Comfort weight analysis for optimising ergonomic cabin design of a drilling rig machine using three-dimensional human simulation

Ergonomics in product design enhance user comfort, correlating with increased efficiency and usability between the user and the product. Anthropometry is an important part of creating ergonomic design that correlates with human body size. By utilising anthropometry data, this research aims to analyse the cabin's comfort in drilling rig machine by creating 3D simulations for human-machine interaction. The comfort values will be assessed using Jack software. An experimental orthogonal design is applied to generate 81 optimal combination samples. The sample with the optimal comfort value can also be obtained. Following that, the extreme learning machine and mean impact value (ELM-MIV) algorithm is employed to evaluate the comfort weight analysis of each influencing factor. The accuracy value of the prediction model assessed using mean squared error (MSE) is 0.004, and the squared correlation coefficient (R2) value is 0.996. It can be inferred that the prediction error is apparently minimal, indicating the reliability of ELM implementation in data training. It will optimise the redesign of a drilling rig machine cabin to solve work-related health issues and improve operator comfort and efficiency.