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Product platform design through sensitivity analysis and cluster analysis

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Abstract

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design.

Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.

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Abbreviations

AOF :

Aggregated objective function

PPCEM:

Product Platform Concept Exploration Method

VBPDM:

Variation-Based Platform Design Methodology

PP:

Physical Programming

\({\mathcal{P}_{s}}\) :

Preference aggregation operator

x fam :

Vector of design variable values for an entire product family

x i :

Vector of design variables for variant i

p i :

Vector of decision parameters for variant i

z i :

Vector of predetermined design parameters for variant i

x i j :

Instantiated design variable x j for variant i

p i j :

Decision parameter p j for variant i

z i j :

Predetermined design parameter z j for variant i

m :

Number of variants in a product family

n :

Number of constraints

k :

Number of design variables

SI i k :

Local sensitivity of x k for variant i

Gs k :

Global sensitivity of x k

ID:

Index of Dissimilarity

SI t :

Threshold value of sensitivity

f i* :

Optimum performance for variant i

f i*j :

Optimum performance for variant i when x i is commonized with x j

\({x_k^{c_1\ldots c_N}}\) :

Commonization value of x k for cluster {c 1... c N }

\({x_{i(p)}^j}\) :

Platform variable x i for variant j

\({x_{i(np)}^j}\) :

Non-platform variable x i for variant j

H:

Magnetizing intensity

P:

Desired power for each motor in the family

η:

Efficiency

M :

Mass

T :

Torque

TT :

Torque target for each motor

Nc :

Number of turns of wire on the motor armature

Ns :

Number of turns of wire on each field pole

Awa :

Cross-sectional area of the wire on the armature

Awf :

Cross-sectional area of the wire on the field poles

r :

Radius of the motor

t :

Thickness of the stator

I :

Current drawn by the motor

L :

Stack-length of the motor

V t :

Input voltage

n pole :

Number of poles

ρ:

Resistivity of copper

ρcopper :

Density of copper

ρsteel :

Density of steel

l gap :

Gap length inside the motor

μ0 :

Permeability of free space

μ air :

Relative permeability of air

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  1. Department of Mechanical & Industrial Engineering, University of Illinois at Chicago, 842 W. Taylor Street, Chicago, IL, 60607, USA

    Zhihuang Dai & Michael J. Scott

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  1. Zhihuang Dai
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  2. Michael J. Scott
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Correspondence to Michael J. Scott.

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Dai, Z., Scott, M.J. Product platform design through sensitivity analysis and cluster analysis. J Intell Manuf 18, 97–113 (2007). https://doi.org/10.1007/s10845-007-0011-2

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  • DOI: https://doi.org/10.1007/s10845-007-0011-2

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