The 'what' and 'how' of learning in design, invited paper

Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Optimization for automated assembly of puzzles

The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets

Design reuse research : a computational perspective

This paper gives an overview of some computer based systems that focus on supporting engineering design reuse. Design reuse is considered here to reflect the utilisation of any knowledge gained from a design activity and not just past designs of artefacts. A design reuse process model, containing three main processes and six knowledge components, is used as a basis to identify the main areas of contribution from the systems. From this it can be concluded that while reuse libraries and design by reuse has received most attention, design for reuse, domain exploration and five of the other knowledge components lack research effort

Distraction Osteogenesis in Oral and Craniomaxillofacial Reconstructive Surgery

Distraction osteogenesis (DO) is a tissue engineering method to regenerate new bone. The application of DO in the field of oral and craniomaxillofacial surgery has provided a promising alternative as it can be integrated with conventional surgical technique for bone lengthening or expansion. This technique has the advantages of providing superior amount of bone lengthening thus eliminating the need of autogenous graft and donor site morbidity, can be applied in young patients and allows simultaneous expansion of the surrounding soft tissues. In this chapter, we provide a comprehensive overview of the background history and development of DO which is based on Ilizarov technique, along with its basic principles, indications, classification of DO devices and protocol in craniomaxillofacial bone lengthening or expansion. Its clinical applications which include alveolar DO, mandible DO, maxilla DO, transport DO and craniofacial DO are clarified. This technique however requires proper understanding of clinical and technical components to avoid potential complications which include relapse, infection, adjacent structure injury, device failure and other complications. The emerging results of research and advances in DO are further elaborated at the end of this chapter