From increasing the resource yield to strengthening your value chain, the diligent execution of optimization within the design process can mean the difference between thriving and declining.
Optimizing early and often has the following key benefits:
It creates a lasting ripple effect on the designs and on the financial impact throughout the design and product life cycle.
The number of designs explored increases drastically while empowering engineers to understand their models better to improve results.
The data gained from multiple iterations opens a whole new vista of insight.
In this article, we’ll guide you through these areas of opportunity while contrasting the traditional vs. modern optimization mindset.
Early Decision Making Has a Big Impact on Life-Cycle Costs
It is well known that 70–80% of the life-cycle costs of design are determined by decisions made by engineers during the early design stages. Engineers have the power to impact the cost, value, and quality of a design while exploring the possibilities that impact downstream activities.
Let’s imagine a company that designs and manufactures chassis for heavy equipment. Modern optimization techniques in the early design stages enable the engineer to explore chassis designs that are cheaper, faster, lighter, safer, or stronger. Based on the objective at hand, a chassis-design search space that would otherwise be very difficult or impossible to explore can be evaluated by the optimizer through a quantitative and formal analysis.
With an intelligent optimizer, the team can explore and execute model adjustments to the chassis that are more drastic and impact not only the design but also the following factors:
Manufacturing plan (e.g. machining process and plant layout)
Manufacturability and costing
Intelligent Optimization Goes Beyond an Engineer’s Intuition
In a traditional design process, only the last 5–20% of the design would be optimized, and this is usually done by exploring tweaks/adjustments through a few trial-and-error runs or design group meetings or by using a sampling plan. There are two main issues with this process:
First, drastic changes would not be possible as much of the design work was already completed and the process and assembly plan would otherwise require too much altering.
Second, the opportunity to explore vastly different design alternatives is far passed.
Here’s a closer look at performing design exploration through an intelligent optimizer versus DOE (design of experiments) or sampling plan:
DOE or sampling plan: Limitations exist in typical DOE scenarios. Engineers must possess a deep understanding of their model to capture the design to its essentials. Only several variables are included in the plan (3–8 on average). If the engineer is not too familiar with his model and more variables are present, some must be disqualified based on engineering intuition, as the sheer amount of sample points would be too costly and lengthy to compute. In addition, engineering intuition, although important, may result in design disqualification that could otherwise be explored.
Intelligent optimization: With intelligent optimization, an engineer can set a relatively loose model with loose bounds to run simulations through the optimizer. Since the optimizer automates the iterations and design exploring based on intelligent sampling and advanced algorithms, an engineer does not have to be present or active at his station in order to generate designs, gain insight, and find the best design. This synergy allows engineers to simulate and optimize over the weekend, during their downtime, in the evenings, during meetings, or whenever. This increases the designs explored by easily 2 or 3 times.
Intelligent Optimization Unearths Data That Can Help Prevent Mistakes
Insight into how different variables interact, the model’s behaviour, what optimality acts like, and what design should be explored or deemed infeasible are all more clearly showcased from iteration runs.
Engineers Can Understand Their Models Better
An intelligent optimizer opens an alternative workflow. The engineer will load his rough design and input as many variables as needed; same would apply to his constraints. The engineer would pick a generic cheap objective (such as mass or stress capacity) and let the optimizer intelligently explore the search space. Since the optimizer has an intelligent component that learns and directs the search as opposed to traditional DOE, the data and insight gained would be more meaningful and guide the engineer to better designs.
The Intelligent Optimization Process Saves Time and Money
Optimization has an impact on financial levers. Running designs through an intelligent optimizer early and frequently has a positive impact on the following aspects:
Material quantity (weight management)
Manufacturing process (composites as key example)
Number of prototypes used
Time spent on design
Time spent on data population and data entry
The impact of delays with the design and development process is something that deserves serious attention. A recent survey by Tech-Clarity found that missing product requirements reduced profits by 31% while missing time to market goals reduced the profitability by 21%. Considering the same survey found 61% of manufacturers reporting missed deadlines as a major challenge, there is tremendous opportunity for companies to proactively address and capitalize in this area.
An intelligent optimizer is aimed at reducing design issues that often appear at the end of development during validation and testing. This is crucial as issues late in the developmental process require redesign and rework that ultimately push deadlines and budgets out of scope. With intelligent optimization, this is a preventable situation.
To learn more about optimization reach out to our team at empowerops.com.