Organizers: Bengt Lennartson, Qing-Shan Jia, Maria Pia Fanti, Peter B. Luh, Jingang Yi, Karinne Ramirez-Amaro
Site: Mexico City (In person)
Date/Time: Aug 20, 2022, 9:00-15:00
Abstract: The enormous interest in artificial intelligence and especially machine learning (ML) among scientists in different research fields has recently also influenced the focus of our CASE conference. This is manifested by the main themes at IEEE CASE 2018-2021: Knowledge-based Automation, Smart Automation, Automation Analytics, and Data-Driven Automation. Since learning is such an important tool in many automation solutions, including data-based model generation, online optimization, and adaptive control, it is crucial to increase our activities in this field even further, to become an important player in the tough scientific race around ML that is going on right now.
The goal of this workshop is therefore to create a deeper interest and understanding of ML, but also to identify niche areas of ML in automation, where our research community should take the lead. More specifically, we want to present some interesting ongoing research activities, but also to discuss and propose what we believe are important research directions where automation can play an important role in this dynamic research area.
The presentations in this workshop will be given by members of a recently created AdHoc on Machine Learning for Automation. This AdHoc is focused on how to strengthen research activities, but also organization and infrastructure around ML research within automation. The workshop will therefore conclude with an open discussion to get interesting inputs for future activities within this challenging research field.
Abstract: Quality and productivity are critical for additive manufacturing (AM). With increased availability of AM product data, Machine Learning for AM (ML4AM) has become a viable strategy for knowledge discovery and performance enhancement. Although general-purpose machine learning and geometric analysis in computer vision have been extensively studied, ML4AM differs in many ways. Shape accuracy control in AM needs a new engineering-informed data analytics framework to facilitate efficient machine learning of AM product data. Furthermore, new AM processes such as wire arc additive manufacturing (WAAM) introduce additional challenges. This workshop introduces recently development in ML4AM including control for WAAM.
WS3: AI for Efficiency and Sustainability in Industrial Disassembly Processes
Organizers: Xiwang Guo, Jiacun Wang
Site: Mexico City (Online)
Date/Time:: Aug 20, 2022, 9:00-13:00
Abstract: Efficiency and sustainability will be the key for the future factory, whose main focus will be on
efficient and sustainable industrial processes. A sustainable production, an efficient use of the
resources, and an increase in the recovered and reused products will be crucial to reduce the impact
of the production on the environment, in compliance with the upcoming Industry 5.0 paradigm.
Artificial intelligence (AI) and robotics are leading to deep workplace innovation, optimizing
human-machine interactions, and giving more importance to workers. But the environmental goals
can only be achieved by rethinking the production processes in order to limit the environmental
impact. Disassembly is an industrial process that will have to be continuously optimized to increase
efficiency and sustainability in years to come. Disassembly extracts valuable components/materials
from end-of-life goods for reuse and recycling. It is also used in product refurbishment when
products are restored to full manufacturer conditions by running quality tests and replacing broken
or defective parts. Refurbishing products is a great opportunity for sustainability as it gives new
life to used products instead of producing new ones, thereby providing consumers with quality
products at an affordable price. Statistics say that the refurbished market for consumer electronics
is estimated to be $10 billion. Disassembly consists of a series of tasks performed in lines made up
of workstations where workers may be assisted by robots. Making these lines as efficient and
sustainable as possible includes the design, the optimization, and the improvement of the
collaborations between workers and machines. Artificial Intelligence (AI) can help deal with the
complexity of these problems to find and implement solutions that increase efficiency and reduce
the impact of production on the environment. This Workshop aims to collect the latest research and
achievements and discuss the progress regarding advanced AI techniques for optimal industrial
WS4: Benchmarking Coaxial Rotor Systems to Optimize Performance in Autonomous Applications - A Tutorial
Organizers: Minas Liarokapis, Joao Buzzatto
Site: Mexico City (Online)
Date/Time:: Aug 20, 2022, 9:00-13:00
Abstract: In the UAV field, the efforts to develop drones with more payload and flight time capacity are constant. Coaxial
multirotor drones offer high payload capacity in a relatively small vehicle footprint . However, compared to
regular 'flat' multirotors, they exhibit a much lower efficiency. The content covered in this proposed tutorial
is based on a very recent work of the authors where they developed a control allocation method in which
experimental results showed an increase in efficiency of up to 11% compared to the current state-of-the-art .
Additionally, the tutorial also covers the operation of an open-source benchmarking platform developed by the
authors with the purpose of testing and optimizing the performance of coaxial rotor systems. Therefore, the
tutorial will provide the participants with all the tools needed to perform experiments, develop, and implement
control allocation methods to improve the efficiency of coaxial rotor systems in autonomous applications.
Abstract: AI-based Smart Manufacturing Systems (AISMS) incorporates various technologies, i.e., Internet of Things (IoT), big data analytics, system modeling, and Artificial Intelligence (AI). Such technologies are permeating different aspects of manufacturing industry and make it smart and capable of addressing challenges such as interoperability, decentralization, distributed control, real-time manufacturing process control, service orientation, and maintenance optimization. As one of the most sophisticated manufacturing industries, semiconductor industry has been actively adopting AISMS to boost productivities.
This is a half-day workshop on semiconductor smart manufacturing technology workshop. The purpose of this workshop is to share with IEEE communities the recent advancement and development of semiconductor smart manufacturing technologies and relevant applications ranging from semiconductor tools scheduling, AI based defect detection and classification, smart equipment dispatch, intelligent process control, etc. The workshop aims to provide technical discussion forum for researchers from different fields and promote interdisciplinary and multidisciplinary research collaboration.
Abstract: Multi-robot teams have been used in a wide range of applications, including surveillance, inspection, rescue, automation, and logistics. Due to the variety of critical components in these applications, the collaboration between agents in the robot team can quickly become a challenging problem, particularly when there is a variety of hardware, battery life, size, and functionalities of the robots that are moving in a dynamic environment. Because the robots are working in an dynamic environment, they need to dynamically change their behaviors to adapt to the state of the environment in a way that is fully coupled to the type of agent. For example, depending on the robot, some environmental constraints can be waived or become more restricted. The tasks need to be assigned and managed precisely to achieve the goals while minimizing the execution time and energy costs and avoiding collisions.
Due to the collaboration among autonomous robots, robot team establishment introduce new requirements, new challenges, and new solutions to real-world problems. While many heterogeneous and autonomous robots are organized as a team to accomplish a mission, assigning a proper task to each robot, and evaluating their performance before acting is essential. Optimal task assignment can avoid failures and increase operating efficiency while the robots are executing their mission.
Role-Based Collaboration (RBC) is a flexible strategy that can facilitate agent collaboration between agents in centralized or decentralized management by using the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. Research shows that the RBC methodology can be used to manage a robot team's performance by optimizing task allocations. However, a critical part of RBC is the role assignment which requires a pertinent evaluation matrix, i.e., Q, that reflects the qualification of each agent for each role.
This workshop will discuss related methodologies including RBC approaches and the E-CARGO model.