Design for manufacturing and assembly (DfMA) is an engineering methodology which aims to increase ease of manufacture and efficiency of assembly by considering manufacturing and assembly constraints in the design process. However, current DfMA approaches in the construction sector are not automated enough to identify the design features that may cause project delay in real time. This leads to longer design cycle. Also, current scheduling algorithms rely on human intervention to generate activity network from a design output. Addressing these inefficiencies, we propose an interpretative machining learning model to predict the construction duration given a design output. More importantly, the same model identifies the design features that may cause the most delay in the project. The model is trained on a residential design dataset with various features, such as layout, geometry, and element typology. The output of the model is the project duration and an importance map, indicating the influence each feature of the given design has on the total project duration. The results from this model can considerably reduce the design cycle by supporting architects to create fabrication and assembly aware design even when they have little knowledge of production and assembly processes. This model will contribute to a novel computational approach for DfMA.

@en

More widespread use of industrialized construction (IC) is hampered by the high capital cost of advanced production facilities paired with low profit margins. A novel service-oriented cloud manufacturing (CMfg) model could in theory increase utilization and profitability of distributed production facilities. However, little research has investigated how IC can benefit from the CMfg model. This paper examines opportunities and challenges of applying CMfg for IC. First, an adapted model of CMfg for construction is developed based on a literature review. Second, four possible scenarios for applying this adapted CMfg model are designed. Finally, an evaluation is performed through a survey among 25 practitioners and 12 in-depth interviews with industry experts. The paper assesses the desirability and categorizes the benefits and barriers of such a CMfg platform for IC. The results suggest that CMfg could enhance the design quality, support IC suitability assessment for project developers and lower financial risks for off-site manufacturers.@en

A library of prefabricated parts and assemblies, i.e. module library, can help a firm in the construction industry transition to a more industrialized and product-oriented approach. However, existing approaches to manage such libraries are oriented around single-use projects. There is need for a more flexible data structure to support storage, analysis and reuse of design information. This paper proposes a graph-based approach to develop a module library. The approach includes a graph representation of modules, a graph database development, and a graph-based similarity analysis. The proposed approach is validated using a prefabricated timber panel system via a web-based application. Implementation demonstrates a more efficient process for bill of material generation and identifying the impacts of design changes.

@en

The current digital fabrication workflow requires many iterations between design and manufacturing. Automated manufacturability analysis can reduce the number of iterations at the design stage. However, existing approaches that leverage design for manufacturing and assembly (DfMA) do not consider detailed product features and production capabilities. To address this limitation, this paper utilizes an ontology-based approach to connect design and manufacturing knowledge. The developed manufacturability analysis system (MAS) involves semantic reasoning to analyze manufacturability by combining feature-based modelling, production capability modelling and manufacturing rules. The system was tested on a timber panelized project to demonstrate complex manufacturability analysis capability. The testing proves that the system could provide real-time feedback to the designers, leading to fewer design iterations. Thus, the paper is a first step towards automated fabrication-aware design and the results from the study lay the foundation for future research on connecting knowledge for interdisciplinary rule checking

@en

Cloud manufacturing is an emerging manufacturing paradigm to enable rapid production for mass customization. Industrialized construction shares a similar production environment with manufacturing products, so it has a great potential to utilize the paradigm. Previous studies never examined cloud manufacturing in the construction context. This work takes the industrial difference into account and proposes a cloud manufacturing framework by ontology modeling. Three ontologies, including ifcOWL, OPW, and OWL-S, are linked to support the design to the manufacturing process of a building project. The framework benefits the design data and manufacturing data integration, and enhances the resource sharing by semantic web service.

@en