This paper presents a Digital Twin (DT) of a 6G communications system testbed that integrates two robotic manipulators with a high-precision optical infrared tracking system in Unreal Engine 5. Practical details of the setup and implementation insights provide valuable guidance for users aiming to replicate such systems, an endeavor that is crucial to advancing DT applications within the scientific community. Key topics discussed include video streaming, integration within the Robot Operating System 2 (ROS 2), and bidirectional communication. The insights provided are intended to support the development and deployment of DTs in robotics and automation research.

Industry 4.0 is a blend of the hyper-connected digital industry within two world of Information Technology (IT) and Operational Technology (OT). With this amalgamate opportunity, smart manufacturing involves production assets with the manufacturing equipment having its own intelligence, while the system-wide intelligence is provided by the cyber layer. However Smart manufacturing now becomes one of the prime targets of cyber threats due to vulnerabilities in the existing process of operation. Since smart manufacturing covers a vast area of production industries from cyber physical system to additive manufacturing, to autonomous vehicles, to cloud based IIoT (Industrial IoT), to robotic production, cyber threat stands out with this regard questioning about how to connect manufacturing resources by network, how to integrate a whole process chain for a factory production etc. Cybersecurity confidentiality, integrity and availability expose their essential existence for the proper operational thread model known as digital thread ensuring secure manufacturing. In this work, a literature survey is presented from the existing threat models, attack vectors and future challenges over the digital thread of smart manufacturing.

Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems. Semantic models are used increasingly to link these datasets from different stages of the IoT systems life-cycle together and to automatically configure the AI modelling pipelines. This combination of semantic models with AI pipelines running on external datasets raises unique challenges particular if rolled out at scale. Within this paper we will discuss the unique requirements of applying semantic graphs to automate Digital Twins in different practical use cases. We will introduce the benchmark dataset DTBM that reflects these characteristics and look into the scaling challenges of different knowledge graph technologies. Based on these insights we will propose a reference architecture that is in-use in multiple products in IBM and derive lessons learned for scaling knowledge graphs for configuring AI models for Digital Twins.

When digital transformation is discussed, technology is usually not far behind. Whether it's Internet of Things (IoT), cloud, or artificial intelligence (just to name a few), tech is changing how organizations around the world are doing business. While there's no question that technology goes hand-in-hand with digital transformation, there are other essential considerations that must come first in a digital transformation strategy. These include identifying value-driven business outcomes and developing a culture of change and collaboration. In our State of Industrial Digital Transformation report, our research analysts describe DX technologies as "levers or tools to support business value-oriented initiatives."

Let's face it: making decisions is hard. Naturally, with decisions come mistakes, and mistakes are both costly and painful. "It's good to learn from your mistakes. It's better to learn from other people's mistakes." Failing in real life is expensive, but failing in the virtual world is cheap.

Oqton keeps adding new intelligent features to their Artificial Intelligence powered platform, FactoryOS. Oqton is building partnerships with machine manufacturer that open their API, and has announced their partnership with EOS. We believe such system is key in moving toward a more automated workflow, and we are in the process of testing it out with our own machines. We hope in the future to be able to plug into it deep learning based modules for live defect analysis. Click here to find out more about this technology.

Aerospace executives can now optimize manufacturing processes by leveraging artificial intelligence (AI) with high-performance computing (HPC) technologies and the digital thread. A digital thread follows the lifecycle of a product from design inception through engineering and product lifecycle management, to manufacturing instructions, supply chain management, and through to service events. You'll be able to enhance the aerospace design process to protect budgets, avoid static production rates, and nudge your business ahead of competitors. Even better, as aerospace design becomes more complex, AI can help keep your business ahead of the innovation curve. I recently chatted with a vice president of IT Infrastructure at a large manufacturing company and his message was very clear: HPC technologies that support modeling and simulation are very important to his business users.

Electronics and the components that power them are more complex and advanced than ever. With these products an integral part of our daily lives, their reliability has become nothing less than mission-critical. As the demand for components accelerates, it is important that quality is not compromised under the pressure to meet quantity requirements. Otherwise we're going to be seeing a lot of recalls at the end of the digital supply chain. According to NHTSA data from 2007 to 2016, the automotive industry encountered this very issue, with car recalls due to electronics increasing threefold. To ensure that product reliability keeps pace with the complexity and sheer volume of today's electronics, a novel and holistic approach must be implemented across the supply chain: building a digital thread with Machine Learning and IoT analytics.

Ok, I'm going to say something unpopular now. This is a message for the industrial companies, the ones who build, operate, maintain, and rely on large, complex assets, processes, capital equipment, systems, and machinery. Some of these assets have been used by companies for 20 years. If you have been reading the popular press, you have been fed a line and a vision of a bright, beautiful, and imminent future. You've been told about the coming of Industry 4.0.

We are living in a society with more moving parts and pieces than ever before. Not only is there more variation in those individual products; there is more customization. From car doors to knee replacement hardware, nearly every product can be customized. However different the product, all can benefit from technology that standardizes processes and efficiently delivers results. Augmented reality (AR) technology, for example, offers some of the most powerful manufacturing technology solutions.