With product customisation and emerging business opportunities, small and medium manufacturing enterprises (SMEs) must find ways to collaborate and share competency in a trustable manner to survive a turbulent market. Therefore, service industry turns to the manufacturing industry and SMEs migrate to cloud manufacturing (CM) and ubiquitous manufacturing. However, existing platforms use centralised networking, which suffers from security, scalability and big-data problems. In this paper, we propose a blockchain-based platform as a trustable network to eradicate third-party problems, which can improve the scalability, security and big-data problems for SMEs. Our proposed platform is developed based on a consortium blockchain which provides a peer-to-peer communication network between the end user and the service provider. We improve existing consensus mechanism and communication protocol based on a cyber-physical system (CPS), via an autonomous agent. Firstly, we provide a review of cloud manufacturing, ubiquitous manufacturing and blockchain-based manufacturing approaches by highlighting the main problems. Then, the proposed platform, blockchain ubiquitous manufacturing (BCUM), is explained, based on its architecture, consensus algorithm and CPS, with the help of autonomous agent communication. The proposed platform has been developed for 3D printing companies which are geographically distributed and tested based on network performance and three practical scenarios.

The new paradigm of digital manufacturing and the concept of Industry 4.0 has led to the integration of recent manufacturing advances with modern information and communication technologies. Therefore, digital simulation tools fused into production systems can improve time and cost-effectiveness and enable faster, more flexible, and more efficient processes to produce higher-quality goods. The advancement of digital simulation with sensory data may support the credibility of production systems and improve the efficiency of production planning and execution processes. In this paper, an approach is proposed to develop a Digital Twin of production systems in order to optimize the planning and commissioning process. The proposed virtual cell interacts with the physical system with the help of different Digital Manufacturing Tools (DMT), which allows for the testing of various programs in a different scenario to check for any shortcomings before it is implemented on the physical system. Case studies from the different production systems are demonstrated to realize the feasibility of the proposed approach.

A manufacturing model is an information and knowledge model that describes the manufacturing capability of a particular organization. This work contributes to the area of information and knowledge structure to support manufacturing decisions. The structures of the manufacturing model have been defined to achieve suitable access to, and maintenance of, the manufacturing knowledge. Emphasis has been made on investigating a suitable manufacturing model structure in order to readily access manufacturing knowledge related to process planning activities. The aim of this research was to design a manufacturing knowledge model (MKM) and demonstrate its functionality through experimental software. This paper presents requirements for a new MKM, proposes its structure and describes its design.

During the last decade, industrial robot installations have increased greatly, contributing to higher energy consumption at the process level. To align with Industry 4.0 objectives to reduce energy consumption, further Industrial Internet of Things, and advance Smart Manufacturing, this paper explores energy consumption optimisation of process-level industrial robots through Design and Analysis of Experiment methodologies. The operating parameters of a Kawasaki ZZX130L, 6 DOF model industrial robot, investigated herein are speed, acceleration, payload, and temperature using a linear factorial experiment analysis. It is this paper’s goal to determine which of the explored parameters contribute most to energy consumption. Statistical analysis of data was conducted using Minitab 19, an industry standard tool, and it was found that linear speed and acceleration contributed to nearly 95% of energy consumption in any of the first three joints of the Kawasaki robot, with the other factors, payload and temperature, contributing the rest. It remains important to explore other factors contributing to EC and seek to minimise them in any way possible.

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

Robotics and automation have been a growing area within K–12 educational institutions for the past decade. Across secondary educational institutions, students are introduced to robotics in classes, after-school clubs, and competition leagues through various educational platforms, vendors, and kits. Robotics was initially implemented in schools to help drive more interest in STEM through hands-on application of mechanical, electrical, structural, and computer engineering concepts. Recently, the trend of K–12 robotics has become very niche, focusing more on mobile robotics or robotics competitions. Because of this trend, students have limited exposure to emerging technological advances, such as those found in Industry 4.0. Exciting technological areas, such as digital twins, are not covered in curricula, and this lack of exposure negatively influences the direction of student interest in the “T” and “E” of STEM, with many students never pursuing computer science, technology, or robotics in higher education. The primary goal of this research is to provide a methodology to expose secondary students to Industry 4.0 technologies by leveraging accessible technologies, such as Unity and the Robot Operating System (ROS), to develop a low-cost, high-fidelity digital twin of a pick-and-place robot in a smart warehouse operation. This digital twin prototype will help students to learn about Industry 4.0 trends, such as next-generation automation systems, digital twins, digital manufacturing, intelligent automation, and additive manufacturing, using ROS–Unity integration and hardware accessible to secondary schools to simulate a pick-and-place robotic application. By harnessing the accessibility of Unity and ROS to create a low-cost digital twin prototype for a secondary school, this research has a secondary goal of improving the pipeline of students interested in pursuing STEM-related learning in higher education, thereby ensuring a future STEM workforce that can research, design, develop, operate, and maintain the systems and technologies of Industry 4.0.