In this blog post, we’ll explore the concepts of scientific design and optimal design as applied in mechanical engineering, as well as the role that optimal design plays in product design.
What Are Scientific Design and Optimal Design?
In the past, the importance of product design garnered significant attention due to patent disputes surrounding smartphones. In the lawsuits between Samsung and Apple at the time, not only features such as “bounce-back” and “multi-touch zoom” but also the iPhone’s design patents became major points of contention. Since then, design has established itself as a key factor in determining a product’s competitiveness and an essential element for successful product sales. As such, the importance of design in today’s product development is growing even more.
However, if a student majoring in mechanical engineering—rather than someone with a design background—were to attend such a crucial design decision-making meeting and discuss the product’s design, the other attendees might find it somewhat puzzling. It may be hard to believe at first, but from here on, I will discuss not merely design that pursues beauty, but scientific design as practiced in mechanical engineering—that is, optimal design.
First, since optimal design is fundamentally a computer-aided design technique, understanding the concept of Finite Element Analysis (FEA) will make it easier to grasp its principles and advantages. Finite Element Analysis is a computational method that divides a complex real-world object into countless small elements for calculation; simply put, it’s similar to building an object using LEGO blocks in a virtual reality environment.
To help you understand this, let’s take a closer look at the surface of the smartphone you use. It likely appears as a single smooth surface without any distinct boundaries. So, is the surface of a smartphone really made up of just a single solid piece? No, it isn’t. It only appears to be a single surface because the boundaries between the countless materials that make up the surface are so minute that they’re imperceptible to the naked eye.
Lego blocks, on the other hand, are different. If you think back to the Lego toys you played with as a child—whether it was a spaceship or a police car—the boundaries between each block are clearly defined. A structure where each element is clearly distinguished in this way is described as being composed of finite elements.
Now that you understand the concept of finite elements, let’s examine why we use them in a virtual space rather than creating an object that is an exact replica of reality. If we were to build a car using blocks the size of a grain of rice—much smaller than the LEGO blocks currently on the market—we could reproduce a real car with even greater precision. However, such tiny blocks would be difficult to assemble and would take an excessively long time to build, making them unsuitable for actual toys.
The same applies to creating objects in a virtual space. Instead of replicating reality exactly, models are constructed using finite elements of an appropriate size that balance computational efficiency and accuracy.
Using this finite element analysis eliminates the need to manufacture and test a new physical product every time a design is modified. Since the product model already exists in a virtual space, it can be subjected to repeated analyses with new design iterations. Furthermore, while testing a physical product for damage caused by severe impacts or repetitive use would require discarding the test sample, in a virtual environment, the same model can be used to perform as many iterative experiments as desired. In this way, finite element analysis offers the advantage of effectively reducing the time and cost required for product manufacturing and testing.
How Does Design of Experiments Find the Best Design?
Design of Experiments (DoE) is a design method that further maximizes these advantages. Consider the mindset of a dating novice about to go on a first date with his girlfriend. He’ll worry about what to wear and what to talk about to ensure a successful date. Since he can’t know his date’s ideal type with certainty, he’ll gauge her reactions each time they meet and gradually figure out what she finds ideal.
However, if a virtual simulation existed that reflected both her personality and behavior, he could repeat countless virtual dates without worrying about damaging the relationship in real life. Ultimately, he could find the best date plan and increase the likelihood of a positive outcome right from the first meeting.
Optimal design works in a similar way. It is a method of applying numerous design proposals to a virtual product composed of finite elements, comparing the performance of each, and then identifying the most suitable design.
How does optimal design find the best shape?
The process by which a computer independently finds the optimal design is also similar to the virtual dating analogy mentioned earlier. Let’s assume that, at first, you tried a virtual date with long hair, but the other person’s reaction wasn’t favorable. Based on the fact that they don’t like long hair, you try again with slightly shorter hair. Since the reaction is better this time, you repeat the same process while gradually adjusting the hair length. As the experiment continues, you’ll discover that it’s not simply a matter of “shorter is better”; in fact, once the hair falls below a certain length, the other person’s favorability actually drops. Ultimately, you’ll conclude that the length just before that point is the one the other person prefers most.
Optimal design works in exactly the same way. The computer repeatedly calculates how well the design meets the target conditions while making small adjustments to the initial design shape. For example, if given the condition to design a structure that can withstand the greatest possible load while using as little material as possible, the computer finds the most efficient shape through countless iterative calculations. Through this process, unnecessary parts are removed and necessary parts are retained to create the optimal structure.
Even if it starts with a simple shape, it gradually evolves into the form best suited to the objective through repeated calculations and performance evaluations. This process allows for the rapid evaluation of far more scenarios than design based solely on human experience and intuition, helping to achieve more rational design results.
Many of the products we use in our daily lives are often created through this process. Take a bicycle frame as an example: it starts with a simple shape, but through iterative optimization, the design evolves to remove unnecessary material and reinforce areas where loads are concentrated. Ultimately, a frame that is lightweight yet sufficiently strong is completed.
In this way, the countless products we use without giving them much thought are not simply designed based on intuition; they are the result of an optimization process that involves scientific analysis and iterative calculations to reduce unnecessary elements and maximize performance. While it may be difficult to readily imagine the intersection between mechanical engineering and design based on common sense, mechanical engineering today is continuously evolving by pioneering new design domains that take product performance and efficiency into account.
