Smart Machining is a general term that refers to integrating intelligence to the machining processes in order to improve quality, increase machining performance, and allow reliable unattended operations. From another perspective, Smart Machining aims to reduce waste and simultaneously increase the agility of system to respond to design variations. In this context, Smart Machining not only optimizes individual machining processes, but also defines the optimum combination of machining processes to manufacture parts in order to reduce the manufacturing lead time. Cutting tools are one of the most important components of each machining systems and they play an important role in success or failure of a machining operation. Therefore, bringing intelligence to cutting tools is also a promising challenge that must be undertaken by researchers. This invited session would like to encourage researchers to present their novel ideas regarding Smart Machining and Smart Cutting Tools. The preferred topics include but are not limited to smart flexible manufacturing systems, integrating knowledge to the machining systems, smart cutting tools with embedded sensors to measure cutting force or temperature etc.

Autonomous vehicles requiring high technologies such as vision, sensing, and control are increasingly needed in various industrial fields within the context of Industry 4.0. In the field of manufacturing, autonomous vehicles such as autonomous mobile robots and automated guided vehicles are being applied to the delivery, handling, and inspection of products to improve the productivity and flexibility. This trend has motivated rapidly growing research on industrial applications of autonomous vehicles. This session is dedicated for a deep dive on the up to date research progress on the design, modeling, control, and operation management aspects of the autonomous vehicles for intelligent manufacturing systems. Prospective authors are invited to present state-of-the-art technologies, challenges, and opportunities in this emerging field.

Digital Twins are nowadays gaining momentum in smart manufacturing context. They are generally intended as virtual replicas of physical systems, capable to run on different simulation disciplines that synchronize the virtual and real systems, thanks to sensed data and connected smart devices, mathematical models and real time data elaboration. More specifically, they blend the virtual models and capabilities of Cyber-Physical Systems (CPS) (i.e. computing, communication, data collection, intelligent sensoring), with the potential of big data analysis and the pervasive connection allowed by Industrial Internet-of-Things, to have an advanced real-time perception of the physical assets and a prompt prediction of their behaviour. Digital Twins promise high benefits when used to support the design, management, monitoring, control, performance evaluation and optimization of manufacturing assets (i.e. manufactured products, production equipment and production systems). Moreover, they open the way to extensive simulations under various scenarios and conditions, with the possibility to be up-dated in real-time, to detect anomalies and to conduct accurate diagnostics and prognostics. Eventually, they open new possibilities to make a joint production and maintenance scheduling, relevant to assure more robust performances in stochastic environments, as well as to assess and optimize performances at different levels, from components and machines to production systems.