Design Automation Lab

Volvo Cars is one of  LiU:s partners

Product development was revolutionized by the introduction of the first CAD software in 1963, a digital drawing board called Sketchpad. The premise of the innovation was to automate a manual, repetitive and erroneous draft-drawing process.  

In hindsight, the inventor of Sketchpad, Ivan Sutherland maximized the technological possibilities available in the 1960s. changing industry as it became vastly easier to draft designs and this spurred major innovations over the next half-century. The premise has changed since the 1960s. There is no longer a struggle in draft design, but many new fields need an innovative touch.

Two of the current industry challenges Design Automation Laboratory study are:

  • Engineering-To-Order (ETO) products and
  • Complex Engineering Products

In order to face the above challenges, we focus currently on following research areas:

  • Knowledge Based Engineering
  • Multidisciplinary Design Optimization
  • Machine Learning
  • System of Systems Engineering


Industry challenges Show/Hide content

Two of the current industry challenges Design Automation Laboratory study are Engineering-To-Order (ETO) products and Complex Engineering Products.

Engineer to Order

The conflicting demands of increased customization of products and decreased costs and delivery times poses challenges in product development and manufacturing companies. Many companies attempt to manage the complexity of customization with platform- and product family design where the physical attribute of the product is broken down into modules to be shared between product variants. Still, product developing, and manufacturing companies are decelerated by repetitive work, misinterpretations, assumptions and uncoordinated processes which hinders the achievement of mass customization where increased customization does not imply higher costs and lead times. Especially for customized products, a significant amount of engineering resources is demanded for each customer order - a main challenge to overcome in mass customization. Another challenge and well-established problem within product development are the lack of knowledge in the early stages of the product development process, where the design freedom is at highest, and vice versa in the later stages, often referred to as the design paradox. Research within product development and the development of computer aided technologies often has this paradox as its vantage point and aims at increasing the knowledge in the early stages in order to produce better products more efficiently.

The design paradoxThe design paradox

Complex Engineering Products

CAD software have been incrementally improved to contain much more than just a digital draft board. CAD is today at the centre of product development where a digital representation of the physical product can be generated early in the product development process. Various characteristics of the product can be computed and analysed by utilizing physics-based tools such as FEM, CFD, dynamic system simulation, etc. Each needing various information contained in CAD. The connection between various fields is itself a major drawback in terms of repetition and extensive lead times. 
For instance, during the design phase the processes of geometry definition in CAD and other disciplines are filled with repetitive operations. One example of this is that CAD geometries are needed for computer aided engineering (CAE). However, the geometries needed for CAE analysis need to be simplified, meshed, pre-processed, analysed and then post-processed, where each stage is typically managed by different individuals in the organization. Furthermore, the process is highly iterative and repetitive, and in any give stage there might be a need to iterate back one or several steps, see Figure 1. Applying various types of digital automation techniques can be of great value to speed up the response time of the experts operating at various stages.

. Example of a highly repetitive part of the CAD-CAE integration. Example of a highly repetitive part of the CAD-CAE integration.

Knowledge Based Engineering

Multidisciplinary Design Optimization

Machine Learning

Systems of Systems Engineering

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