Search

The development of experiential learning methodologies is gaining attention, due to its contributions to enhancing education quality. It focuses on developing competencies, and build-up added values, such as creative and critical thinking skills, with the aim of improving the quality of learning. The interdisciplinary mechatronics field accommodates a coherent interactive concurrent design process that facilitates innovation and develops the desired skills by adopting experiential learning approaches. This educational learning process is motivated by implementation, assessment, and reflections. This requires synergizing cognition, perception, and behavior with experience sharing and evaluation. Furthermore, it is supported by knowledge accumulation. The learning process with active student's engagement (participation and investigation) is integrated with experimental systems that are developed to facilitate experiential learning supported by properly designed lectures, laboratory experiments, and integrated with course projects. This paper aims to enhance education, learning quality, and contribute to the learning process, while stimulating creative and critical thinking skills. The paper has adopted a student-centered learning approach and focuses on developing training tools to improve the hands-on experience and integrate it with project-based learning. The developed experimental systems have their learning indicators where students acquire knowledge and learn the target skills through involvement in the process. This is inspired by collaborative knowledge sharing, brainstorming, and interactive discussions. The learning outcomes from lectures and laboratory experiments are synergized with the project-based learning approach to yield the desired promising results and exhibit the value of learning. The effectiveness of the developed experimental systems along with the adopted project-based learning approach is demonstrated and evaluated during laboratory sessions supporting different courses at Sanyo-Onoda City University, Yamaguchi, Japan, and at the American University in Cairo.

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach that utilizes Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to tackle a critical challenge: detecting defects in wrapped film products. With their delicate and reflective film wound around a core material, these products present formidable hurdles for conventional visual inspection systems. The complex task of identifying defects, such as unwound or protruding areas, remains a daunting endeavor. Despite the power of commercial image recognition systems, they struggle to capture anomalies within wrap film products. Our research methodology achieved a 90% defect detection accuracy, establishing its practical significance compared with existing methods. We introduce a pioneering methodology centered on covariance vectors extracted from latent variables, a product of a Variational Autoencoder (VAE). These covariance vectors serve as feature vectors for training a specialized One-Class SVM (OCSVM), a key component of our approach. Unlike conventional practices, our OCSVM does not require images containing defects for training; it uses defect-free images, thus circumventing the challenge of acquiring sufficient defect samples. We compare our methodology against feature vectors derived from the fully connected layers of established CNN models, AlexNet and VGG19, offering a comprehensive benchmarking perspective. Our research represents a significant advancement in defect detection technology. By harnessing the latent variable covariance vectors from a VAE encoder, our approach provides a unique solution to the challenges faced by commercial image recognition systems. These advancements in our study have the potential to revolutionize quality control mechanisms within manufacturing industries, offering a brighter future for product integrity and customer satisfaction. [ABSTRACT FROM AUTHOR]