Design for additive manufacturing (DfAM) is gaining increasing attention because of the unique capabilities that additive manufacturing (AM) technologies provide. While they have the ability to produce more complex shapes at no additional cost, AM technologies introduce new constraints. A detailed knowledge of the AM process plays an important role in the design of parts in order to achieve the desired print result. However, research on knowledge management in this area is still limited. The large number of different AM processes, their individual sets of critical parameters and the variation in printing all contribute to a high level of uncertainty in this knowledge domain. Applying AM at the early stages of design projects introduces another source of uncertainty, as requirements are often not well defined at that point. In this paper, a knowledge management system using Bayesian networks (BNs) is proposed to model AM knowledge in cases where there is some uncertainty and fill the knowledge gap between designers and AM technologies. The structure of the proposed model is defined here by introducing the overview layer and detailed information layer. In each layer, different types of nodes and their causal relationships are defined. The system can learn conditional probabilities in the model from different sources of information and inferences can be conducted in both forward and backward directions. To verify the accuracy of the BNs, a sample model for dimensional accuracy in the fused deposition modeling (FDM) process is presented and the results are compared with other methods. A case study is provided to illustrate how the proposed system can help designers with different design questions understand the capabilities of AM processes and find appropriate design and printing solutions.

SummaryFire performance of water‐based plaster systems containing acrylic copolymer binder and non‐flammable mineral and ceramic fillers intended for decorative coating application was determined by cone calorimetric method. The coatings were applied on common building substrate materials used in construction sector, namely, Medium Density Fibre (MDF) boards, Gypsum Boards (GIB) and Stainless Steel (SS), and the different plaster finishes attempted on these substrates were Tierrafino, Suave and Bone China respectively. Group numbers of the different test samples were also evaluated following the Kokkala method. Based on various measurements, especially heat release, smoke evolution, mass loss data and the Group number estimates, it is evident that these organic fire retardant free coatings can offer good fire safety if applied on appropriate substrates like GIB or SS. Copyright © 2016 John Wiley & Sons, Ltd.

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.

Purpose - This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user's choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach - AM process selection methods are reviewed based on decision theory. The authors also examine how the user's preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings - All current studies follow a sequential decision process and focus on an "a priori" articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An "a posteriori" articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value - This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

In New Zealand there has at times been a perceived conflict between research and business, between enquiry and delivery of results, between curiosity and completing. Behind this perception lies a cultural difference between business people and many academic researchers in scientific and engineering disciplines. In business the pressure of cash flow, cost containment, and sales dominates and there is often no time for education and enquiry unless it is linked directly to the business results that day. Longer term strategic vision from the CEO is therefore essential. In academic research, pressure from the 'market' is absent but the tyrannies of ever increasing student numbers, publishing papers and attracting government funding rule. Consequently the response to economic imperatives is very different for businesses and research institutions. Businesses look outward to find solutions in the market, knowing all the while that technological changes outside of their control might also be required. Conversely academic institutions sometimes look inward for solutions, rather than aligning themselves with industry's manufacturing and product innovation challenges. In fact the timescale of these latter, business challenges can be weeks or 1-2 months, whereas the institutional research and funding challenges are measured year by year. So the link between increased funding for manufacturing and materials research, and successful product and technological innovation in business is hard to discern unless specific case studies can be drawn. Here we explore this issue using several case studies from the Materials Accelerator programme, and its Network of companies and research providers across New Zealand.

This chapter contains sections titled: Introduction What is an Organic Transistor? How Does an Organic Transistor Work and How Does it Distinguish Itself from a Conventional One? Basic Logical Integrated Circuits: Ring Oscillators Complex Organic Circuits: the 64-Bit RFID Tag Organic CMOS Circuits Printing Electronics Application and Future Prospects Summary and Prospects Acknowledgements References

This paper presents an organic CMOS transponder carrying 4 bits of digital information in conjunction with another 4 bits for the protocol.The organic CMOS integrated circuits are fabricated on a ~250 mum thick flexible polyester substrate, using methods compatible with printing processes for mass production.

Top-gate n-TFTs and CMOS circuits on plastic substrates based on solution-processed n- and p-channel semiconductors and polymeric dielectrics have been fabricated. These unencapsulated devices exhibit good performance characteristics in ambient.

L'invention concerne un composant electronique, notamment un transpondeur RFID, qui comprend au moins une porte logique (3). Cette porte logique (3) est formee de plusieurs couches qui sont appliquees sur un substrat commun (10) et qui comprennent au moins deux couches electrodes, au moins une couche semi-conductrice (13, 23), notamment organique, appliquee a partir d'un liquide, ainsi qu'une couche isolante (14, 24). Ces couches sont realisees de sorte que la porte logique comprenne au moins deux transistors a effet de champ (1, 2) a structure differente. Les transistors a effet de champ (1, 2) sont formes de plusieurs couches fonctionnelles qui peuvent etre appliquees par impression ou a la raclette sur un substrat support (10).