Material design

Material design

Material design refers to the process of developing new materials with specific properties through the use of theories, computations, and theoretical design. This involves predicting the constituents, structures, and properties of a new material or developing a new material with specified properties. Material design variables can include the Young modulus, yield stress, material density, thermal conductivity, specific heat coefficient, and more. The selection of the most appropriate material design is dependent on the specific requirements of the target application.From: Micro- and MacroMechanical Properties of Materials [2013], Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity [2018], Analysis and Optimum Design of Metal Structures [2020], Biodegradable Systems in Tissue Engineering and Regenerative Medicine [2019]

New Product Development

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Published in Titus De Silva, Integrating Business Management Processes, 2020

Titus De Silva

These principles are considered in the design analysis stage, which includes three parameters for design: (a) process design; (b) material design; and (c) energy consumption design. Process design focuses on reducing the energy consumption during processing and minimising waste and pollution. Material design takes into consideration the selection and the use of raw materials to minimise hazardous waste, pollution and the amount of raw materials used. Energy consumption design is associated with the selection of materials and processes that minimise energy needs during manufacture and use (Deniz, 2002; Gungor & Gupta, 1999).

Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity

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Published in Science and Technology of Advanced Materials, 2018

Takeshi Onishi, Takuya Kadohira, Ikumu Watanabe

Material design is a process of developing new materials with specific properties. In most practical cases, the desired process cannot be envisioned, and instead it is constructed by trial and error. In this approach, a trial is a time-consuming experiment. Minimizing the number of trials is critical for an efficient development. On the contrary, such efficient development is challenging because 1) The relation between a process and a property is unclear and indirect; and 2) The search space (the set of possible processes) is too large to look up.

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Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity