Robust Optimization

World's Best Practices for Developing Winning Vehicles

Subir Chowdhury, Shin Taguchi

• Publisher: Wiley
• Year: 2016
• Print ISBN: 9781119212126
• eBook ISBN: 9781119212140
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Automotive Transportation & Engineering
• Index: Technology & Engineering

Robust Optimization is a method to improve robustness using low-cost variations of a single, conceptual design. The benefits of Robust Optimization include faster product development cycles; faster launch cycles; fewer manufacturing problems; fewer field problems; lower-cost, higher performing products and processes; and lower warranty costs. All these benefits can be realized if engineering and product development leadership of automotive and manufacturing organizations leverage the power of using Robust Optimization as a competitive weapon.

Written by world renowned authors, Robust Optimization: World's Best Practices for Developing Winning Vehicles, is a ground breaking book whichintroduces the technical management strategy of Robust Optimization. The authors discuss what the strategy entails, 8 steps for Robust Optimization and Robust Assessment, and how to lead it in a technical organization with an implementation strategy. Robust Optimization is defined and it is demonstrated how the techniques can be applied to manufacturing organizations, especially those with automotive industry applications, so that Robust Optimization creates the flexibility that minimizes product development cost, reduces product time-to-market, and increases overall productivity.

Key features:

• Presents best practices from around the globe on Robust Optimization that can be applied in any manufacturing and automotive organization in the world
• Includes 19 successfully implemented best case studies from automotive original equipment manufacturers and suppliers
• Provides manufacturing industries with proven techniques to become more competitive in the global market
• Provides clarity concerning the common misinterpretations on Robust Optimization

Robust Optimization: World's Best Practices for Developing Winning Vehicles is a must-have book for engineers and managers who are working on design, product, manufacturing, mechanical, electrical, process, quality area; all levels of management especially in product development area, research and development personnel and consultants. It also serves as an excellent reference for students and teachers in engineering.

1
Introduction to Robust Optimization

The automotive industry is very dynamic and the product is continuously changing. The competition is so cut-throat that it is becoming increasingly important to deliver quality products at all times. The customers are demanding the highest quality product at a cheaper price. Robust optimization is the mantra for automotive product development organizations both for original equipment manufacturers (OEMs) and their suppliers, especially in this competitive environment. Dr. Genichi Taguchi's Robust Optimization idea is simply revolutionary. To practice robust optimization correctly, product development and manufacturing organizations need to change the way they work, the way work is done needs to change, the way work is managed needs to change, knowledge and skills need to change, the way organizations are led needs to change. Obviously, all of these take time. Not accepting this reality will be more devastating in the future for any organization that wants to win customers' hearts by consistently delivering highest quality products.

Dr. Genichi Taguchi talked about quality as loss to society and how that loss is estimated using a “Quality Loss Function.” He talked about robustness – the functional stability of products or processes in the face of ubiquitous variation in the usage conditions (noise factors). He talked about a product development process involving system, parameter and tolerance design steps. He suggested that engineers focus less on meeting requirements and more on discovering combinations of design variable values that (1) stabilize the function and (2) control the adjustment or “tuning” of that function. He talked about ideal functions.

Dr. Taguchi asked engineers and engineering leadership to look at technical work in an entirely different light.

What happened?

Well, since the word “quality” was part of the “Quality Loss Function,” the quality experts in the organization took over that concept.

Robustness sounded like product performance in the field. So robustness was delegated to the reliability and validation engineers. Noise factors seemed similar to best case and worst case conditions, so that, too, was a good fit to reliability and validation engineering.

His recommended product development system sounded a lot like existing concurrent engineering and optimization methodologies. System engineers looked at Dr. Taguchi's comments and said, “We already do this – there's nothing new here!”

Parameter design was seen as setting design variable values at levels that met requirements in all conditions. Since parameter design borrowed orthogonal arrays from design of experiments, Taguchi's methods were often seen as a form of Design of Experiment. In most engineering organizations, Designed Experiments were organized by a quality expert when engineering had a problem. Parameter design was delegated to quality and product engineering. Often, an experiment was conducted only if a problem of sufficient magnitude presented itself. Taguchi's parameter design methods were roundly criticized by statisticians for, among many other things, a lack of statistical rigor. Even today, “Taguchi Designs” remain a subset of most statistical computer programs. A subset only “recommended” for preliminary, screening experiments.

1.1 What Is Quality as Loss?

One of our client engineers once had a car with a noisy transmission. He took it to the dealer because the noise bothered him. The dealer attached a machine to the transmission. It printed out a report.

“Your transmission is within specification,” the dealer said.

There was nothing more to be done. He drove the car for a couple of years. He was glad when he could replace it with a new one. He never bought that brand of car again – even though their transmission was in specification. The dealer's machine and the printout said so.

Dr. Genichi Taguchi defines quality as “Quality may be assessed as the minimum loss imparted by the product to society from the time the product is shipped.” The larger the loss, the poorer is the quality. This kind of thinking says that there is a difference among products even if they are within specification.

The “ideal” amount of noise from an automotive transmission is zero (yes, it's impossible to achieve). As the noise from the transmission increases it will bother some people more than others. But when the noise bothers someone enough, he or she will suffer a loss. They have to take the time to drive to the dealer and wait while the service technician conducts a diagnosis. There will be a dollar value for his time. The drive, diagnosis and report out will take about two hours. Two hours at that time in this person's life is probably worth about $250. Is that the total loss? What about the company's loss of a future sale? How much is that worth? What is the profit the company would make from the sale? The loss suffered by the company who made the noisy transmission is certainly more than $250.

If an automotive manufacturer makes a very, very noisy transmission, a customer might insist that it be replaced. It doesn't matter if the transmission is in or out of specification. The customer wants it replaced. The total loss to society is probably around $3500 (including customer inconvenience). It doesn't matter whether the transmission is under warranty or not. If under warranty, the manufacturer pays; if not, the customer pays. Either way “society” is out $3500 for each transmission that is so noisy it needs to be replaced.

Using this type of data, the quality in regards to audible noise of any transmission can be estimated. The actual amount of audible noise in decibels could be placed along the bottom axis. Dr. Genichi Taguchi is suggesting that every transmission that makes any noise at all contributes a slight amount of loss to society.

The redefinition of quality that you, as the technical leader of your organization, need to embrace is that producing parts within specification is absolutely necessary. However, only producing parts that meet requirements is no longer competitive.

For long-term success in the marketplace, we must focus on producing low-cost products that lower the loss to society. The average dollars lost by society due to audible transmission noise can be estimated for the transmissions made by your company versus the transmissions made by your competition. The long-term competitive position of your company correlates well with such estimates. Products with lower quality loss to society do better over time in the market. Where do your products rate?

While automobiles provide value to society such as transportation and pleasure of driving, automobiles are producing significant amounts of losses. Those losses include emissions, global warming, and automobile accidents. Dr. Taguchi always dreamt about accident-free automobiles and automobiles that clean air.

1.2 What Is Robustness?

What is robustness? You may have to dust off some of your old textbooks (or go online), but you can do it. The ideas aren't that complicated for a technically trained person like you. Let's define robustness as the ability of a product or process to function consistently as the surrounding uncontrollable or uncontrolled factors vary.

An example is the power window system in the driver's side door of your car. Does it perform today as well as it did the day you took delivery of it? On an extremely cold morning? On a hot summer day? When you are sitting in the car with the motor off? At 50 mph? Has the window ever stopped working entirely?

If two window systems are being compared, the more robust window system is the one that performs most consistently over a large number of cycles, at low and high temperatures, when running on battery power, or when the car is moving a high speed.

Higher robustness means that a product will last longer in the field, that is, in the hands of the customer. No matter how old the vehicle, no customer should have to awkwardly open the door of her car on a cold winter day to pay and pick up her order at the drive-through window. Only window systems with high levels of robustness can meet that requirement.

Robustness is easy to understand. We appreciate the chain of coffee stores that provides a cup of coffee with consistent taste, aroma, and temperature, regardless of whether we buy it in Seattle or Shanghai. We gravitate toward products that perform consistently over a long useful life. A carpenter needs a circular saw that will last for years of hard use after being thrown into the back of a pickup truck. The expensive two-fuel stove in our kitchen shouldn't have the control panel fail in the first month we own it.

One common misunderstanding about robustness is that more expensive products tend to be more robust. We think that we have to pay for robustness. But is a luxury brand car more robust than a small traditional sedan of one-quarter of the price? In many regards, probably not. More importantly, robust optimization provides methods by which high robustness can be achieved at low cost.

1.3 What Is Robust Assessment?

Robustness is a measurement, not a requirement to be reached. Robustness is only meaningful in comparison. Is my product more or less robust than my competitor's? By how much? Is the new design more or less robust than the old design? By how much? The measure or robustness is the signal-to-noise ratio (S/N ratio). The higher the S/N ratio, the more robust the product or process.

Use the creativity of your people to develop methods to assess (estimate) the robustness of your products in 15 minutes! Usually no more than six measurements are needed to estimate robustness. Most companies that use these ideas strategically develop special fixtures to help engineers estimate robustness quickly and efficiently.

After learning and applying Robust Assessment, an Engineering Vice President at Ricoh said, “From now on, our assessment on a paper handling system will take only two sheets of paper.” At Nissan, a robust assessment technique was developed that takes only 15 minutes to assess robustness of a power window system with a high confidence level.

John Elter, a former VP of Engineering at Xerox, said that engineering labs used to be filled with prototype copy machines running continuously for life test and to estimate failure rate. After Robust Assessment, they are filled with jigs and fixtures to measure functions and robustness; functions include paper feeding, toner dispensing, toner charging, toner transfer, fusing, etc.

1.4 What Is Robust Optimization?

Robust optimization, a concept as familiar as it is misunderstood, will be clarified in this chapter. We conduct robust optimization by following the two-step process: (1) Minimize variability in the product or process, and (2) adjust the output to hit the target. In other words, first optimize performance to get the best out of the concept selected, then adjust the output to the target value to confirm whether all the requirements are met. The better the concept can perform, the greater our chances to meet all requirements. In the first step we try to kill many birds with one stone, that is, to meet many requirements by doing only one thing. How is that possible?

We start by identifying the ideal function, which will be determined by the basic physics of the system, be it a product or process. In either case, the design will be evaluated by the basic physics of the system. When evaluating a product or a manufacturing process, the ideal function is defined based on energy transformation from the input to the output. For example, for a car to go faster, the driver presses down on the gas pedal, and that energy is transformed to increased speed by sending gas through a fuel line to the engine, where it is burned, and finally to the wheels, which turn faster.

When designing a process, energy is not transformed, as in the design of a product, but information is. Take the invoicing process, for example. The supplier sends the company an invoice, and that information starts a chain of events that transforms the information into various forms of record-keeping and results, finally, in a check being sent to the supplier.

In either case, we first define what the ideal function for that particular product or process would look like; then we seek a design that will minimize the variability of the transformation of energy or information, depending on what we are trying to optimize.

We concentrate on the transformation of energy or information because all problems, including defects, failures, and poor reliability, are symptoms of variability in the transformation of energy or information. By optimizing that transformation – taking out virtually all sources of “friction” or noise along the way – we strive to meet all the requirements at once.

To understand fully this revolutionary approach, let's first review how quality control has traditionally worked. Virtually since the advent of commerce, a “good” or acceptable product or process has been defined simply as one that meets the standards set by the company. But here's the critical weakness to the old way of thinking: It has always been assumed that any product or process that falls anywhere in the acceptable range is equal to any other that falls within that range.

Picture the old conveyer belt, where the products roll along the line one by one until they get to the end, where an...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Preface
• Acknowledgments
• About the Authors
• Chapter 1: Introduction to Robust Optimization
• Chapter 2: Eight Steps for Robust Optimization and Robust Assessment
• Chapter 3: Implementation of Robust Optimization
• Part One: Vehicle Level Optimization
• Part Two: Subsystems Level Optimization by Original Equipment Manufacturers (OEMs)
• Part Three: Subsystems Level Optimization by Suppliers
• Part Four: Manufacturing Process Optimization
• Index
• End User License Agreement