Ayoub Lharchi: Information Modeling for Assembly Design, Sequencing and Optimization

This project aims to investigate the different aspects of the planning and design for assembly in an architectural context. The focus lays specifically within timber construction and has the ambition of laying the foundations of a novel computational approach for Design for Assembly (DfA). The approach would enable assembly decisions in the early design phases using integrated digital assembly models. 
The research examines existing DfA principles in other disciplines such as mechanical engineering and product design and speculate about their transferability into an architectural scale. By combining computer science techniques and design practices from other disciplines, this project defines in a first step a scheme by which professionals can describe, analyse and communicate assembly information. Based on this, we developed a set of computational methods and tools allowing a complete simulation of the assembly procedure and detection of any problems or collisions, and at the same time Assisted Assembly Planning (AAP) workflows.

The expected outcome of this work can be described as follows: First, a contribution towards considerations of assembly in architectural design and how it can be enabled in the early phases. This would be translated into a set of recommendations and guidelines to design for assembly in   an architectural context. Furthermore, we present a novel approach for information modeling where assembly takes a central role in the model along with other traditional embedded data. This would manifest in a general framework and a digital model specifications. This framework, entitled “Assembly Information Modeling” is implemented as a central digital model containing the structure architectural design, construction details, three-dimensional representations, assembly sequences, issues management and other necessary data for a seamless assembly procedure.

Funding
This project is part of the Innochain Training Network. It has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642877.