Name: Workshop on Challenges and Novel Approaches for Industry 4.0
The workshop was jointly organised by Innovation Booster Robotics and Innovation Booster – Databooster
Date: March 16th
Theme: Industry 4.0 with subtopics such as smart factory, cobotics and AI
The event was organised on premises of Switzerland Innovation Park Biel/Bienne. As this is a center of innovation, premises of a smart-factory and cobotics center, the symbolic meaning of this location resonated well with the event.
The program allowed participants to receive an update on insights and challenges within Industry 4.0 by key note speakers, engage in an ideation workshop to move from exploring challenges to identifying potential solutions, and plenty of networking opportunities.
- 09:00-10:00 registration + networking breakfast
- 10:00-10:15 welcome by the boosters and promotion of calls for proposals
- 10:15-12:00 keynote presentations
- 12:00-13:30 networking lunch
- 13:30-16:30 ideation workshop + networking break
- 16:30-17:00 wrap-up and promotion of calls for proposals, satisfaction survey
We hosted 5 excellent speakers:
Prof. Dr. Sarah Dégallier Rochat, Lead of Humane Digital Transformation at Bern University of Applied Sciences delivered a presentation on Robots as tools: New approaches to robot integration for SMEs. She highlighted that Swiss SMEs are the makers and can turn workers into makers via the concept of augmented worker.
Dr. James Hermus Postdoctoral Researcher in the Learning Algorithms and Systems (LASA) Laboratory at EPFL delivered a presentation on Real Time Adaptive Systems for Human Robot Collaboration. He talked about methods to teach robots to perform skills with the level of dexterity displayed by humans in similar tasks.
Philipp Schmid Head Industry 4.0 & Machine Learning at CSEM (Swiss Center for Electronics and Microtechnology) delivered a presentation on Industry 4.0 and Machine Learning. He highlighted the need of how machine learning and robots can automate processes at industrial sites and hence increase future of smart-factories.
Dr. Renaud Dubé CTO and Co-Founder of Sevensense Robotics delivered a presentation on Visual AI: Empowering a new generation of mobile robots. We learned about robots visual capabilities and challenges: lighting and viewpoints changes and understanding semantics.
Prof. Dr Marc Pollefeys Professor of Computer Science at ETH Zurich and the Director of the Microsoft Mixed Reality and AI Lab in Zurich delivered a presentation on Spatial computing and industrial metaverse. He gave interesting examples how metaverse can be used for instructional trainings of workers at industrial settings and how spatial computing is contributing to more sophisticated mapping and localization of robots.
An ideation workshop followed during the afternoon. The workshop was moderated by facilitators working for the Databooster:
Prof. Dr. Patricia Deflorin and Dr. Jürg Meierhofer
The challenges identified could be roughly clustered into 3 categories.
Cluster 1 relates to the general challenges of integrating automation (both on software and hardware side) into existing processes. On the one hand, this includes the necessary technological knowledge and understanding of WHAT one wants to implement – on the other hand, it also includes the expertise or competence development within the company on HOW it can ultimately be integrated. Decision-makers, employees and customers must all be integrated into this process, and employee acceptance and training/up-skilling must be ensured – all while considering the short- and long-term cost-benefit relations, ethical and moral issues, and cultural acceptance.
Cluster 2 refers to machine learning systems that react more flexibly/dynamically to process changes. On the one hand, regarding rapidly changing environmental conditions, on the other hand, related to highly dynamic process sequences (small batch manufacturing). This requires not only innovative approaches in human-machine interactions (intuitive, ease-of-use handling, no-code environments etc.) but also standardization in processes and interfaces as well as further developments in modular and self-learning ML systems. In this context, the challenge also arises as to how and whether the individual, experience-based knowledge of experts in a company can be transferred to (semi-)automated processes, e.g., the transformation of human intuition in process understanding to rule-based robot-supported systems.
Cluster 3 concerns the (extended) use of cobots/robots in the field of maintenance. This concerns the large area of logistics/ergonomics, from pick-up, sorting and movement of highly divers component categories, to complex processes in material/surface inspection, automated damage repair/replacement, and assembling and dismantling of large rail vehicles. In these processes, the reliability/accuracy requirements are a major (technical) challenge and addressing them would often involve very high costs.
In each cluster, the workshop focused on some of the identified challenges and discussed possible solutions.
Solutions Cluster 1:
- Guideline/framework for the integration of automation processes into existing workflows, considering management, customer, and employee’s perspectives (at the meta-level).
- Framework for integration and regular assessment of compliance with ethical and moral guidelines and legal framework conditions.
- Guideline/framework for the practical implementation of automation processes in the company regarding the involvement of employees: internal acceptance, considering employee’s needs, training/education (up-skilling) and empowering.
- Needs assessment for automation solutions in industry (standardization, interfaces, usability/interactivity).
Solutions Cluster 2:
- Development of automatization solutions that can meet the requirements of low volume/small batch production or highly variable process flows (and are economically feasible).
- Development of ML systems with improved flexibility in terms of self-learning/self-optimizing components (self-configuring modular robots, autoML) so that they can better adapt to changing environments and high-complex processes.
- Development of monitoring systems to capture unconscious, intuitive human components in the processing/manufacturing process and convert them into a rule-based, machine-executable program (e.g. ViT).
Solutions Cluster 3:
- Development of a tunnel scanning and cleaning system to identify and remove paint from wagons/locomotives – a combination of intelligent optical sensing for detection and characterization of paint and non-destructive automatization for cleaning/removal of paint while preserving the underlying paint/coating etc.
- Development of a tunnel scanning to identify and characterize surface damages/deformations on wagons/locomotives – a combination of intelligent passive and active optical sensing, resulting in a digital 3D-representation and classification of surface damages/deformations.
- System development of an automation solution (may be in the field of cobots?) for the dismantling of trains, considering very different work steps and a mobile implementation which works inside the vehicles.
- Conceptual development of a holistic system (identifying segments, sub-processes, requirements) to support logistics/ergonomics, both in terms of the potential of autonomous (e.g., for, sorting, transport) and worker assistance systems (e.g., exoskeleton, human-robot collaborations).
Thank you to the speakers, workshop facilitators and all participants!
Please explore the above challenges and solutions and we welcome your submissions to the next call for proposals for funding: