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.

With time-to-market in the automotive industry steadily decreasing, the demand for additive manufactured prototyping components is higher than ever. However, in order to make larger 3D printed volumes tangible, process chains still need to be optimized and further developed in regards to output quantity, production speed, and economic viability, according to a new study by German multinational automotive firm BMW. Having identified a need to further optimize and increase the efficiency of additive manufacturing technologies and their process chains, BMW has conducted research into the complexity and economical value of Artificial Intelligence (AI) for the automated identification of 3D printed parts. The paper outlines the state-of-play of current available additive manufacturing process chains, the complexities of using AI for part recognition, and the economic viability of using AI-based platforms such as AM-VISION, an automated machine learning part recognition system from Dutch 3D printing, post-processing and automation firm AM-Flow, to further industrialize overall 3D printing process chains. The research paper, which has been compiled by authors from BMW, AM-Flow and the University of Duisburg-Essen (UDE), highlights how additive manufacturing's technological progress is enabling higher production speeds, increased choice of materials, and adjustable robust mechanical properties within parts that resemble those of conventional products.

The production of lithium-ion battery cells is characterized by a high degree of complexity due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks as failure analysis challenging. In this paper, a new method is presented that includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. Special algorithms are presented that help detect and resolve inconsistencies between the expert-provided parameters which are bound to occur when collecting knowledge from several process experts. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production and its application for root cause analysis.

As antivirus and machine learning-based malware detection have increased their effectiveness in detecting file-based attacks, adversaries have migrated to "living off the land" techniques to bypass modern security software. This involves executing system tools preinstalled with the operating system or commonly brought in by administrators to perform tasks like automating IT administrative tasks, running scripts on a regular basis, executing code on remote systems, and much more. These binaries are inherently benign and commonly used in most environments, so attackers can trivially bypass most first-line defenses simply by blending in with the noise of what's executing on a recurring basis. Detecting patterns like this post-compromise requires sifting through millions of events with no clear starting point. In response, security researchers have begun authoring detectors to target suspicious parent-child process chains.

This paper presents the first results of the research into AI-based support of the room configuration process during the early design phases in architecture. Room configuration (also: room layout or space layout) is an essential stage of the initial design phase: its results are crucial for user-friendliness and success of the planned utilization of the architectural object. Our approach takes into account different possible actions of the configuration process, such as adding, removing, or (re)assigning of the room type. Its mode of operation is based on specific process chain clusters, where each cluster represents a contextual subset of previous configuration steps and provides a recurrent neural network trained on this cluster data only to suggest the next step, and a case base that is used to determine if the current process chain belongs to this cluster. The most similar cluster then tries to suggest the next step of the process. The approach is implemented in a distributed CBR framework for support of early conceptual design in architecture and was evaluated with a high number of process chain queries to prove its general suitability.

Not a day goes by when one is not bombarded by the latest innovations around artificial intelligence (AI), robotics and machine learning (ML). The inflationary use of these terms makes many people question if they are simply catchy buzzwords ― part of a short-lived market hype. On the other hand, expectations concerning the capabilities of AI and robotics are at an all-time high. From the ultimate AI-built utopia to Skynet apocalypse ― everything seems possible. Time for a grounded look at what AI, ML and robotics actually can and should do in the area of finance process automation.