Morphology of Free-form Timber Structure Design by Machine Learning Methods oriented by Material and Robotic Fabrication

Robotic arms are increasingly being used as an automation tool in non-standardized fabrication and construction, while the mechanical characteristics can also impact the accomplishment or the accuracy of the components. Timber is regularly used in different scales of a non-standard free-form structure fabricated by the robotic arm. The anisotropic mechanical characteristics of timber constrain the structural morphology. Developing a method of determining the morphology that meets the technical restrictions of the robotic arm and the material properties of timber is the aim of this research. In this paper, taking Centre Pompidou-Metz as a geometric case, glue-laminated timber as the main construction material, LSTM is applied for predicting the shape of the element. The geometric data is transformed into the fabrication data to testify to the kinematic singularities. The limitation of the workspace is derived from the Monte-Carlo method based on the DH model of the robotic arm. The experimental results show that the proposed method is effective in predicting the curves that match the characteristics of timber materials and robotic fabrication constraints.