This study proposed a conditional generative adversarial network (cGAN) model for predicting steel plate deformation based on heating line positions in a line heating process. A database was constructed by performing finite element analysis (FEA) to establish relationships between heating line positions and deformation shapes. Deformation shapes were converted into color map images. Heating line positions were used as conditional labels for training and validating the proposed model.
During the training process, generator and discriminator loss values, along with MSE and R² metrics, converged stably, demonstrating that generated images closely resembled the actual data. Validation results showed that predicted deformation magnitudes had an average relative error of approximately 3% and a maximum error of less than 7%. These findings confirm that the proposed model can effectively predict steel plate deformation shapes based on heating line positions in the line heating process, making it a reliable predictive tool for this application.

We used a construction method using a CNC milling machine, where free-formed molds were made by cutting EPS (Expanded PolyStyrene) foam with the CNC machine, to build free-formed buildings. CNC milling is off-the-shelf technology that can easily cut EPS foam; however its production cost is too high and the time to manufacture an EPS mold is too long. This paper proposes a novel cutting machine with a fast and cost effective mechanism to manufacture EPS concrete molds. Our machine comprises a cutter and Cartesian coordinate type moving mechanism, where the cutter cuts EPS foam using a hotwire in the shape of ‘Π’ and is capable of adjusting its cutting angle in real-time while keeping its cutting width. We proved through cutting experiments on the CNC machine that cutting time was greatly shortened compared to the conventional method and that the resulting concrete mold satisfied manufacturing precision.