This study explores the important task of validating data exchange between a control box, a Programmable Logic Controller (PLC), and a robot in an industrial setting. To achieve this, we adopt a unique approach utilizing both a virtual PLC simulator and an actual PLC device. We introduce an innovative industrial communication module to facilitate the efficient collection and storage of data among these interconnected entities. The main aim of this inquiry is to examine the implementation of Ethernet/IP (EIP), a relatively new addition to the industrial network scenery. It was designed using ODVA’s Common Industrial Protocol (CIP™). The Costumed real-time data communication module was programmed in C++ for the Linux Debian platform and elegantly demonstrates the impressive versatility of EIP as a means for effective data transfer in an industrial environment. The study’s findings provide valuable insights into Ethernet/IP’s functionalities and capabilities in industrial networks, bringing attention to its possible applications in industrial robotics. By connecting theoretical knowledge and practical implementation, this research makes a significant contribution to the continued development of industrial communication systems, ultimately improving the efficiency and effectiveness of automation processes.

Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured.

Pneumatic tires1 are used in a wide range of industries. However, they are difficult to design and rely on the knowledge of experienced designers. To aid in the design of pneumatic tires, this paper suggests the use of Generative Adversarial Networks (GAN) models. The 2000 created images for training were used after removing highly sim-ilar images using Mean Squared Error (MSE) and Structural Similarity Index (SSIM). To find the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, Deep Convolu-tional Generative Adversarial Networks (DCGAN), StyleGANv2 ADA and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, and StyleGAN v2-ADA models showed that the circle shape was distorted and did not maintain a consistent pattern, but ProjectedGAN showed that the circle remained consistent and the pattern was less distorted than the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed best among several techniques that measure the difference be-tween the generated and actual image distributions.