‘…For me, the most compelling tidbit was CEO Bernard Charlès’ declaration that DS is so over the whole Industrie 4.0 thing. He said that it is “yesterday’s way of thinking, that tomorrow is about ecosystems of makers inventing new things with the end-customer in mind: new services, new content, that industries will offer their clients.” That’s fascinating, because many of the industrial companies I deal with are still very much in the midst of Industrie 4.0. They’re trying to figure out what that means to them, and what/how to digitalize more of what they do and if they even should. …’
Source: DS: Industrie 4.0 is old news; makers and innovators will rule tomorrow
Our impression, from the above highly observant note by astute industry analyst Monica Schnitger, plus many months of our own pondering:
Has DS at last thrown in the towel on engineering, and cast its fate instead in the future of its new ‘scientific‘ brands?
Dassault’s technological decision to require that, to implement 3DEXPERIENCE and CATIA V6 solutions, client organizations must commit to ENOVIA certainly suggests so, in the face of strong new-adoption preferences among leading engineering organizations for rival data/process management platforms such as Teamcenter, Windchill, and now Aras.
In its recent acquisition of CFD leader CD-adapco, Siemens PLM also acquired CD-adapco’s Red Cedar Technology subsidiary, developer of the HEEDS design space exploration software. With this move, Siemens PLM joined its principal PLM rivals in owning premier technology for design space exploration. Dassault Systemes entered the market in 2008 with its acquisition of Isight developer Engineous Software, while PTC has long offered an internally developed design space exploration product known today as Creo Behavioral Modeling Extension (BMX).
World-class design space exploration products are also available from a multitude of independent software developers focused exclusively on this area. On the one hand, these vendors’ commitment to design space exploration is effectively assured. On the other hand, many are small companies that must devote a great portion of their resources to software R&D and customer support; the result is often constrained marketing and sales budgets that make robust growth a challenge.
Being owned by a large, deep-pocketed PLM vendor has the potential to liberate a design space exploration software business from such constraints. But for customers, could there be drawbacks as well? In evaluating and selecting a vendor, buyers need to weigh the likely benefits of sourcing design space exploration software from a major PLM vendor against the potential limitations. Continue reading
Discussions of how to simulate early in product development fixate too often on FEA, overlooking the power of systems modeling and 0D/1D simulation for studying, exploring and optimizing designs at the beginning of projects, when product geometry is seldom available for 3D CAE but engineering decision-making can have its greatest impact and leverage on project success. Continue reading
Parametric shape optimization “searches the space spanned by the design variables to minimize or maximize some externally defined objective function” (Jiaqin Chen, Vadim Shapiro, Krishnan Suresh and Igor Tsukanov, Spatial Automation Laboratory, University of Wisconsin–Madison, “Parametric and Topological Control in Shape Optimization,” Proceedings of ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference). “In other words, parametric shape optimization is essentially a sizing problem that is a natural extension of parametric computer-aided design.
“The downside of parametric shapes is that they do not provide any explicit information about the geometry or topology of the shape’s boundaries. This, in turn, leads to at least two widely acknowledged difficulties: boundary evaluation may fail, and topological changes in the boundaries may invalidate boundary conditions or the solution procedure.”
Non-parametric optimization, by contrast, operates at the node/element level to derive an optimal structure. It can offer greater design freedom, and can make use of existing CAE models without the need for parameterization. “The main advantage of non-parametric shape optimization is the ease of setup, avoiding tedious parameterization that may be too restrictive with respect to design freedom” (Michael Böhm and Peter Clausen, FE-DESIGN GmbH, “Non-Parametric Shape Optimization in Industrial Context,” PICOF (Problèmes Inverses, Contrôle et Optimisation de Formes) ’12). “One of the major disadvantages on the other hand is that the CAD interpretation of the shape optimization result is not trivial.” Continue reading