- Why to Use Robust Design Method?
- Robustness Strategy
- Tools Used in Robustness Strategy
What Is Taguchi Method?
Robust design method is also named as the Taguchi method. This method was introduced by Dr. Genichi Taguchi and is great help for engineering productivity improvement. It works to aid in the system design which is insensitive to uncontrollable or noise variation. Taguchi extended the implementation of robust designs to involve a range of input conditions.
Why to Use Robust Design Method?
Many companies have invested a lot in the Six Sigma approach aimed at reducing waste during manufacturing and operations. These efforts have had great impact on the cost structure and therefore on the bottom line of those companies. Many of them have reached the maximum potential of the traditional Six Sigma approach. What would be the engine for the next wave of productivity improvement? It can be the adoption of improved product development processes under the design for Six Sigma. The design for Six Sigma approach is focused on:
- increasing engineering productivity so that new products can be developed rapidly and at low cost
- value based management.
Robust design method is central to improving engineering productivity. Many companies which uses this method have saved a lot of money in diverse industries: automobiles, xerography, telecommunications, electronics, software, etc.
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Typical Problems Solved by Taguchi Method
The robust design method helps to reduce the development time and cost by a factor of two or in many problems.
In general, engineering decisions involved in product/system development can be classified into two categories:
- Error-free implementation of the past collective knowledge and experience
- Generation of new design information, often for improving product quality/reliability, performance, and cost.
With robust design, a company can rapidly achieve the full technological potential of their design ideas and achieve higher profits.
Robustness Strategy
The robustness strategy is to prevent problems through optimizing product designs and manufacturing process designs.
An example: The manufacturer of a differential op-amplifier used in coin telephones faced the problem of excessive offset voltage due to manufacturing variability. High offset voltage caused poor voice quality, especially for phones further away from the central office. So, how to minimize field problems and associated cost? There are many approaches:
- Compensate the customers for their lost money.
- Screen out circuits having large offset voltage at the end of the production line.
- Implement tighter tolerances through process control on the manufacturing line.
- Change the nominal values of critical circuit parameters such that the circuit’s function becomes insensitive to the cause, namely, manufacturing variation.
The last approach is the robustness strategy which provides the methodology for systematically arriving at solutions that make designs less sensitive to various causes of variation. It can be used for optimizing product design as well as for manufacturing process design.
The robustness strategy uses 5 primary tools:
- P-Diagram is used to classify the variables associated with the product into noise, control, signal (input), and response (output) factors.
- 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.
- 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.
- Signal-to-Noise Ratio is used for predicting the field quality through laboratory experiments.
- Orthogonal Arrays are used for gathering dependable information about control factors (design parameters) with a small number of experiments.
Conclusion
The Taguchi Method empowers companies to go beyond traditional quality control by building resilience directly into the design of products and processes. Through tools like P-diagrams, signal-to-noise ratios, and orthogonal arrays, engineering teams can systematically optimize performance while minimizing the effects of variation. This approach not only shortens development time and cuts costs, but also ensures higher customer satisfaction and fewer field failures. In a world where reliability and speed are critical to success, robust design is the next frontier for companies seeking long-term, sustainable excellence.
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