Autodesk Claims Machine Learning Technology Will Transform 3D Engineering
Autodesk announced recently the availability of a shape-based search capability in A360. A blog article titled How Machine Learning Will Transform 3D Engineering describes the new capability, called Design Graph, as a “Google search-like functionality for the world of 3D models.”
Google search functionality is probably the wrong metaphor for 3D search. Web search is fundamentally text based, whereas searching for a part or a design requires a combination of textual and geometric terms and attributes, and sufficiently deep domain semantics. In fact, the blog article makes the very same argument later, describing Design Graph’s purpose to “identify and understand designs based on their inherent characteristics—their shape and structure—rather than by any labeling (tags) or metadata” (i.e. not Google-like).
Design Graph is not the first attempt to offer the engineering community a geometry-based search tool. Geometry-based search technologies such as Siemens PLM’ Geolus and Dassault Systemes’s Exalead have been available in the market for 10 years. Analytic software companies aPriori Technologies and Akoya use shape recognition engines to calculate manufacturing costs and optimize inventories based on a part’s shape and features. Standalone search engines are also available from Enfinio, and 3DSemantix. And there is no shortage of academic work in this field.
Still, the engineering community remains unimpressed. The use of shape-based search in day to day engineering activities is tepid at best.
The benefits of finding and reusing parts and validated designs are obvious. Over the years, I have been writing about this topic extensively and you may want to follow some of the links at the bottom of this article. To summarize the benefits of design reuse quickly: save valuable time during design and manufacturing volume ramp-up; eliminate redundant work and reduce costly errors and engineering changes; and reduce inventory.
Furthermore, as I repeatedly point out to manufacturing companies, design reuse is much more than using an existing part instead of designing a new one. Reusing a design means that you take advantage of existing processes and knowledge that are associated with this part, such as manufacturing work instructions, tooling, initial quality metrics, supplier performance data, and warranty claims forecast.
So why are engineering organizations not standing in line to exploit shape-based search as a key design practice?
I believe that 3D-search technology is yet another victim of business practices and culture standing in the way of technology innovation.
We educate design engineers and reward them based on their ability to invent and innovate, not reuse. In many product organizations, design reuse is not a stated goal that is recognized and rewarded engineering activity—although it should be. This is not to say that organizations are not looking for ways to lean their design and manufacturing processes and reduce costs; they do. But a crucial point that many software vendors and even management consultants often miss, is that while the investment and process changes take place withing the engineering organization, the benefits and savings occur mostly downstream, outside the sphere of influence, and, indeed, of interest, of engineering. This makes for a hard sell of any new technology!
If you want to sell this type of tools and process changes to engineers, don’t try to impress them with irrelevant (and often inaccurate) fanciful descriptions of machine learning and artificial intelligence the way the Autodesk blog article does. Tell them how about the new capabilities improve activities and outcomes they care about. And involve the other stakeholders, such as manufacturing, operations, procurement and service, as they understand and gauge the value of process changes from a complete product lifecycle and total lifetime cost point of view.
Image: Field of Dreams (1989)