This document discusses the methodology for conceptualizing the architecture of electronic control unit (ECU) casings. It involves several steps: 1) Layout optimization to meet thermal, vibration, and sealing requirements. This includes optimizing the surface area, isolating components from vibration, and designing effective gaskets. 2) Selection of materials based on a decision matrix comparing properties like structural strength, thermal properties, durability, cost, and weight for materials like aluminum, copper, and cast iron. Aluminum was selected. 3) Selection of manufacturing processes based on decision matrices comparing options for functionality, quality, cost, tooling costs, and lead time. This included comparing casting, machining, and their combinations.

1. International Journal of Mechanical Civil and Control Engineering
Vol. 1, Issue. 3, June 2015 ISSN (Online): 2394-8868
23
Architectural Conceptualisation Methodology Of
Electronic Control Unit Casing
Abstract — ECU Casing encloses Printed circuit board (PCB) for
protection foreign conditions. Architecture Conceptualization is
an important phase of product development architecture of ECU
Casings. Paper explains the methodology for architecture
selection which involves steps such as layout optimization,
Material Category, Manufacturing Category, Production Process,
And Material Grade. Statistical analysis tool is used for
comparison of all dependant parameters and its applicability in
each step of architecture selection.
I. INTRODUCTION
ECU Casing encloses Printed circuit board (PCB) for
protection against components from Thermal Damage, Dust
and Water ingress,Vibrations.
The design methodology of ECU Casing involves various
Phases of product cultivation from processing of inputs till
validation of the product. Requirement Analysis being first
phase, analyses all PCB requirements, mechanical
requirements. Architecture Conceptualization being second
phase, analyses layout optimization, Material Category,
Manufacturing Category, Production Process, Material Grade.
Design being third phase, analyses all aspects of Engineering
Design, Manufacturing Design, and Feasibility Design.
Validation being fourth phase includes prototyping, physical
testing and Optimization. This Paper concentrates on second
phase only [1].
II. ARCHITECTURE CONCEPTUALIZATION
A. Scheme / Layout optimization:
Scheme includes finalization of orientation and sizing of
components in the assembly. It is decided after analysis of
requirement. For complying thermal requirements,
amplification of the surface area and good thermal conductivity
material is required. For complying vibration requirement, PCB
must be isolated from metal interfaces by rubber material. For
complying sealing requirement, Gasket must be designed with
proper resting on metal surface. For complying packaging
requirement, production process with less part to part variation
should be ensured.
Surface area of fins must be sufficient to control the
temperature of electronic components below safe limit. The
PCB should be packaged such that it will be hold firmly
without any warpage. The sealing design is conceptualised by
the target requirement against water and dust ingress protection.
Figure 1 elaborates the broad level architecture of electronic
control unit assembly. This includes PCB assembly, ECU
Casing which is dissipating the heat to atmosphere. Integrated
gasket takes over PCB assembly, bottomplastic cover and ECU
Casing [1].
Fig. 1. Broad Level Architecture of Electronic Control Unit.
B. Material Category selection:
Material type decision matrix helps in analysing material
type to be used for all components in the assembly by cross-
functional study between functional parameters that each
component should adhere to and deliverable parameters that are
expected from component to accomplish production targets.
Decision Matrix Rating methodology which is applicable to all
matrixes in the document is as per attached table no 1. Rating 1
indicates poor performance of a product in the respective
category and Rating 5 indicates highest performance of a
product in the respective category. In functional parameters
(structural, thermal, durability properties) rating (1-5) increases
with the property but in case of cost, rating (1-5) decreases with
increase in cost [1].
TABLE 1. DECISION MATRIX METHODOLOGY
1
Sohan Sontakke, 2
Choudhari C.S
Department of Mechanical Engineering, AISSMS
College of Engineering, Pune, INDIA

2. International Journal of Mechanical Civil and Control Engineering
Vol. 1, Issue. 3, June 2015 ISSN (Online): 2394-8868
24
Rating Performance Structural Cost
1 Poor 0- 100 MPA 200- above Rs
Per Kg2 Average 100-200 MPA 150-200 Rs Per
Kg3 Good 200-300 MPA 120-150 Rs Per
Kg4 Very Good 300-400 MPA 70-120 Rs Per
Kg5 Excellent 400-Above
MPA
0-70 Rs Per Kg
Material type decision matrix (Table 2) establishes
comparative analysis between aluminium, copper and cast iron
against functional and deliverable parameters.
In structural comparison, cast iron leads the segment for a
same cross section, hence rated to 5, In Thermal properties
comparison, copper stands out with highest point. Durability
refers to behaviour of material in fatigue. Cast iron is famous
for its damping properties, hence highly rated. In terms of cost,
copper is worst and cast iron is best. In terms of weight,
aluminium is lightest of all. Aluminium is easily
manufacturable in large number of quantities. The main
competition is between cast iron and aluminium. In functional
parameters, cast iron leads aluminium. But due to its lagging in
deliverable parameters, aluminium stands out.
TABLE 2. MATERIAL TYPE DECISION MATRIX
Functional
Parameters
Deliverable
Parameters
Total
Structural
Properties
Thermal
Properties
Durability
Properties
Costper
unit
Weightper
unit
Mass
Production
Feasibility
Alumin.
um
3 4 3 4 5 5 24
Copper 4 5 4 2 4 2 21
Cast Iron 5 3 5 5 2 3 23
Fig 2. Material Type Decision Matrix.
Fig. 2, gives more clarity. Cast iron (marked in green) peaks
in some points but overall area occupied by aluminium
(marked in blue) is more. Hence aluminium stands out of all
these due to inherent characteristics in thermal, cost and mass
production feasibility. Cast iron being structurally very good
lags in cost per unit due to wall thickness constraints in casting
and thermal properties. Copper being thermally excellent lags
due to heavy cost.
C. Manufacturing Category selection:
Manufacturing category decision matrix helps in analysing
Manufacturing type to be implemented for all components in
the assembly by cross-functional study between functionality,
quality, cost per unit, tooling cost and lead time.
Manufacturing category is subjected to number of components
to be developed. If the component is to be launched in three
quantitative stages, matrix for all three stages gives different
results [1].
Manufacturing category decision matrix (Table 3)
establishes comparative analysis between casting sub-types
such as casting up to final shape, casting to rough form and
light machining, casting to rough form and heavy machining
against functionality, quality, cost per unit, tooling cost, lead
time. No. of components plays an important role in matrix
formation. Two matrixes with different no. of components are
analysed.
Prototyping is done to evaluate the product design, which
needs few components in short time. Hence, the manufacturing
option should be able to deliver few components in shortest
time. In functionality and quality, final casting is rated high.
But rating lowers in terms of tooling cost and lead time. Rough
casting and heavy machining will deliver product with quality
lower than the final casting, hence rated low. But, the tooling
cost and lead time will be least and hence rated highest. Rough
casting with heavy machining stands highest in the matrix with
18 points.
TABLE 3. MANUFACTURING OPTION DECISION MATRIX
(BASED ON PRODUCTION STAGE – ONLY 10 COMPONENTS)
Manufacturi
ngProcess
Sub-Types
Functionality
Quality
Costper
unit
ToolingCost
LeadTime
Total
Casting
Final Casting 5 5 5 1 1 17
Rough Casting
light machining
4 4 3 3 3 17
Rough Casting
heavy machining
3 3 3 5 4 18

3. International Journal of Mechanical Civil and Control Engineering
Vol. 1, Issue. 3, June 2015 ISSN (Online): 2394-8868
25
Fig. 3. Manufacturing Option Decision Matrix ( 10 components)
Fig. 3, gives more clarity. Rough casting (marked in brown)
covers more area than other options (colours). Hence, the best
option is to make a rough casting and heavy machining.
For production stage (2000 Per Month), the ratings for cost per
unit, quality and functionality proves very important. Final
casting leads with rating of 5 in major parameters. In
functionality and quality, final casting is rated highest. But
rating lowers in terms of tooling cost and lead time. Rough
casting and heavy machining quality rated lowest in
functionality, quality and per unit, cost. Final casting stands
highest in the matrix with 19 points.
TABLE 4. MANUFACTURING OPTION DECISION MATRIX
(BASED ON PRODUCTION STAGE - 2000 PER MONTH)
Manufacturin
gProcess
Sub-Types
Functionality
Quality
Costper
unit
ToolingCost
LeadTime
Total
Casting
Final Casting 5 5 5 1 3 19
Rough Casting
light machining
4 4 3 3 4 18
Rough Casting
heavy machining
3 3 1 5 5 17
Fig 4. ManufacturingOptionDecisionMatrix ( 2000components per month)
Fig. 4, gives more clarity. Final casting (marked in blue)
covers more area than other options (colours) proving final
casting as a best option.
D. Production Process Selection:
Production Process Decision matrix helps in analysing
Process type by cross-functional study between component
functional parameters such as Minimum Wall Thickness,
Dimensional Stability, Casing Defects/ Porosity, Roughness,
Aesthetics,Machining Allowances [1].
Production Process Selection matrix (Table 5) compares the
processes. High pressure die casting leads all processes in all
parameters, hence rated highest.
TABLE 5. PRODUCTION PROCESS DECISION MATRIX.
Types of
casting
Min.Wall
Thickness
Dimensional
Stability
Casing
Defects
Roughness
Aesthetics
Machining
Allowances
Sum
Gravity 2 3 3 3 3 2 16
Shell Sand 3 3 3 3 3 3 18
Low Pressure 4 4 4 4 4 4 24
High Pressure 5 5 4 5 5 5 29
Fig. 5. Production Process Decision Matrix
Fig. 5, gives better clarity. Purple colour of high pressure
die casting covers maximum area, hence leading the segment.
E. Material Grade Selection:
Material Grade decision matrix helps in selecting Material
Grade to be used by cross-functional study between Process
Feasibility, Cost, Structural, Properties, Thermal, Properties,
and Availability [1].
Material Grade Selection matrix (Table 5) highlights the
applicability of LM 20 material due to excellent combination
of cost, structural and thermal characteristics. For high
pressure die casting process feasibility, all materials in the

4. International Journal of Mechanical Civil and Control Engineering
Vol. 1, Issue. 3, June 2015 ISSN (Online): 2394-8868
26
matrix are good, hence at same level in matrix. In terms of
structural and thermal properties, LM 20 leads other grades.
TABLE 6. MATERIAL GRADE DECISION MATRIX.
Process
Feasibility
Cost
Structural
Properties
Thermal
Properties
Availability
Sum
LM 2 5 3 4 2 4 18
LM 6 4 4 4 2 4 18
LM 20 5 5 5 5 4 24
LM 24 4 4 3 4 4 19
LM 25 5 4 5 4 5 23
Fig. 6. Material Grade / Composition Decision Matrix.
Fig. 6, gives better clarity. Red colour of LM 20 covers
maximum area, hence leading the segment.
Acknowledgment
I am beholden to my co-author /mentor /guide Prof. C. S.
Choudhari from AISSMS College of Engineering, Pune for
his valuable Suggestions. I am sincerely grateful to him for
sharing truthful and illuminating views on number of issues
related to the project. I am using this opportunity to express
my gratitude to all staff and Head of mechanical engineering
department of AISSMS College of Engineering, Pune for
their aspiring guidance, invaluably constructive criticism
and friendly advice during the project work.
REFERENCES
[1]. Sohan Sontakke and Pankaj Kumar, “Design and Development of
Electronic Control Unit Casing of Electric Hybrid Transmission
System” Presentedat SIAT – 2015,SAE paper no. 2015-26-0120.
[2]. V. Krishnan and Karl T. Ulrich, “Product Development Decisions:
A Review of the Literature