AD and Robust Design

A robust design is expected to perform its intended function under all operating conditions (different causes of variations) throughout its intended life without necessarily eliminating noise factors (disturbance factors that cause system functional variability) [Mohsen and Cekecek, 2000]

Robust design method is the general term used to describe a process initiated by Taguchi as quality engineering [Taguchi, 1986]. Taguchi aimed to reduce production variance by creating a quality loss function, and optimizing the product to minimize the loss function. The methods have been expanded and developed, and are commonly termed robust design or Taguchi methods today [Park, 1996]. The premise of robust design is that by consciously considering the noise factors (environmental variation during the product's usage, manufacturing variation, and component deterioration) and the cost of failure in the field, the Robust Design method helps ensure customer satisfaction. Robust Design focuses on improving the fundamental function of the product or process, thus facilitating flexible designs and concurrent engineering.

An overwhelming majority of product failures and the resulting field costs and design iterations come from ignoring noise factors during the early design stages. The noise factors crop up one by one as surprises in the subsequent product delivery stages causing costly failures and band-aids. These problems are avoided in the Robust Design method by subjecting the design ideas to noise factors through parameter design.

The Robustness Strategy uses five primary tools:

i) P-Diagram is used to classify the variables associated with the product into noise, control, signal (input), and response (output) factors. The P-Diagram integrates several ideas of the robustness process, such as signal, noise, control factors, and noise (uncontrollable) factors, in a graphical form. Figure 2.12 shows the format of a P-Diagram.

Figure 2.12 – P-Diagram format

The noise factors are the parameters/factors that are beyond the control of the designer. Parameters that can be specified by the designer are called control factors.

ii) Ideal Function is used to mathematically specify the ideal form of the signal- response relationship as embodied by the design concept for making the higher-level system work perfectly. The ideal function is a mathematical description of the energy transformation within the system.

iii) Quadratic Loss Function (also known as Quality Loss Function) is used to quantify the loss incurred by the user due to deviation from target performance.

iv) Signal-to-Noise Ratio is used for predicting the field quality through laboratory experiments.

v) Orthogonal Arrays are used for gathering dependable information about control factors (design parameters) with a small number of experiments.

The Robust Design optimizes a given design concept or solution to increase the robustness. However, this approach does not provide any process for system design and it focuses on only one requirement at a time. A problem might arise when a design has to satisfy two requirements simultaneously, such as designing a car door to seal completely and close easily where a coupling exists between these two functional requirements.

The quality and effectiveness of Robust Design greatly depends on the selection of an appropriate system output characteristic. However, this selection process, currently, has the same problem as the current design practices, both are more like an art than

System

Signal _Response

Noise Factors

science. Several research articles [Hu et al., 2002, Mohsen and Cekecek, 2000] recognized this weakness of the Robust Design approach and they suggested Axiomatic Design principles as a scientific base for Robust Design.

Hu et al. (2002) developed several new approaches to enhance Robust Design by using TRIZ and AD principles and they successfully applied and verified one of the new approaches in a case study in a large automotive company.

Mohsen and Cekecek (2000) demonstrate that the output of AD functional decomposition can be used as inputs to the parameter diagram (P-Diagram) of the robust design analysis. The AD can be used to formulate the P-Diagram of a system. The functional decomposition (mapping and zigzagging) produces the required inputs for the P-Diagram.

• Each functional requirement (FR) (or Design Range) is the signal • The actual output of the system (system range) is the response,

• The design parameters (DPs) that are used to satisfy the FR are the control factors

• The coupling in the design is a noise factor, the internal noise factor (the other noise factors to consider are external environment, piece-to-piece variation, effect of time, and customer usage).

Noise factors such as manufacturing variations, aging, customer usage, environmental conditions and system interfaces, are used in functional testing to simulate the real world [Mohsen and Cekecek, 2000]. The same noise factors should be used in the optimization process to make the design more robust.

The AD method currently addresses the robustness by the two design axioms and the stiffness concept. The independence axiom results in products with reduced internal interaction by achieving functional independence. The information axiom makes sure that the design with highest possibility of success is selected. Also, the design alternative with lower stiffness – the ration of FR to DP – is more robust.

Melvin (2003) extends the AD method and proposes a strategy where the major sources of noise are identified and then specifically targeted during the product

conceptual design. He lists several strategies to make the design more robust; such as reducing FR sensitivity to a noise factor, reducing the noise factor, and compensating for FR variation due to a noise factor.