Quad-Copter Weight Optimization using Generative Design

Abstract

For a quadcopter design main criteria is the weight of the frame. The conventional cad design frame weighs around 130g for ABS material, so using a generative design process I created a new design that weighs only 67g for ABS material.

Full text

DAYANANDA SAGAR COLLEGE OF ENGINEERING
Accredited by National Assessment & Accreditation Council (NAAC) with ’A’ Grade (AICTE
Approved, an Autonomous Institute Affiliated to VTU, Belagavi) Shavige
Malleshwara Hills, Kumaraswamy Layout, Bengaluru-560078
DEPARTMENT OF AUTOMOBILE ENGINEERING
(Accredited by NBA)
Mini Project Report on
“ Quadcopter Frame Optimization ”
Submitted in partial fulfilment for the award of degree of
BACHELOR OF ENGINEERING
in
AUTOMOBILE ENGINEERING
Submitted by
1DS17AU037
PUNEETH NAGARAJU O
Under the guidance of
Dr./Mr. Nandakumar MB
Asst/Asso. Professor Department
of Automobile Engineering, Dayananda
Sagar College of Engineering
Shavige Malleshwara Hills, KS Layout, Bengaluru-560078
2020-21

DAYANANDA SAGAR COLLEGE OF ENGINEERING
Accredited by National Assessment & Accreditation Council (NAAC) with ’A’ Grade
(AICTE Approved, an Autonomous Institute Affiliated to VTU, Belagavi) Shavige
Malleshwara Hills, Kumaraswamy Layout, Bengaluru-560078
DEPARTMENT OF AUTOMOBILE ENGINEERING
(Accredited by NBA)
Certificate
Certified that the project report entitled ‘Generative Design and Analysis of Quad-copter
Frame ’ is a Bonafede work carried out by Mr. Puneeth Nagaraju O bearing USN: 1DS17AU037
under the guidance of Mr. Nandakumar MB, Professor, Department of Automobile,
Dayananda Sagar College of Engineering, Bengaluru, in partial fulfilment for the award of
Bachelor of Engineering in Automobile Engineering of the Visvesvaraya Technological
University, Belagavi.
Dr./Mr. Nandakumar MB Dr. H.N. Suresh
Asst/Asso. Professor, Professor & Head,
Dept. of Automobile Engineering Dept. of Automobile Engineering
Dayananda Sagar College of Engineering. Dayananda Sagar College of Engineering
Bengaluru Bengaluru
Dr. C.P.S Prakash
Principal
Dayananda Sagar College of Engineering.
Bengaluru

ABSTRACT
A generative CAD based design exploration method is proposed. It is suitable for complex
multi-criteria design problems where important performance criteria are incomputable. The
method is based on building a genotype of the design within a history based parametric CAD
system and then, varying its parameters randomly within pre-defined limits to generate a set of
distinctive designs. The generated designs are then filtered through various constraint envelopes
representing geometric viability, manufacturability, cost and other performance related
constraints, thus reducing the vast design space into a smaller viable design space represented
by a set of distinctive designs. These designs may then be further developed by the designer. The
proposed generative design method makes minimal imposition on the designer’s work process
and maintains both flexibility and fluidity that is required for creative design exploration. Its
ability to work seamlessly with current CAD based design practices from early conceptual to
detailed design is demonstrated. The design philosophy behind this generative method and the
key steps involved in its implementation are presented with examples.

CONTENTS
1. INTRODUCTION
1.1. Quad-Copter Frame types
2. LITERATURE SURVEY
3. 1ST ITERATION – CONVENTIONAL DESIGN
3.1. Designed using Fusion 360 CAD
3.2. Stimulation Analysis Report of 1St Iteration
3.3. Weight Analysis of the 1st Iteration
4. 2ND ITERATION – GENERATIVE DESIGN
4.1. Generative design Procedure in fusion 360
4.2. Generative Design Output Static Analysis details
4.3. Mass Analysis of 2nd Iteration
5. METHODOLOGY
5.1. 3D Printing
5.2. Generative Design
6. CONCLUSION
7. REFERENCES

LIST OF FIGURES
➢ Figure: - 01 to 09 Types of Quad-Copter
➢ Figure: - 10 Render image of the 1st Iteration Model
➢ Figure: - 11 to 13 Designed using Fusion 360 CAD
➢ Figure: - 14 to 21 Convectional Design Static Analysis details
➢ Figure: - 22 Weight Analysis of the 1st Iteration
➢ Figure: - 23 Render image of Generative Design of Quad-Copter frame
➢ Figure: - 24 to 35 Generative design Procedure in fusion 360
➢ Figure: - 36 to 37 Generative Design Static Analysis details
➢ Figure: - 38 Mass Analysis of 2nd Iteration
➢ Figure: - 39 Representation of 3D printing
➢ Figure: - 40 Multiple design outcomes of the Generative Study
➢ Figure: - 41 Conventional Design
➢ Figure: - 42 Generative Design

1- INTRODUCTION
Conventionally designed and fabricated Quadcopter frame are less durable and weight of
frame is comparatively high. We can overcome this problem by using Generative Design and
Additive manufacturing.
For achieving above criteria, we selected Generative Hybrid X Type Design. Because Hybrid X
is made for racing as well as for carrying payload.
A quadcopter, also called a quad rotor helicopter or quad rotor, is a multirotor helicopter that
is lifted and propelled by four rotors. Quadcopters are classified as rotorcraft, as opposed to
fixed-wing aircraft, because their lift is generated by a set of rotors (vertically oriented
propellers).
Unlike most helicopters, quadcopters use two sets of identical fixed pitched propellers; two
clockwise (CW) and two counter-clockwise (CCW). These use variation of RPM to control lift and
torque. Control of vehicle motion is achieved by altering the rotation rate of one or more rotor
discs, thereby changing its torque load and thrust/lift characteristics.
1.1 Quadcopter Frame Types:
There are many different styles of frame, all related to the stance of the arms and the size
and shape of the electronics carriage. Below, each frame type is explained along with a graphical
example.
• True X
The true X is shaped as it sounds, an X geometry to which a motor is mounted to each end of
the arms. The perpendicular distance between the center of each motor is equal, therefore giving
the quadcopter the same level of stability on all axis.
Fig:01
• Wide X
A wide X has its arms splayed outward to the side. The wide X geometry is more common in
freestyle frames, this is because more central space is often required to mount an action camera
and battery on top of the frame.

Fig-02
• Hybrid X or Stretch X
The stretch X is a rotated wide X. The stretch X is typically favored by racers who are seeking
more stability on the pitch axis, which can improve control when the quadcopter is racing at high
speed.
Fig-03
• Dead Cat
The dead cat style is typically favored by larger quadcopter designs. Its purpose is to remove
the propellers from the sight of the on-board HD camera, this is achieved by increasing the
perpendicular distance between the two frontal motors. The popularity of the dead cat design
has sagged along with the increasing interest in smaller miniguides. Although, there are some
mini and micro quads that continue to utilize the dead cat design, typically as a means of
accommodating uniquely shaped center carriages.
Fig-04

• H Shape
The H style is another archaic style of quadcopter design. In a H quad, the arms are positioned
at the front of a long “bus” style carriage. Recently, the H quad has lost favor due to its bulky size
and awkward configuration.
Fig-05
• HX Shape
The HX is a newer variant of the H. Instead of placing the arms at the tip and tail of the
carriage, a true X, wide X or stretch X configuration is applied, most often wide or true X.
Fig-06
• Z Shape
A Z quad uses two similar base plates mounted on top of each other to produce a stepped
geometry between the front and rear motors. Mounting the motors on different planes improves
the prop wash handling of the quadcopter, as less turbulent air is directed towards the rear
motors during forward fight.
Fig-07

• Plus Shape
A plus frame has the same footprint as a X frame that has been turned 45°. A plus frame can be
seen as advantageous in that each motor is responsible for rotational movement in only one axis,
theoretically meaning finer control is possible. Although, plus frames are more prone to breakage
due to most impacts involving a forceful strike to the front arm only.
Fig-08
• Vertical Arms
Vertical arms rotate the orientation of the arms to produce as small of a surface area as
possible to minimize drag. Durability is not usually compromised as the arm may still maintain
width; however, construction of the frame is often more complex than standard horizontal
frames.
Fig-09

2- LITERATURE REVIEW
2.1 A Practical Generative Design Method - SHIVAM KRISH
A generative CAD based design exploration method is proposed. It is suitable for complex
multi-criteria design problems where important performance criteria are incomputable. The
method is based on building a genotype of the design within a history based parametric CAD
system and then, varying its parameters randomly within pre-defined limits to generate a set of
distinctive designs. The generated designs are then filtered through various constraint envelopes
representing geometric viability, manufacturability, cost and other performance related
constraints, thus reducing the vast design space into a smaller viable design space represented
by a set of distinctive designs. These designs may then be further developed by the designer. The
proposed generative design method makes minimal imposition on the designer’s work process
and maintains both flexibility and fluidity that is required for creative design exploration. Its
ability to work seamlessly with current CAD based design practices from early conceptual to
detailed design is demonstrated. The design philosophy behind this generative method and the
key steps involved in its implementation are presented with examples.
Research highlights
• A Method for implementing Generative Design on top of CAD platforms.
• It can be used in the conceptual stage of design development.
• It is based on emergence.
• It is a practical method that can be used by designers.
2.2 Generative Design: A Paradigm for Design Research - JON MCCORMACK, ALAN
DORIN
Generative design offers new modes of aesthetic experience based on the incorporation
of system dynamics into the production of artifact and experience. In this paper, we review a
number of processes that can be explored by designers and suggest how design as a discipline
can benefit from this research. These processes include self-organization, swarm systems and ant
colonies, evolution, and generative grammars. We give example applications of these processes
to creativity and design.
Our world is becoming increasingly infiltrated and mediated by electronic systems and
devices. The role of design is shifting in response to these changes. In this new role, established
design practices may be inadequate or insufficient, particularly if we consider the function of
design to extend beyond the simplistic desires of consumerism and the validation of corporate
ideologies. A more expanded agenda, such as that proposed by Anthony Dunne, sees design as
‘relocating the electronic product beyond a culture of relentless innovation for its own sake,
based simply on what is technologically possible and sociologically consumable, to a broader
context of critical thinking about its role in everyday life’ (Dunne 1999).

In traditional design, the role of the designer is to explore a solution space. Solutions may
be aesthetic, semiotic, cultural, dynamic, industrial, corporate, political, or any combination of
these and other determinants. The key relationship between designer and artefact is a direct one
(even if mediated via some third-party or medium). There is a direct relationship between the
designer’s intentions and that of the designed artefact. In contrast, design using generative
methods involves the creation and modification of rules or systems that interact to generate the
finished design autonomously. Hence, the designer does not directly manipulate the produced
artefact, rather the rules and systems involved in the artefact’s production. The design process
becomes one of meta-design where a finished design is the result of the emergent properties of
the interacting system (McCormack & Dorin 2001). The ‘art’ of designing in this mode is in
mastering the relation between process specification, environment, and generated artefact.
Since this is an art, there is no formalized or instruction-based method that can be used to guide
this relationship. The role of the human designer remains, as with conventional design, central
to the design process.
2.3 Teaching Generative Design - FISCHER, T. AND HERR
Generative design, which integrates multidisciplinary types of expertise in
unconventional ways, was reserved just until recently to experienced and highly autodidactic
designers. However, growing recognition of the importance of generative design methodologies
have resulted in a need to introduce theories and applications of generative design to
undergraduate students as part of their design studies. This emerging educational field of
generative design teaching currently lacks methodologies, teaching experience and introductory
study material. Available textbooks related to algorithmic form generation, discussing
algorithmic growth, artificial life, fi-actual images, emergent behavior and the like have
originated in the field of mathematics. This resource provides an abundance of examples and
generative approaches but when adapted to design education, it poses great interdisciplinary
challenges which are addressed in this paper. Experiences in generative design teaching are
presented, focusing on the relation between algorithmic reproduction of nature (as emphasized
by authors in the mathematical field) and innovation (as commonly emphasized in design
education). This discussion leads to a derivation of pedagogic suggestions as early steps on the
way towards theories and curricula of generative design teaching, addressed to curriculum
planners, generative design teachers as well as novices of the field such as undergraduate
students.
The production of “generations” from initial blueprints is immanent to the variance and
reproduction of all life. It is quite an obvious idea to adopt this natural approach to human- made
design and to realize product generations designers can choose ‘fit survivors’ from, which
promise to make particular sense in given contexts. In this way, generative design represents the
design discipline’s interest to apply natural inspiration not only in terms of the creation of
products but also in terms of the process of creation. This interest has a long history. One early
example of generative design thinking Mitchell identifies are Aristotle’s musings on the
generation of design variations1. Though generative design is not restricted to the application of
particular types of tools, digital computers have turned out to be especially appropriate for the
following reasons: To generate design implies a somewhat industrial approach to production

insofar as efficient automation is required to output large quantities of solutions. A
programmable universal machine is certainly a very helpful tool in this respect. In contrast to
industrial manufacturing however, generative design leaves the monotony of production up to
the computer and at the same time overcomes and avoids the monotony of products. Moreover,
a significant part of generative design labor comprises permutation of design elements and
attributes, which is most easily accomplished by means of symbolic computation. This symbolic
representation that is inherent to computer-aided design (CAD) also seamlessly integrates
elements of design simulation.
Generative design solutions come into existence in form of digital representations,
allowing early evaluations before their actual (e.g. physical) modelling, production or application.
In this respect, generative design differs vitally from its natural inspiration, which experiments,
generates and extinguishes designs in the most blind and unscrupulous ways. Computer-aided
generative design (and CAD in general) is also easily integrated with common office, data
processing and communication procedures and equipment. As a result, generative design has
good reasons to utilize mathematics, programming and computers and often involves digital
toolmaking. Today, genetic algorithm, cellular automaton or shape grammar are important and
very common keywords in international discourses on CA(A)D but due to their relative novelty in
design and their complexity, these approaches are largely neglected in undergraduate design
studies. This is not only due to the interdisciplinary involvement of generative design work. Very
little has been done so far to develop methodologies, materials and curricula for generative
design teaching and to clarify terms and techniques for teaching purposes.
2.4 Load-Adapted Design of Generative Manufactured Lattice Structures -
GUNTHER REINHART, STEFAN TEUFELHART
Additive layer manufacturing offers many opportunities for the production of lightweight
components, because of the high geometrical freedom that can be realized in comparison to
conventional manufacturing processes. This potential gets demonstrated at the example of a
bending beam. Therefore, a topology optimization is performed as well as the use of periodically
arranged lattice structures. The latter ones show the constraint, that shear forces in the struts
reduce the stiffness of the lattice. To avoid this, the structure has to be adapted to the flux of
force. This thesis is supported by studies on a torqueloaded shaft.
The use of lightweight design components has a multitude of advantages in various fields
of application, like an increased efficiency of material and energy. In case of accelerated masses,
the main benefit is the reduction of the energy, which is needed to operate the respective
mechanical system. This leads to lower operating costs and a more efficient and sustainable use
of resources. The approach of lightweight design can be distinguished in the three categories
microscopic, mesoscopic and macroscopic. In this regard, microscopic design addresses the well-
directed manipulation of the microstructure of one and the same material. In contrast,
macroscopic lightweight strategies deal with the optimization of the part geometry in relation to
the loads applied to it. Finally, mesoscopic approaches – as the main subject of this contribution
– describe the use of material-structures like honeycombs, foams or lattices, which exhibit some
very advantageous properties like low density along with high strength and stiffness.

In the recent past, the realization of lightweight strategies – especially in case of
macroscopic approaches –was mainly restricted by the manufacturing technologies.
Conventional processes like milling, turning or casting show a multitude of limitations concerning
the geometrical freedom in part design. In shape cutting for example, the accessibility to the part-
surface has to be assured. In casting, basic conditions like mold removability, draft angles or wall-
thickness ratios have to be taken into account. Alternatively, to those production techniques, a
new category of manufacturing processes has established since the year 1986: additive layer
manufacturing (ALM). In ALM-processes, the joining of individual volume elements is getting
adopted to build up a part. In Selective Laser Melting (SLM) for example, the process starts up
with the application of a layer of powder on a building platform.
Afterwards, the powder material gets selectively solidified by melting with a laser. This is
followed by lowering the platform for the thickness of one layer and the anew adoption of
powder material. This procedure is repeated until the manufacturing of the part has finished.
Thereby, it has to be mentioned that ALM-produced components show a minor anisotropic,
orientation-dependent material characteristic because of their generative composition in layers.
In the recent past, the application range of the SLM-process has extended from manufacturing
of prototypic parts to the production of applicable technical components, as well as from the
processability of plastics to the generation metal components.
In case of complex parts and structures needed in small lot sizes, these techniques show
advantages compared to conventional processes. Thus, it is possible to produce individual parts
with tailored properties in a flexible and fast way. Beyond these technical aspects, economic
considerations play a major role when taking the use of ALM processes into account. In the
production costs of HSC-Milling, investment casting and ALM have been faced to each other for
different sample parts, whereat the generative manufactured components have partly shown
severe cost advantages for small lot sizes and complex structures Therefore, for the design and
production of tailored lightweight components, this kind of process features fewer restrictions
concerning their part geometry as well as a reduction of costs for manufacturing and utilizing the
lightweight components.

3 – 1st ITERATION: CONVENTIONAL DESIGN
Fig-10
Render image of the 1st Iteration Model
3.1 Designed using Fusion 360 CAD
Step 1: Sketching
Fig-11
Using sketch option in the tool bar need to create the 240mm circle which is our boundary
condition for this design. The motor mounts and arms are designed at an angle of 55 ̊ from the
center of body.
Step 2: Extrude to 6mm

Fig-12
To convert the 2D sketch into the 3D model we need to use the extrude tool. Extrude the
sketch from the XY plane to 6mm in Z direction.
Step 3: Post Processing like Fillet and Shape Optimization
Fig-13
The extruded model needs to post process like fillet at the edges to avoid the sharp
edges. The holes for fasteners mount are add at the motor mount and at the center. The
triangular shape holes are made for the mass optimization.

3.2 Convectional Design Static Analysis details
Material used for the study is ABS Plastic
Density
1.06E-06 kg / mm^3
Young's Modulus
2240 MPa
Poisson's Ratio
0.38
Yield Strength
20 MPa
Ultimate Tensile Strength
29.6 MPa
Thermal Conductivity
1.6E-04 W / (mm C)
Thermal Expansion Coefficient
8.57E-05 / C
Specific Heat
1500 J / (kg C)
Table-01
Step 1: Defining the Constraints
Fig-14
The motor mounts are considered as the fixed constraints for this study. There are four
holes for mounting motor in total there 16 holes for 4 motors are selected for fixed constraints.
Step 2: Defining the Load Case such as Motor Trust and Components load

Fig-15
The motor thrust of 10N is considered for this study. This load case is located at the
bottom surface of the motor mount in the upward direction.
Fig-16
The weight of the components like motors, battery, flight controller and electronic
speed controller are all assumed to be 25N. this load case is applied on the top face of the
model in the downward direction.
Step 3: Select the Material for Study i.e. ABS Plastic

Fig-17
In the tool bar select the materials option, their we need to specify the specific material
for this study i.e. ABS plastic.
Step 04: Solve in Cloud
Fig-18
After defining all parameter for study are ready for the solving this study. If there are
any errors in defining parameter the errors will popup in the precheck tool. If there no errors
than we need to select the solve option in tool bar. For best results cloud solve is preferred.
Step 5: Check the Factor of Safety of the Study

Fig-19
After solving the study, the results, we can check through the safety factor. For this
study the safety factor is 8.83 which is pretty good, means the model can will stand at all which
we defined load case.
Step 6: Stress Analysis Report
Max Stress is 2.264MPa
Fig-20
The above figure represents the stress distribution on the frame. The blue color region
represents the low stress area and the red region represents the max stress area. The maximum
stress is 2.264MPa i.e. on the motor mount holes.
Step 7: Displacement Analysis
Max Displacement is 0.658mm

Fig-21
The above figure represents the displacement distribution on the frame. The blue color
region represents the less displacement and the red region represents the high displacement.
The maximum displacement occurs at the front and back edge of the frame i.e. 0.658mm.
3.3 Weight Analysis of the 1st Iteration: 130g
Fig-22
For analyzing the total mass of this 3d model we used Cura slicer. In this slicer we
defined the parameters for 3d printing this model in 100% infill. The mass of the model is 130g
and 18.51m of ABS filament is required for printing this model.
4 – 2nd ITERATION: GENERATIVE DESIGN

Fig-23
Render image of Generative Design of Quad-Copter frame
4.1 Generative design Procedure in fusion 360
Step 1: Define Preserve Geometry
Fig-24
The motor mounts, flight controller mount and support mounts are considered as the
preserve geometry in this generative design study. The loads and constraints are defined on the
preserved geometry, represented in the green color.
Step 2: Define Obstacle Geometry

Fig-25
The motors, flight controller mount holes and support mounts holes are considered as
the obstacle geometry in this generative design study. The outer boundary is defined as the
obstacle. Obstacle geometry are represented in the red color.
Step 3: Select the Structural Constraints
Fig-26
The fixed constraints are defined as like in static analysis i.e. the motor mount holes are
considered as the fixed constraints.
Step 4: Apply Load Cases

Fig-27
The load cases are applied on the preserve geometry. The motor thrust of 10N is applied
upwards on the bottom of the motor mount. The components weight is considered as 25N and
applied on the top face of the preserved geometry in the downward direction.
Step 5: Define the Objectives of the Study
Fig-28
Under design criteria there are two objectives in the generative design study. One is the
minimize mass, here we need to define the safety factor limits i.e. 2 in this study. Another
objective is maximize stiffness here we need to define the minimum mass of the model.
Step 6: Select the Manufacturing Methods

Fig-29
Under the design criteria we need to specify the manufacturing methods for the iteration
of the generative design study. Here we selected unrestricted, additive and milling methods.
Step 7: Select the Materials for Iteration Study
Fig-30
We need to specify the materials for generative study under the materials tool in the tool
bar. Here we can select multiple materials up to 7 for iterations. We selected ABS plastic and
other additive material library materials for this study.
Step 8: After defining all criteria generative the analysis

Fig-31
After defining all parameter for study are ready for the solving this study. If there are any
errors in defining parameter the errors will pop up in the precheck tool. If there no errors than
we need to select the Generate option in tool bar. Generative analysis will conduct multiple
design iterations in cloud.
Step 9: In the explore tab we will get multiple design iterations
Fig-32
The multiple design iterations are viewed under the explore tab in tool bar. The left corner
there are filters like study, materials, manufacturing methods etc. There are several outcomes
for this study, for selecting the best outcome we need to use the filters are our requirements.
We can sort it by the maximum stress or minimum weight etc.
Step 10: We can analysis each iteration when we open it

Fig-33
For selecting the outcome need to double click on the outcome. When we will get the
several iterations for one outcome. At initially the mass is added in full volume of the design,
then by repeated iterations depending on the minimum stress area material is removed and get
converged at some iteration. Here 44 iterations are in total, at the right side we can get the
properties of each iteration.
Step 11: Export the CAD file from the design iterations
Fig-34
For exporting the required iteration model there is a option in toolbar create a design
from outcome. There is another option create mesh from the outcome for mesh file. In this study
we need the CAD file for further process.
Step 12: Open the exported design and Save as .Stl for 3D Printing

Fig-35
When design file is successfully created than we need to open the design file and need to
save in the cloud.
4.2 Generative Design Static Analysis details
4.2.1 Stress Analysis
Max Stress 6.553 MPa
Fig-36
The above figure represents the stress distribution on the frame. The blue color
region represents the low stress area and the red region represents the max stress area. The
maximum stress is 6.553 MPa i.e. on the arms of the frame.
4.2.2 Displacement Analysis
Max displacement 0.2464mm

Fig-37
Fig-21
The above figure represents the displacement distribution on the frame. The blue color
region represents the less displacement and the red region represents the high displacement.
The maximum displacement occurs at the flight controller mount i.e. 0.2464mm
4.3 Mass Analysis of 2nd Iteration: 67g
Fig-38
For analyzing the total mass of this 3d model we used Cura slicer. In this slicer we defined the
parameters for 3d printing this model in 100% infill. The mass of the model is 67g and 9.48m of
ABS filament is required for printing this model without support structures.
5 - METHODOLOGY

5.1 3D Printing
The 3D printing process builds a three-dimensional object from a computer-aided
design (CAD) model, usually by successively adding material layer by layer, which is why it is also
called additive manufacturing.
Fig-39
Representation of 3D printing
The inkjet printer was invented in 1976. Charles Hull adapted inkjet printing technology
to create stereo-lithography, a printing process which is able to turn 3D software models into 3D
products. 3D printing is the common name for Additive Manufacture. This is where a model is
built up layer by layer. A wide range of materials can be used, including: plastics, metals and
rubber.
5.1.1 Time Line for Additive Manufacture:
• 1976 The inkjet printer is invented
• 1983 The start of 3D Printing using Stereo lithography
• 1988 The first 3D Printer using Fused Deposition Modelling invented
• 1993 Powder bed 3D Printing invented
• 2005 Open Source collaboration on line
• 2006 Selective Laser Sintering used for customizing products
• 2008 Thingy Verse starts
• 2009 DIY kits start being sold.
• 2011 3D Printing in gold and silver
• 2012 3D-Printed prosthetic jaw.
• 2014 Patients jaw rebuilt using 3D Printing.
3D printing is a new way of producing products. The general term used to describe 3D Printing
in all its guises is Additive Manufacturing. As mentioned previously a product is built by adding
layers. The definition and quality of the product is dependent on the size of the extruder or
layering height as well as the resolution of the design software.
To contrast Additive Manufacturing with predominantly Subtractive Manufacturing
techniques: More traditional school processes have relied upon taking blocks of material and
cutting parts of them down to required sizes and shapes etc. Even casting for example usually
requires a pattern to be made using subtractive methods first and even if the casting process is

seen as a genuine additive process casting has a number of limitations which make 3D Printing
more advantageous.
These advantages usually come down to manufacture of more complex forms, the potential
to reduce waste and the manufacture of components which would be virtually impossible to
make in any other way. However, for the first time ever 3D printing offers the ability to have
products manufactured anywhere in the world. No longer would it be necessary to have
industrialized centers distributing products around the world. This could now be done by sending
3D “virtual designs” through the internet and having these manufactured at the point of need.
These designs could also be customized or modified for more specific or needs within its
operating environment.
Manufacturing could be transformed from Just In Time Logistics to Just in Time
Manufacturing as suggested by © Big Innovation Centre (The Work Foundation and Lancaster
University). This would also have an impact on the quality of life. The carbon footprint of products
could be significantly reduced as products could now be manufactured where needed or within
the locality. Busy transportation network and infrastructure would be reduced too. By providing
students with the opportunities to realize the potential of this manufacturing process we are
supporting future generations in improving the quality of life for themselves and future
generations.
5.1.2 Benefits for 3D Manufacturing
• Customization
This is where a product can be partially re-designed to specifically suit a new environment
or new needs.
• Reduced Stocks
Manufactures and retailers would operate with less stock producing only when needed.
Other components or raw materials for the printing would still be required. Just in Time
Manufacturing would now be much more immediate.
• Reduced Capital Costs
Large scale capital investment in factories and machinery would be reduced. However
investment in the printer itself would still need to be accounted for, as would the raw materials
for each print.
• Reduced Transport Costs
Distribution of the products and the components, which make up the products, would be
significantly reduced.
• Environmental Benefits –
3D printing should enable companies to reduce the carbon footprint of themselves and their
products due to the reduced transport costs.

5.1.3 Weaknesses of 3D Manufacturing
• Can Be a Slow Process
At the moment large components can take significant amounts of time to produce. This will be
an area
where print development engineers are already focusing.
• Responsibility
If 3D printed products go wrong who will be responsible for the fault. Will this lie at the hands
of the designer, print manufacturer or the person who is actually responsible for down loading
the print
• Real World Proofing
So far, a lot of the expected impacts on manufacture are conjecture. Finding out if the process
works
practically will be the next step in assessing if the technology is practical if at all possible.
• Assembly.
Depending on the sort of products to be manufactured will mean what other components
will be needed. Some of these will not be practical to be manufactured totally using 3D Printing.
5.2 Generative Design
Generative design is an cad engineering software function in which a designer collaborates
with artificial intelligence algorithms to generate and evaluate hundreds of potential designs for
a product idea. The generative design process starts with defining the goals and constraints of
the project.
Generative design leverages machine learning to mimic nature’s evolutionary approach to
design. Designers or engineers input design parameters (such as materials, size, weight, strength,
manufacturing methods, and cost constraints) into generative design software and the software
explores all the possible combinations of a solution, quickly generating hundreds or even
thousands of design options. From there, the designers or engineers can filter and select the
outcomes to best meet their needs.
Imagine if instead of starting a “drawing” or CAD design based on what you already know or
ideas that are in your head, you could tell a computer what you want to accomplish or what
problem you are trying to solve. For example, say you want to design a chair. Instead of drawing
two or three options (maybe 10 if you’re really creative), you can tell the computer you want a
chair that supports X amount of weight, costs X much, and uses X material. The computer can
then deliver hundreds, if not thousands, of practically and easily manufacturable design options
that all meet that criteria and are likely options that you could not conceive on your own. That’s
the power of generative design.

Fig-40
Multiple design outcomes of the Generative Study
But generative design is not limited to product development, it can also apply to larger
scale projects such as buildings and office spaces. A recent project in the MARS Innovation District
of Toronto used generative design to create the floor plan for an office environment that would
not have been possible to create by humans alone. The architects used generative design to
factor in employee wants and needs, including preferences for each person’s work environment.
The project began with collecting data from employees about their work styles and
preferences by asking questions such as “Are you a heads-down worker or an interactive one?”
It also took into account the teams that should be located near each other for maximum office
collaboration. Once the data was collected, the generative design software produced 10,000
options. Human designers then sorted through the options, making trade-offs in a way that only
humans can. The result of this human-computer collaboration was one the workers love - and
one neither human nor machine could ever have arrived at on its own
In the near future, items that we use every day, the vehicles we travel in, the layout of
our daily work environment and more will be created using generative design. Products may take
on novel shapes or be made with unique materials as computers aid engineers in creating
previously impossible-to-conceive solutions.
As AI becomes part of all work processes and generative design becomes the norm for
product design, it will be exciting to see what we can achieve when we can create more things to
accommodate the growth of the global middle class, making them better suited to consumer
needs, in less time, with less material waste, less fuel waste, and less negative impact on our
planet.

5.2.1 Why Generative Design?
The typical design process, often referred to as the traditional design process, requires
the knowledge and expertise of the designer to craft products that meet the needs of the end
user. Often a laborious process, this requires designers and engineers to meticulously understand
various principles and processes to adequately generate a final design. Even then, and after many
hours of design, analysis and prototyping, the pressures to reduce the development cycle often
result in designs that are far from optimal.
Combine this with the next generation of products that are emerging, which require ultra-
high-performance characteristics and are too demanding for the traditional design process, and
this is where generative design will help create the optimized designs of the future.
With the emergence of technologies such as artificial intelligence algorithms and infinite
computing, which are much more accessible that any time in the past, designers and engineers
can co-create designs using parameter driven optimization.
5.2.2 What is Generative Design?
Generative design is the process of using algorithms to help explore the variants of a
design beyond what is currently possible using the traditional design process. Mimicking natures
evolutionary approach, generative design uses parameters and goals to quickly explore
thousands of design variants to find the best solution.
With each iteration generative design is testing the structure and learning from each step,
applying change at each stage to help produce an optimized solution which meets the design
goals within the parameters outlined in the study setup.
This process often results in designs that would not have otherwise been possible to
create using the traditional design process. As the final forms are designed to suit a specific need,
they’re shape is unique and often referred to as organic.
5.2.3 How does it work?
As previously discussed, generative design allows for a more integrated workflow
between designer/engineer and computer. In effect they both become co-creators of the final
design.
Though there are various ways generative design can be used the following is a summary of the
more typical application of the generative design process:

6 - SUMMARY AND CONCLUSION
Fig-41 Conventional Design: 130g
Optimization
Fig-42 Generative Design: 67g
• Weight optimization i.e. from 130g (conventional design) to 67g (generative design).
Overall, 48% of weight of the quad-copter frame is optimized.
• Stress optimization i.e. from 2.264MPa (conventional design) to 6.553MPa (generative
design). Overall, 189% of stress of the quad-copter frame is increased.
• Displacement optimization i.e. from 0.658mm (conventional design) to 0.2464mm
(generative design). Overall, 62.5% of displacement of the quad-copter frame is
optimized.

 By Adopting Generative Design in Quad-Copter frame manufacturing we can achieve
below Benefits.
• Reduced Weight
Reducing the weight of manufactured parts is another benefit and a real game changer for
the aerospace and automotive industries. One recent case comes from General Motors, where
engineers used generative design to produce a new seat bracket that combined eight
components into one. This resulted in a 40 percent weight reduction and a 20 percent strength
increase compared to the original design.
• Rapid Approach to an Optimal Solution
Since more designs can be created within a shorter time frame, the optimal design solution
can also be found quickly. This is because designers can compare and contrast all the different
designs generated by the software before selecting the best one.
• Highly Customizable Constraints
Using the designer’s inputs and artificial intelligence, the latest Autodesk design software can
produce highly customized architectural plans based on preset parameters. After the initial
design is produced, the architect can then adjust the software, creating different designs that
satisfy certain criteria such as the building size and cost.
• Increased Productivity
An increase in the productivity is to be expected as a result of the numerous design variants
available at the touch of a button. Instead of taking precious time to come up with the various
possibilities of a design, designers can use this time on other projects.
• Decreased Human Resource Costs
For many businesses, employee wages account for a large portion of the operating
expenses. This is important in the manufacturing industry as multiple designers need to work
together to come up with innovative ways to solve design problems. However, these costs can
now be reduced with the help of generative software, freeing up expensive human resource costs
that can be funneled elsewhere in the business.
• Consolidation of Multiple Parts
The ability to consolidate multiple parts into a single part is another benefit of generative
design. This is because highly complex information can be processed at a rate that is not possible
for human brains to conceive. As a result, a single part can now be created to replace assemblies
of two or more separate parts.
• Decreased Manufacturing Costs
Due to the consolidation of multiple parts into a single part, decreased manufacturing
costs can be expected. This is because the supply chain will be simplified due to the elimination
of unnecessary parts, reducing the overall manufacturing cost of the product.

• Reduced Material Waste
This is another benefit resulting from the consolidation of multiple parts. By creating
models that require fewer parts, less materials are also required. This helps reduce material
wastes and costs and is also very environmentally friendly.
• Avoids Expensive Manufacturing Rework
Manufacturing rework is a costly process that can reduce production output significantly.
Rework requires extra time and energy to coordinate and complete. With the help of simulation
and built-in testing functions such as those in Autodesk’s product line, most rework can be
eliminated and a final design can be reached within a shorter amount of time.
• Increased Customer Loyalty and Satisfaction
Being able to generate multiple designs at a faster pace means more satisfied customers.
Since the delivered designs are also of higher quality and meet all the customer’s requirements,
it is a great way to increase customer loyalty and build an enviable reputation.
• Thousands of Ideas at the Touch of a Button
Thanks to powerful artificial intelligence software, designers no longer need to think up
designs the old-fashioned way. For example, when designing a small cup, the designer can input
desired parameters such as the weight, material, and volume and the software will deliver all
possible designs that meet those criteria.

7 - References
• Quad-Copter Frame
• https://www.getfpv.com/learn/new-to-fpv/all-about-multirotor-fpv-drone-frame
 3D Printing
• https://3dprinting.com/what-is-3d-printing/
• Infographics.com and BBC
• Three-Dimensional Policy: Why Britain needs a policy framework for 3D printing
 Generative Design
• https://www.autodesk.com/industry/manufacturing/resources/engineering-
leadership/generative-design
• https://knepublishing.com/index.php/KnE-Engineering/article/view/612/1903
• http://www.manufacturinglounge.com/11-quick-fire-benefits-of-generative-design-for-
manufacturing/