Design-led component selection

PII: SOOlO-44S(97)00103-6

Computer-Aided Design, Vol. 30. No. 5, pp. 391-405, 1998 6 1998 Elsevisr Science Ltd. All rights reserved Printed in Great Britain 0010-448!%%/$19.00+0.00

ELSEVIER

Design-led component selection Q J Harmert*, P M Weaver* and K M Wallace*

process. Designers need to know which parts are available and which best match their requirements. Ideally, the widest possible range of options should be considered. This paper describes a new method to assist designers in translating design requirements into potential embodiments employing catalogued components. The method is illustrated with case studies of selection problems.

Designers are under increasing pressure to deliver better, cheaper products in less time. To the designer of low-volume or one-off products, using past designs and catalogued components is often essential. Little software support exists early in the design process for designers wanting to use catalogued components. Most information on off-the-shelf solutions has to be gleaned from component manufacturers catalogues. Searching a large number of paper- or computer-based catalogues soon becomes unwieldy and inefficient. This paper describes a method to assist designers in selecting components early in the design process. 0 1998 Elsevier Science Ltd. All rights reserved.

RELATED WORK The usual way of accessing and selecting engineering components is to manually search through suppliers catalogues. Culley and Webber3 have highlighted drawbacks in this haphazard browsing approach: information is poorly structured and in a variety of media; selection is generally tedious and time consuming; and objective information is difficult to obtain as each manufacturer has a different style of presenting their data. For example, one battery manufacturer will present self-discharge data in terms of capacity retained4, another in terms of capacity lost5. In design literature a number of authors describe approaches to accessing and selecting components based on their function, for examples see References 2,3,6*7.Roth was one of the first to map functions to embodiments (or components) using “design catalogues” 8. These are classification schemes of possible ways of carrying out a function. For example, a catalogue of “rotary transmissions” would show various options including gear trains, pulleys and belt drives. The aim is that every possibility is systematically considered by designers. However, although these catalogues help generate feasible solutions they do not assist in evaluating them. Within the Schemebuilder system at Lancaster EDC, function maps’ display the range of performance of cornpet-. ing working principles for fulfilling a function in various domains. They help to identify whether a component meets the required performance, but do not indicate which offers the best performance in view of the design requirements. The CASOC system developed at Bath University assists designers in directly choosing a specific catalogued component once they know the required type lo. Users specify their requirements in terms of ranges for component parameters. CASOC searches a database for satisfactory components and offers an evaluation procedure to support tbe final decision. By using actual manufacturers component codes, the system speeds up the tedious task of finding and ordering specific catalogue components. The system covers plain and rolling element bearings and springs from a number of different manufacturers, enabling comparisons between them to be made. An increasing number of component manufacturers and suppliers provide electronic versions of their catalogues. RS

Keywords: design, component selection, graphical interaction, constraints

INTRODUCTION In mass-produced products like automobiles, costly investment in tooling and automated assembly is spread over a large quantity which results in high-quality, low-cost engineering. In contrast, products produced in low volumes (less than 1000 units per year) appear to offer poor value for rehabilitation devices money. For example, many for disabled people, appear either relatively expensive or comparatively unrefined’. A powered arm support containing no high-technology, for example, can be as costly as an advanced car engine. One way the low-volume producer can help address this problem is to use standard components wherever possible: “If you can buy it, don’t make it” *. In this way a welldesigned, quality-assured component such as a battery is procured at a fraction of the cost and effort required to develop and make it in-house. Purchasers acquire the result of a large investment in design and development by the battery manufacturer. For example, to design a battery from scratch might involve satisfying more than 50 interrelated design requirements, but a suitable battery can be selected from a catalogue by defining a few parameters (properties) like energy capacity, operating current, operating voltage, size, mass and cost. If standard parts constitute a significant proportion of the product, they must be considered early on in the design

‘To whom correspondence should be addressed. Tel.: 01223 420024; Fax: 01223 423373; e-mail: [email protected] tcambridge Consultants Ltd, Science Park, Milton Road, Cambridge CB4 4DW, UK $Engineering Design Centre, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 lPZ, UK Paper Received: 1 April 1996. Revised: 9 December 1997

391

Design-led

component

selection:

Q J Harmer et al.

1

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BACKGROUND ,

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Polymeric

foams

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Density, Figure 1 Material selection weight design in bending

chart

10

p (Mg/m3) showing

guidelines

for minimum

Components, for example, supply their entire catalogue and data library covering many different kinds of component on a single CD”. Data can be accessed rapidly through keyword searches but no further assistance is given with selection. Other commercially available component selection systems focus on a single type of component from one manufacturer (see for review). For example, the “SPEC” spring selector package I2 requires inputs of the spring type (tension, compression, torsion, etc.), maximum dimensions and required force/extension operating point to be given. The program supplies the results of a catalogue search. An improvement on this text-only system is the windows-based “Lee Spring” selector ‘j. Diagrams illustrate the selection parameters and context-related comments are given to assist the course of the selection. With many computer-based methods, if the requirements are not specified precisely enough or lie outside the catalogue range, the search can fail. In the first case every component in the catalogue is selected, in the second none. Often, to the user’s frustration, no indication of the reason for the failure is given. The drawbacks of current commercial component selection methods (paper- and computer-based) can be summarised as follows: (1) Systems are manufacturer-specific. (2) Systems are product-specific, e.g. “batteries” rather than “electrical energy sources”. (3) Paper-based methods are tedious and time consuming. (4) Computer-based methods can give “all or nothing” search results. (5) Data presentation formats vary from catalogue to catalogue. * Ashby I4 defines the shape factor as a dimensionless measure of structural efficiency. Efficient shapes include I-beams which possess larger values than solid sections. ‘r The performance index (I%$)“‘lp has been expanded by substituting for the shape factor, using $J = 4aI/A ‘.

392

the

These drawbacks have motivated the development of design-led component selection charts. These charts are used early in the design process to compare components and help designers select those which match the functional requirements most closely. The generic methodology is described and then illustrated using two different types of component selection problem. One involves selection of an appropriate electrical source (battery, solar cell, etc.) and the other concerns selection of a structural component (beam or column).

Increasing performance

Q< ,

(6) Designers cannot easily see the effect of changing selection criteria.

The proposed approach to selecting components is derived from Ashby’s methodology for materials selection i4 which has been used in the Cambridge Materials Selector (CMS) software i5. The performance of materials usually depends on more than one property and Ashby showed that by plotting material properties on logarithmic charts, performance-governing relationships could be clearly seen. For example, to minimise the mass of a beam of given stiffness, it is necessary to choose a material with the largest value of (Er#~)“~lpwhere E, I#Jand p, are Young’s modulus, shape factor for elastic bending and density, reygectively*. A group of material properties, such as (E4) IQ, which governs performance is known as a perJomance index and is independent of any design parameter. Figure 1 shows a material property chart of E+ against p. All engineering materials are covered in the single chart and materials of the same class are grouped together as bubbles in the chart. Materials which lie along straight lines of slope = 2 on the chart make beams of the same weight (for a given stiffness) and therefore have the same value of the performance index. Lines with greater values of the y-axis intercept indicate better performance (a lighter beam) as shown in Figure 1. Although this method is particularly useful for identifying the best material for a beam, Weaver and Ashby16 recognised certain drawbacks if it was to be useful for selecting standard beams listed in catalogues: In practice, not all standard sections of the same material are made to the same structural efficiency. The range of section sizes which can be manufactured is limited. Standard sections are manufactured in discrete sizes not a continuous range. They overcame these limitations by plotting the bending stiffness (flexural rigidity), EZ, against the mass per unit length, Ap (where I and A are the second moment of inertia and the cross-sectional area, respectively)?. EI and Ap are component rather than purely material properties and a new performance index, EI/(AP)~, defines the level of performance. This introduces scale into the problem and allows standard catalogued beams to be plotted as shown in Figure 2. Each symbol on the chart refers to a beam that can be bought off-the-shelf and beams made from the same material cluster together on the chart. Clearly standard beams are not made over a continuous range of sizes and each cluster consists of discrete points in EZ-Ap space. There are also limits on the range of sizes over which beams are manufactured. For example, standard steel l-beams are only made in the range 13 kg m-r to

Design-led component selection: Q J Harmer et al. minimise mass l.OE+lO t

4

I

1

Bending stiffness v beam mass

l.OE+OQ

Performanceindex \

# j q

_I“

I

f ;_

l.OE+05

Softwood beams

1

10

100

1000

Mass/length kg/m

Figure 2

Lightest rafter of given bending stiffness

390 kg m-‘. These represent the limits of economic viability coupled with processing difficulty. It is interesting to note that beams of a given group are not all made to the same efficiency (as indicated by the value of the performance index) and that the efficiency is dependent to some extent on the size of the component. For example, it is difficult to make very small or very large I-beams to a high efficiency (large EZ/(Ap)2) because of manufacturing problems associated with making a large web depth to thickness ratio. This kind of information is impossible to gain from the equivalent materials selection chart (Figure 1) where there is no indication of scale. Selection is made by identifying the value of bending stiffness needed for a particular design and drawing a horizontal line. A corresponding vertical line indicates the upper limit of mass acceptable for a design. Together, the two lines define a quadrant of acceptable beams. The selection can be tightened by lowering the level of acceptable mass until a handful of sections remain (as indicated by the arrow in Figure 2). Further charts cover selection for other types of loading,

$ Strictly it is not the constraint that is plotted but a form of the constraint written in terms of component properties.

such as torsion and compression, and other objectives, such as minimum cost. This type of chart is re-examined in more detail in the case studies. In summary, materials and component selection differ in one key area. To select a material, the scale does not need to be specified, but for a component, it is essential. Measures of scale, like dimensions and mass, must be included in the range of component properties on which component selection is based. As a result, a new kind of chart that plots a constraintt (like bending stiffness, EI) against an objective (like mass per unit length, Ap) is required. SELECTION METHODOLOGY In this paper a “component” is defined in the broadest sense as any artefact which fulfils some function. It is generally part of a larger assembly or product. Any standard catalogue component which designers might buy-in to use in an assembly or product is included. A component can be identified by a set of key properties which describe its behaviour. The unique set of properties or property profile of one component distinguishes it from another. This is analogous to the set of material properties which describe an 393

Design-led component selection: Q J Harmer et al.

(a)

owar source l

Supply electrical energy at 12V, 1.5A for at least 45 minutes.

Support load of 6OOONover length of 55m with deflection less than 30mm. Depth less than 306mm.

l

l

Minim&e cost

Constraints

(a)

Supply electrical energy

Objective

Minimum mass, M

Voltage, V = 12V Current, I < 15A Time,

(b)

I

t > 45minutes

Support load

Relationshios batwaan

Constraints (a)

Component Properties

Voltage, V = 12V Voltage, V = 12V Current, Time,

Max power output, P>18W

I < 15A

t > 45

minutes

Energy capacity, E>46.6k.f

i fb)

Failure moment > 4125Nm

Load, W = 6fJOON Length, L = 5.5m

Desired Property Profiles

Bending stiffness > 4.8x10sNm2

Deflection, 6 < 3Omm Depth, D < 3OOmm

Figure

3

Translating

design requirements

into a desired property profile

engineering material like density (p) and Young’s modulus (0. When specifying a component, designers are generally interested in “what it does”, not necessarily “how it does it”. As long as the component fits the functional requirement, the mechanism by which this is accomplished is of secondary importance. For instance, there are many types of battery with different chemical systems and enclosures, but the designer’s interest is in the battery’s properties such as energy capacity, power output level, cost and so on. The selection methodology is summarised in Figures 3 and 4 with the case studies described later in the paper as examples. Early in the design process, the product may be described in terms of its functions. Starting with the design specification, those functional requirements relating to a particular component are translated into a set of functions,

394

Depth < 3OOmm

constraints and objectives. Ashby uses a similar scheme for materials selection’4. The function, such as “supply electrical energy”, specifies the type of component of interest, e.g. electrical energy sources. Constraints are identified from functional requirements such as “supply electrical energy at 12 V, 1.5 A for at least 45 minutes”. The objective is often to minimise cost or mass. The next step involves translating the constraints into required component properties and obtaining a desired property profile (Figure 3). Finally, selection charts are used to identify a sub-set of potential components by comparing graphically the desired property profile with property profiles of existing components in the database (Figure 4). Three types of selection chart can be plotted: .

Constraint

versus

Constraint

(Figure

5(a)).

The

4

Constraints

Pmpew2=Y ~Property3>2

Properh/lCX

Function

Figure 4 Information flow in the component

selection methodology.

l~__________~_~_~___~~~~~~~~~~~~~~~-~~~~

1

Examples

given for electrical

r

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energy sowces and structural

components

Selected component

I *Selection

: I

’ Translating requirements into desired property profile (see Figure 3 for more detail) 1

Product Specification

Desired property profile

3

Min. Umenabn

Electrical energy source databaw

Design-led

component

selection:

Q J Harmer et al.

Selection region: components which pass both constraints

Group of components which satisfies the constraint and minimises the objective function

A group of components

I

/

I Decreasing -+ objective zective

Constraint 2

(4

e.g. Max. section depth, d

-

Increasing objective

e.g. Mass per unit length (Ap)

Selection regions: components which maximise both performance indices under different operating conditions

Coupling line: a function of the operating conditions (e.g. loading and geometry) which differs for each design

Performance Index 2 e.g.Efficiency of bending strength,

d (AP)~

Figure 5 (a) Component property chart plotting a constraint versus constraint. (b) Component (c) Component property chart plotting two performance indices against each other

constraints are plotted as fixed lines parallel to the x-axis

l

l

and y-axis. These charts clearly show which components satisfy the constraints and consequently help to narrow down the selection. Constraint versus Objective (Figure 5(b)). In this chart a fixed constraint is plotted parallel to the x-axis and a line parallel to the y-axis indicates the objective. This objective line can be moved by designers allowing them to choose the subset of components which maximise the objective. For example, a chart of bending stiffness, El (a constraint required by the design of a structural beam), against mass per unit length, Ap (a common objective: minimum mass), enables the lightest available beam of the required stiffness to be chosen. Performance Index versus Performance Index (Figure 5(c)). These charts can be used early in the design to trade off two design constraints against each other without introducing subjective weighting factors. They

property

chart plotting

a constraint

versus an objective.

enable the selection to be narrowed down using only the most general design information. On these charts there is no measure of scale, so a final selection is made later using the constraint versus objective charts. For example, a beam in bending may have constraints on both stiffness and strength (and minimum mass as the objective). Each constraint generates a performance index, in this case efficiency of bending stiffness and efficiency of bending strength. A plot of these two performance indices shows which components maximise both performance indices under different loading and geometry. Two examples of this kind of chart are given in the case studies. In each case, the charts are plotted using logarithmic axes allowing a wide range of options to be considered. Using a series of such charts designers can systematically eliminate components which are not feasible and select the best option

Design-led component selection: Q J Harmer et al.

360000

36000

3600

s

.ti

360

2 Q 9 6

36

!ia

3.6

Dndarybatteries 0.1

0.36

0.036

10000

n

Figure 6

0 Lead Acid Battery

Silver Oxide Battery

o Alkaline Manganese Battery

A Nickel Cadmium Battery Nickel Metal Hydride Battery

+ Zinc Chloride Battery

l

A Lithium Thionyl Chloride Battery

X Solar Panel

0 Lithium Mana. Dioxide Battery

X Petrol Generator

Lightest electrical energy source for a given energy capacity. tank of fuel)

1ooooo

(Solar panels: energy supplied per 8 hour sunlight day. Generators:

energy from one

in view of the design objectives. As well as plotting the functional properties of a broad range of possible components, the charts give a design-led basis for comparison and selection. In general, the number of selection charts needed is proportional to the number of properties in the required property profile. For example, if there are two constraints and one objective two charts are needed. The advantages of this approach are as follows:

performance relative to a design objective. can be com(3) Products from different manufacturers pared. (4) Successive stages can be used to narrow down the selection using a series of constraints and objectives. (5) The graphical format clearly shows the effect of relaxing or tightening constraints. This can help give a “feel” for the sensitivity of the objectives to functional variables.

(1) The widest range of possible components is considered in a single chart using logarithmic axes. indicate best charts constraint-objective (2) Ihe

The method has been applied to two different problems: the selection of portable electrical energy sources and the selection of structural elements. The use of the different

397

Design-led

Table 1

component

List of properties

selection:

Q J Harmer et al.

for electrical energy sources

General properties

Geometry

Electrical properties

Environmental

cost Mass Shelf life

Length Diameter (for cylindrical cases) Depth (for prismatic cases) Breadth (for prismatic cases) Case volume

Nominal voltage Nominal (maximum) capacity Current at maximum capacity Capacity at maximum temperature Maximum current Self discharge rate’ Recharging cycles”

Max. Min. Max. Min.

properties

storage temperature storage temperature operating temperature operating temperature

“Where applicable.

types of selection chart sections by case studies.

is illustrated

in the following

SELECTION CHARTS FOR ELECTRICAL ENERGY SOURCES A selection chart for electrical energy sources is shown in Figure 6. Energy capacity, a constraint, is plotted against mass, an objective. Various types of primary and secondary (rechargeable) batteries, solar cells and generators are plotted on the chart and fall into clear groups. The range of different types presented here is not comprehensive, but is intended to be representative of the common portable sources. The data is taken from manufacturers catalogues (see Appendix for list of sources) and covers five orders of magnitude in mass from batteries weighing less than a gram to petrol generators weighing several kilograms. A complete list of properties is given in Table I. It should be noted that the total energy realised from a source is not always constant: many batteries become less efficient at higher discharge rates and the total energy output decreases. These sources are represented by a line joining maximum and minimum values.

Case study 1: energy source for a drinks dispenser for a disabled person The table-mounted drinks dispenser is required to pump liquid from a glass up a tube to the user’s mouth (Figure 7). A pump driven by a DC motor is used. The power rating for the motor is 1.5 A at 12 V on full load. The device is used intermittently, the pump running for an average of 15 minutes per day. At least three day’s use is required at a time. The mass of the device must also be minimised. What energy source is recommended?

lead acid batteries, and solar panels. Any of these will meet the performance criteria. Lithium thionyl chloride and alkaline manganese are primary (non-rechargeable) battery systems and the cells would need to be replaced regularly. This would be impractical in the drinks dispenser, which leaves the solar panels and rechargeable nickel cadmium and lead acid batteries as feasible options. In practice, a sealed lead acid battery of energy capacity 52 kJ (14.4 W hrs) is used because the “memory effect” of the nickel cadmium is undesirable. The solar panels are a possible alternative but their large flat shape (with risk of damage) and greater cost preclude them here. A further chart using cost as an objective would reveal this. Further charts cover selection for other constraints like operating temperature, shelf life and self-discharge rate, and other objectives like minimum cost and minimum volume. The chart also shows the comparative performance of sources in terms of least mass for a given energy capacity. The lightest source for a specified energy capacity is that with the maximum value of EJM. Where E, is energy capacity and M is the mass of the source. This is a performance index commonly called the Specific Energy of the source”. Contours of constant EJM plot as lines of gradient = 1 in Figure 6. Performance indices are used in the next case study problem with two design constraints.

Combining

two design constraints

Often in a design, there may be more than one constraint to be satisfied. For example, electrical energy sources are required not only to have a given energy capacity, but also a specified discharge rate or power output. If mass is to be minimised, a new performance index, Specific Power, must also be maximised (Specific Power = P/M, where P is power output and M is mass). An approach based on Ashby’s method for mater&l4 allows both constraints to be traded-off in a selection using a single performance index

The selection Converting the energy constraint into a desired property profile as shown in Figure 3, the energy capacity required per day is 12 X 1.5 X 0.25 X 3 X 3600 = 48.6 kJ (Energy = voltage X current X time) or 13.5 W hrs”. We want the subset of sources which have E, > 48.6 kJ and the lowest mass. From the chart (Figure 6) we can see that the sources with sufficient capacity and the lowest mass are lithium thionyl chloride, alkaline manganese, nickel cadmium and sealed

$ One Watt hour equals 3600 J. W hrs are commonly battery capacities. 398

used to describe Figure

7

Drinks dispenser (Sumed International

(UK) Ltd.)

Design-led

component

selection:

Q J Harmer et al.

1000

p’ ’ g Selection region

100

’ s

’

Maximum

10

e E & g a 0 = .-

1

8 &

0.1

0.01

0.001 36

3.6

360

3600

Specific energy (kJ/kg) n

Silver Oxide Battery

0 Lead Acid Battery

0 Alkaline Manganese Battery

A Nickel Cadmium Battery

+ Zinc Chloride Battery

l

A Lithium Thionyl Chloride Battery

X Solar Panel

4 Lithium Mang. Dioxide Battery

X Petrol Generator

Figure 8 Lightest electrical energy source for given energy capacity energy from one tank of fuel)

and power output. (Solar panels: energy supplied per 8 hour sunlight day; generators:

versus performance index chart and without introducing any subjective weighting factors. The method relies on obtaining a single measure in which both design goals can be expressed. In this case, the constraints and objective are expressed as two performance indices. These are Specific Energy = EJM and Specific Power = PIM

Nickel Metal Hydride Battery

Both have the mass, M, in common. Eliminating mass, and introducing the average discharge time of the source (t) t = EJP

(1)

(power equals energy per unit time), gives an equation linking the two performance indices (assuming discharge at constant power) Specific Energy = t(Specific Power)

399

Design-led component selection: Q J Harmer et a/.

600kg load

EC log10 j-j = loglot + log10 5

(1

Figure

* Figure

9

energy sources with Specific Energy plotted on the x-axis and Specific Power on the yaxis. The parallel lines of gradient = 1 correspond to eqn (3) for different discharge times. The two performance indices are coupled, enabling both design constraints to be satisfied without weightings. The chart can be used very early in the design process when constraints are still “fuzzy” to narrow down a selection involving two constraints and an objective. Unlike the selection chart in Figure 6, this chart has no scale factor (such as mass) and so only relative comparisons are possible. Usually, one constraint is active and the other is secondary. In other words, for a given selection, all the components satisfying the active constraint will also satisfy the secondary one. The chart indicates which constraint is active. If the

v

5.5m Loading on a rafter for an attic

or

(g)=t(;)

0

8 shows electrical

(2)

where t is discharge time, P is power, E, is energy capacity, M is mass. Taking logs of eqn (2) gives

minimise cost -

a-. . .

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Cost of beam f/m . Steel universal beams

(EM-1993)

x Fibreforce GFRP l-beams A

400

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Selected

Glulam WOOden

Cheapest rafter of given bending stiffness

q Alcan n

beams

i-beams

Softwood BS5268

Design-led component selection:

Q J Harmer et al.

DepthdOcm 1.OE+07

z 5

Applied bending moment vs. l.OE+O6

5c

$ 8 E l.OE+O5

z TI I *

1.OE+O4

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selection box :fl

l.OE+02

z

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1.00E-02

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Depth of beam (m) A Steel universal beams (E&l-l 993) n

Softwood EC%268

q

Alcan l-beams

A Selected Glulam wooden beams

x Fibreforce GFRP l-beams Figure 11 Rafters with both depth and moment capacity constraints

selection lies above the coupling line (given by eqn (3)) the x-axis constraint is active, if the selection lies below the coupling line, the y-axis constraint is active. The appropriate constraint versus objective chart can then be used to make a final selection. In this way different standard technologies can be compared early in the design process. Using the chart for narrowing down a selection is illustrated in the next example.

Case study 2: energy source for a pocket calculator Early in the design of a pocket calculator, an energy source is to be specified. The requirement is for a low power delivered intermittently over a long period of time (years). Mass is to be minimised. If the calculator is used for a few minutes a day over a period of several years, the total “on” time could be estimated at 300 hours. This determines the coupling line. Eqn (3) plots as a line in the lower half of the chart in Figure 8. The best sources are those furthest along this line, which fall in the selection region shown. For sources which lie above

the coupling line energy capacity is the active (or limiting) constraint. For sources which lie below the line, power output is active. The chart indicates that alkaline manganese, lithium thionyl chloride and lithium manganese dioxide are suitable battery systems, and solar cells are an alternative source. These sources can be taken forward to the next stage of the design. Others can be eliminated. In practice, both solar-powered and battery-powered calculators are commonplace. Most battery-powered calculators use alkaline manganese batteries but lithium batteries with their superior performance are becoming more common. Using only approximate estimates of the functional requirements, the selection chart has led to a rapid narrowing of the design field at the earliest stage of design.

Case study 3: rafter for an attic The overall geometry of a building dictates the requirements for a rafter in the attic of a building. For example, suppose the length, 1, has been specified as 5.5 m and the rafter is to 401

Design-led

Table 2

component

selection:

Cl J Harmer et al.

List of properties for structural components

General properties

Dimensions

Section properties

C,: cost per kilogram

n: deepest dimension(m), e.g. Depth of I-beam or diameter of tube B: broadest dimension (m), e.g. width of I-beam or diameter of tube T: major wall thickness (m). e.g. thickness o flange of I-beam or tube wall thickness t: minor wall thickness (m), e.g. thickness of web of I-beam or tube wall thickness r: fillet radius (m), N.B. not applicable for tubes

A: cross-sectional

Recycle fraction Energy content

Structural properties Ap: mass per unit length; CA:

area

I I,: moment of inertia about x-axis

axial strength EI,: bending stiffness

I,,: moment of inertia about y-axis

El,:

Z,,: section modulus about x-axis

uZz,: bending strength about axls (first yield) uZ,: bending strength about axis (first yield) OS,: bending strength about axis (fully plastic section) uss,: bending strength about axis (fully plastic section) GK torsional stiffness Tq: Torsional strength

Z,,: section modulus about y-axis S,,: plastic section modulus about x-axis S,,.: plastic section modulus about y-axis K: torsion constant Q: torsional sectional modulus

carry a load, W, of 600 kg uniformly across its length (Figure 9). The maximum deflection, 6, must not exceed l/200 of the length to prevent plaster cracking on the ceiling beneath. A further constraint is that the rafter must be less than 30 cm deep. Suitable rafters might include steel joists and beams, aluminium and GFRP beams and, of course, wood. The objective is to find the rafter which minimises cost. These functional requirements are translated into constraints as shown in Figure 3. Potential choices are shown in the selection charts in Figures IO and 11. Each data point corresponds to a different beam. Wood is the obvious choice with the cheapest at around &2 per m. Suitable steel beams start from E4 per m. Factors which are not purely functional, such as availability, fire resistance and assembly costs also enter the selection process. In addition, the joining behaviour with other members must be taken into account. However, there is no reason why properties that describe these features cannot be added to the property profile, and included in the selection. The charts can also determine the limiting or active constraint. For example, the designer may want to know whether the strength or stiffness constraint determines the final choice and, therefore, cost of the rafter. This can be achieved by plotting the performance indices for both strength and stiffness limited design on a single chart (similar in style to Figure 8 for electrical energy sources). To derive these performance indices, we must consider beam theory. For the beam in Figure 9, the strength and stiffness constraints, written as equalities, are given respectively by 5Wl’

M=aYZandEI=

3846

(4)

where sY is yield strength, M is the maximum bending moment and Z is the elastic section modulus. If the cost/ metre, C, of a beam is given by C = AC,,, where C, is the cost/kg of material and A is the section area, then two expressions for cost can be obtained, one for each constraint. These are16 ‘I

= M

213 ACm ~ (a,~)2”

(Strength Constraint)

and ‘I2 AC

(EI)‘l’

(Stiffness Constraint)

Euler buckling resistance xyxy-

These constraints are equally active for beams of the same cost (i.e. C, = C,). Therefore by&

~2=

[(3;4y3)1’2M213]

‘;;I

[(&$)“2M2”]M,

(7)

holds, where M, and M2 are performance indices for stiffness and strength driven design, respectively. The two performance indices are coupled through the term in square brackets which can be evaluated for a particular design. This is plotted on an MI and M2 chart as a straight line (Figure 12). Any component that passes through the line is equally constrained by stiffness and strength and the performance can be read off either axis. Now consider a component with the same value of MI but with a larger M2. The cost given by the strength constraint is now less than that given by stiffness; meaning that the design is stiffness-limited and the cost is given by the value of MI. In other words, a vertical line drawn upwards from the coupling line indicates constant performance for stiffness limited design and is given by the value of M,. A similar argument holds for components with a greater value of MI but with the same value of Mz as components on the coupling line. A horizontal line drawn from the coupling line heading in the direction of increasing MI indicates constant performance for strength limited design; the performance follows from the value of M2 on the coupling line. Together, the lines form a rightangled line with their intersection on the coupling line. It is this right-angle that indicates constant performance (cost). All components that lie within the angle have better performance. As the angled line moves up the coupling line, performance improves. For the example considered here, the cheapest beam is likely to be made from steel or softwood since these lie in the upper right hand side of the chart. Other selection charts are needed to ensure scale is introduced into the problem, i.e. check that the type of component is made in the required size. This is readily accomplished using Figures 10 and II. The selection is now established for structural components. A database of more

(6) of steels,

aluminium

Design-led component selection: Q J Harmer

et al.

I’

[Coupling1

l___L

Increasingpetformance

/..;__

/

I

,’

I’

1

I

I’

1

10

I

100

Performance Index-Stiffness (El?AC,) A Steel universal beams (BS4-1993) l

Softwood BS5268

q

Alcan l-beams

A Selected Glulam wooden beams

x Fibreforce GFRP l-beams Cheapest rafter of given bending stiffness and strength

fibre reinforced plastic (GFRP). Solid rectangular beams made from softwood and glue-laminated beams are also included. The data comprises dimensions, section properties and structural properties of sections (see Appendix for data sources). A complete list of properties is given in Table 2. In practice, a designer would choose a section based on some combination of the above properties. The graphical technique, presented here makes this task easier and is available as a commercial PC Windows based software package “. Multiple selection charts can be analysed simcltaneously for any one design and the effect of relaxing and tightening constraints can be clearly seen.

methodology can be applied to a wide range of engineering components. The key to selection is identifying the set of properties which define a component’s behaviour. This unique property profile of a component distinguishes it from another of the same kind. Property profiles for components of the same kind, e.g. electrical energy sources, can be assembled to form a database. To make a selection, it is first necessary to identify the functional requirements from the specification and write these as constraints. These constraints are then translated into a desired property profile. Finally, selection is made by graphically comparing the desired property profile with that of existing components in the database. Three types of selection chart are identified. These are:

CONCLUSIONS

(1) constraint versus constraint; (2) constraint versus objective; and (3) performance index versus performance

This paper presents a generic methodology for component selection suitable for use early in the design process. The

Permutations

index.

of these can also be used as required. 403

Design-led

component

selection:

Q J Harmer et al.

The methodology has been illustrated with two different examples of component selection: electrical energy sources and structural sections. The uniform style of presentation and broad range of these charts give designers a powerful method for comparing and selecting from competing technologies. A full set of charts can quickly show the relative merits of each existing component and could suggest where new components need to be developed.

ACKNOWLEDGEMENTS We are grateful to the EPSRC for funding this research. We would also like to thank Professor Mike Ashby and the members of the Cambridge Engineering Design Centre for helpful discussions and support during the course of this work.

APPENDIX ENERGY SOURCE DATA All the data for electrical energy sources is taken from manufacturer’s and distributor’s literature current in March 1995. Battery data is from the following catalogues: l l l l l l l l l l l l

RS component catalogue, March 1995 RS data sheets K19638, K18786 Fame11 component catalogue, March 1995 Crompton Etemacell Lithium TCL data sheets Duracell Alkaline and Lithium battery data sheets Eveready Zinc Chloride battery data sheets Panasonic Micro Batteries Technical Handbook Sonnenschein Lithium product data catalogue Varta Ni-MH Button Cells Technical Handbook Varta Sealed Ni-Cd Cells Technical Handbook Varta Primary Button Cells Technical Handbook Yuasa NP Lead Acid Battery Manual

Section data on wooden sections comes from two sources. The rectangular monolithic sections are from BS5268 and the Glue-laminated sections from “The Timber Designers’ Manual’ ’ “. Price data is from Jewson” and Laminated Wood Plc, respectively.*“. Most of the quoted section data can be made in a number of different material grades. We quote properties for a BS Grade 50 steel alloy, which is a general purpose carbon-manganese structural steel with design strength of 355 MPa. Aluminium extrusions are commonly available in either 6061 or 6082 grades. We use data for a 6082 T6 alloy of strength 275 MPa. Fibreforce pultrusions are available in glass reinforced vinyl ester grades. The lay-up information is not disclosed by the company; however, relevant material properties are: Young’s modulus, 17 GPa; shear modulus, 3 GPa; design uniaxial strength, 200 MPa; design shear strength, 20 MPa; and density, 1700 kg rnm3. Both the wooden and glulam sections are made from either European whitewood or redwood in SS grade; laminations are 45 mm thick. Although material properties vary with thickness, typical properties are: Young’s modulus, 10 GPa; shear modulus, 650 MPa; design bending strength; 7.5 MPa; design shear strength, 0.82 MPa; and density, 550 kg m-j.

REFERENCES I. 2.

3.

4.

Jones, D., We have rhe technology. Channel Four Television, London, 1995. Vogwell, J., and Culley, S.J., Optimal component selection using engineering databases. Proc. International Conference on Engineering Design ICED’87, Boston, 1987, pp. 749-758. Culley, S.J. and Webber, S.J., Implementation requirements for electronic standard component catalogues. Proc IMechE, 1992,206,253260. Yuasa, NP Valve Regulated

Lead Acid Battery Manual.

Swindon,

1995.

Maximum energy capacity is the maximum value quoted by manufacturers. When not quoted directly, the minimum energy capacity and maximum power output are derived from manufacturer’s discharge curves at maximum current. Data for solar panels and petrol generators is taken from: l l

BP Solar Product Catalogue, 1994 RS component catalogue, March 1995

5.

Varta, Primary Button Cells Sales Program and Technical Handbook.

6.

Hanover, 1994. Roth, K., Design models and design catalogs. the International

7.

8.

Studies, 1981,2(2),

11. R.S. Components,

Section data and material properties

12. 13. 14.

The structural component database is available commercially from Granta Design, Cambridge. All the section data is either from Manufacturer’s data lists or the appropriate British Standard. Prices are correct as of May 1995. The dimensions and section properties of steel universal beams, columns and joists are from BS4, while steel circular hollow and rectangular hollow sections are from BS4848. Price data comes courtesy of British Steel Plc 18. All the GFRP and aluminium data comes from Fibreforce Composites Ltd I9 and Alcan”, respectively. 404

10.

17. 18. 19. 20. 21. 22. 23.

Proc. Proceedings of Design-ICED 87,

107-l

15.

Widden, M.B., et al., Function-costing and function maps in conceptual design. Proc. CACDW, Lancaster, UK, 1994, pp. 203-219. Vogwell, J. and Culley, S.J., A strategy for selecting engineering components. Proceedings of the Institution of Mechanical Engineers, 1991,205,

15. 16.

on Engineering

Boston, MA, 1987, pp. 60-67. Pitts, G., and Vedamuttu, P.A., The basic concepts supporting a commercial components selection system for designers. Proc. ICED 89. Harrogate, UK, 1989, pp. 1239- 1248. Roth, K.H., Foundation of methodical procedures in design. Design

Maximum power output is the maximum value quoted by manufacturers. Energy capacity for solar panels is based on eight hours light under standard conditions quoted by manufacturers. Energy capacity for petrol generators is calculated using maximum power and one full tank of fuel.

9.

Conference

11-17.

The Catalogue, March-June 1995. G.B. Innomech, SPEC Select, Spring Selection, 1994. Lee Spring, Lee Spring Electronic Catalogue, 1995. Ashby, M.F., Materials Selection in Mechanical Design. Pergamon Press, Oxford, 1992. Granta Design Ltd., Cambridge Materials Selector, 1995. Weaver, P.M., and Ashby, M.F., The optimal selection of material and section-shape. Engineering Design, 1996, 7(2). Crompton, T.R., Battery Reference Book, 1st ed. Butterworths, London, 1990. British Steel, Price List. Rotherham, UK, 1995. Fibreforce Composites Ltd., Data sheets. Runcom, UK, 1995. Alcan, Alcan Extrusion Manual, 1992. Baird, J.A., and Ozelton, E.C., The Timber Designer’s Manual. Collins, 1989. Jewson. Price list, Personal communication, Nuneaton, UK, 1995. Laminated Wood Ltd., Price list, Torrington, Devon, UK, 1995.

Design-led component selection: Q J Harmer et a/. I

Quentin Harmer obtained his PhD in design for low-volume production from Cambridge University Engineering Design Centre in 1996. He is currently a product design engineer with Cambridge Consultants Ltd., one of Europe’s leading suppliers of multidisciplinary design and development services.

Paul Weaver obtained his PhD in design of three-dimensional composite structures from Newcastle University in 1992 and has since undertaken a number of composite design consultancies. He currently holds a post-doctoral research position within the Cambridge University Engineering Design Centre. His main research interest lies within the interaction of materials and structure and their influence on design.

I

As Chairman of the Cambridge University Engineering Design Centre, Ken Wallace is responsible for coordinating the research of the 30 members of the Centre’s team. He is Editor-in-Chief (Europe) of the journal Research in Engineering Design. He translated and edited the book Engineering Design by Pahl & Beitz, the 2nd edition of which was published by Springer-Verlag in 19%.

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