also, a review paper
examines energy-saving techniques for robotic systems
from both the hardware and software areas. A robot's
energy efficiency is a major factor in determining how
useful it is for industry purposes.
1.2. Developments in Additive manufacturing
Robotic additive manufacturing has become
increasingly popular in recent years, and is now used
in a wide range of industries. In the healthcare sector,
AM is being used to improve the quality & reduce the
cost of patient-centric and regenerative medicine,
cardiology, orthopedics, and implants. AM works and
an object is created from a digital model by using
computer-aided design (CAD) software. A heat source
fuel nozzle tip produced by AM (GE aviation, 2018)
2. Methodology
2.1 Robotic Setup
CAD
packages
are realistic simulation
environments that are now being used to design
robotic cells. This is desirable for rapid and
dependable development, configuration, and
coding of robotic cells. Additionally, it is
necessary to develop and execute software for the
remote monitoring and control of cell capabilities.
A simple access authorization mechanism (list of
allowed IPs and password) is used for robocalls.
The
robot
informs
the
authorized
users/programmers by its command. The
user/programmer can obtain the required contour
paths from the information in the 3D workpiece
file. All the contours are graphical objects, such as
lines, arcs, and points, from which the entire path
can be identified, just as a CAD engineer uses
these commands to design the workpiece.
2.2 Path Generation
Designing or creating the path of a tool is a
crucial step in additive manufacturing. The path
that is generated must be collision-free, accurate,
and time-efficient. The design of the tool path is
divided into three categories: offsetting the
reference surface, slicing the non-planar layers,
and generating tool paths for the non-planar layer
Researchers developed a new way to plan the path
of a tool used to create multi-material assemblies.
This toolpath avoids collisions and only requires
the tool to fill the assembly.
To make products faster using laser-based direct
metal deposition, researchers used a robot with 8
arms. This robot has 6 regular arms and 2 extra
arms that can tilt. The extra arms can tilt the laser
head relative to the part being made, which is held
in place by a positioning system.
A 6 DOF reconfigurable robot platform
2.3 Energy Consumption
We can split Robotic AM energy into ‘AM
energy’ and ‘manipulator energy’. Energy needed
for printing, including printing energy, preheating,
and other misc. is called AM energy. Manipulator
energy is used to move the manipulator along the
trajectory.
We want to build an energy consumption model
using losses like frictional, mechanical and
electrical losses (wind, core, stator and others).
Moreover, using robot kinematics and dynamics
to efficiently use energy is better than using losses
in controlling applications. We can say that, the
energy consumption models based on robot
kinematics and dynamics are generally explored.
The amount of energy (E) used to travel a certain
distance (dλN) during a certain time period (λth
time interval) is calculated by dividing the
distance by the number of time periods (N)
3. Robotic Additive Manufacturing Processes
In 2015, the International Organization for
Standardization (ISO) and American Society for
Testing Materials (ASTM International) worked
together to make a common list of words to
describe the basics of different additive
manufacturing processes. They divided additive
manufacturing into seven categories:
3.1 Binder Jetting
Binder jetting uses an ink-jet printing head to drop
liquid bonding agent onto a powder material, layer
by layer, to join specific areas without heat. It has
a
robot
with
3
arms.
through a small opening. It is also known as
FDM, fused filament fabrication(FFF), and FLM..
Setup axes range from three to five.
3.5 Powder Bed Fusion
Powder bed fusion is a type of 3D printing that
uses heat to melt specific parts of a powder bed.
Examples include electron beam melting (EBM),
selective laser sintering (SLS), and direct metal
laser sintering (DMLS). Robotic systems use
mirrors to move the heat source across the powder
bed
in
a
planned
pattern
Binder jetting technique
3.2 Material jetting
Material jetting is a 3D printing process where
tiny drops of material are deposited to form a
partly-completed object, which is then hardened.
It has a three axes robotic setup.
3.3 Directed Energy Deposition
This process uses a focused heat source to melt
powder or wire feedstock and join it to a build
surface.Examples of this process include using
lasers or an electron beam to melt metal and
deposit it layer by layer to build a part. Robots
with three to seven axes move the laser or electron
beam to create the part. The robotic axes range
from three to seven.
Illustration of Directed energy deposition
3.4 Material Extrusion
This is a process where a material is released
Powder bed fusion
3.6 Sheet Lamination
Sheet lamination is an AM process where thin
layers of material are glued together to form an
object. One example of this technology is
laminated object manufacturing.
3.7 Vat photopolymerization
Vat photopolymerization (VP) is a type of additive
manufacturing that uses light to harden a liquid
resin
in
a
vat.
Examples
of
vat
photopolymerization include stereolithography
and digital light processing
4. Machine Learning in Robotic AM
Finding Input - Output relationship in AM of
different processes is very important especially for
Wire arc robotic AM. For this, many techniques
have been developed.Recently, researchers have
started using machine learning (ML) and deep
learning (DL) to predict the results of WAAM
experiments. This is because the data from these
experiments is very non-linear.
Deep learning (DL) uses artificial neural networks
(ANNs) to make predictions. ANNs are often
paired with optimization algorithms to improve
their accuracy.
Gradient descent-based backpropagation (BP) is
the most common optimization algorithm used
with ANNs. It works well, but it can get stuck in
local minimums.
Researchers have tried using other optimization
algorithms, such as genetic algorithms (GAs) and
particle swarm optimization (PSO), with ANNs.
These algorithms work well, but they are slow and
require a lot of computing power.
Many other optimization algorithms have been
developed. One of these algorithms is the gray
wolf optimization (GWO) algorithm. GWO has
been shown to outperform GAs and PSOs in many
cases
.
4.1 Experiment design
Regression analysis is done to find a relation
between input and output parameters. To train the
model, we used 1,296 different sets of input
values and a nonlinear second order regression
equation to create an approximate relationship
between the input and output factors.
y is the output of a process, and X is a setting that
controls the process. b is a number that shows
how much y changes when X changes by one
unit. e is the error in measuring y. j and i are two
different settings that control the process. Xj is the
linear effect of setting j, Xj^2 is the nonlinear
effect of setting j, and XiXj is the interaction
between settings i and j.
4.2 Neural Network Architecture
The skills and abilities of the human brain are a
result of the billions of interconnected neurons
that make up the brain. These mathematical
models based on neurological systems are called
Artificial Neural Networks (ANNs). They have
been widely used for classification issues,
predictive jobs, optimisation, and many other
activities in a variety of disciplines. Information is
fed into the input layer of an artificial neural
network. The information then travels to a hidden
layer of interconnected neurons, where it is
weighted and biased. From there, we get the final
output.
To train two artificial neural networks (ANNs), we
fed the inputs (WFS and TS) to the forward
mapping model and got the outputs (BW and BH).
We used the opposite parameters for the back
mapping model. We started with random values
for the weights and biases of the ANN model,
between -1 and 1. We used a special function
called ReLU in the input and hidden layers of the
model. We trained the model on a dataset
generated from the experimental data, with the
goal of minimizing the error. We then used two
different algorithms, gradient descent with
momentum and the Grey Wolf optimization
algorithm, to compare their performances.
4.3 The gradient descent with momentum
Gradient descent is a way to find the best solution to a
problem by following a path of decreasing error. The
speed of this path is controlled by a setting called the
learning rate.
Momentum is a method added to the algorithm to
ensure that it searches in the right direction without
getting lost in the search space while looking for the
best solution.
The Grey Wolf algorithm is a way to find the best
solution to a problem by mimicking the social
hierarchy and hunting behavior of gray wolves. The
hierarchy is made up of four types of wolves: alpha,
beta, delta, and omega. The alpha wolf is the best
solution, the beta and delta wolves are the second and
third best solutions, and the omega wolves are the rest
of the solutions. The algorithm works by imitating how
gray wolves find, circle around, and attack their prey
5. Results and Conclusions
Robotic additive manufacturing (RAM) is a new way
to 3D print things using robots. It is still being
developed, but it has the potential to revolutionize the
way we build things in the future. RAM can be used to
print things much faster and more accurately than
traditional 3D printing methods. It can also be used to
print things in more complex shapes and sizes.
One of the challenges of RAM is creating toolpaths
(the paths that the robot follows when printing) that
are efficient and collision-free. Researchers are
developing new toolpath strategies to address this
challenge.
RAM is also being used in hybrid manufacturing,
where it is combined with other manufacturing
processes, such as machining and welding. This allows
manufacturers to create complex products that would
not be possible with any one process alone
.
Energy and quality metrics are formulated and a
systematic methodology based on single-objective
optimization and energy-quality map is introduced. It
is used to identify the optimal part placement within
the robot’s reach. As industrial robots become more
capable and flexible, researchers are increasingly
interested in using them for ground and aerial mobility
in manufacturing
Moreover, Additive manufacturing systems can help
products get to market faster and be more customized.
However, current AM processes have limitations, such
as small product size, slow build rates, and the need
for support structures for areas with overhangs. This is
driving researchers to develop new AM methods. Two
major drivers of change are the three-axis
layer-by-layer manufacturing process of conventional
AM systems and their limited work envelope.
We used two machine learning models to map forward
and backward, with three hidden layers of neurons in
each model. We trained the models using gradient
descent with momentum and GWO algorithms. For
forward mapping, the ANN model trained with
gradient descent with momentum performed the best,
based on its MAPE score on the test dataset. GWO
converges much faster than gradient descent. For
backward mapping, we found the best number of
iterations and hyperparameters by running the model
multiple times until we got the desired results.
8. References
1. J. Norberto Pires, Amin S. Azar, Filipe
Nogueira, Carlos Ye Zhu and Ricardo Branco,
The role of robotics in additive manufacturing:
review of the AM processes and introduction
of an intelligent system, Volume 49 · Number
2, 2022, 311–331
2. Suyog Ghungrad, Abdullah Mohammed,
Azadeh Haghighi. Energy-efficient and
quality-aware part placement in robotic
additive
manufacturing.
Journal
of
Manufacturing Systems 68 (2023) 644–650.
3. J. Norberto Pires, Amin S. Azar. Advances in
robotics for additive/hybrid manufacturing:
robot control, speech interface and path
planning. Industrial Robot: An International
Journal Volume 45 · Number 3, 2018 ,
311–327.
4. Damir Godec · Joamin Gonzalez-Gutierrez ·
Axel Nordin · Eujin Pei · Julia Ureña Alcázar
Editors. A Guide to Additive Manufacturing.
2022.