The role of digital technologies in business and society is increasing by leaps and bounds. The online design and composition of products according to one's own needs also takes on new forms. Customer-driven customization in a mass production environment is expected to be the standard by 2030 . The product specification is automatically processed into machine control information, logistics formats and product folders. From design to assembly and production, this is highly autonomous, based on just-in-time-in-place principles, assured on the basis of intrinsic system quality, fed by AI/ML techniques and controlled from a management dashboard with underlying Enterprise Resource Planning. This transition, under the heading of 'smart industry', among other things, is taking place worldwide and is an extra incentive for employees to continue training.
BACKGROUND INFORMATION
Manufacturing is becoming increasingly data-driven. This concerns high-mix-low-volume products, with associated production processes, management and logistics. This form of entrepreneurship requires thorough knowledge about real-time acquisition, processing, application and analysis of data. And that requires sensitive sensors, analysis software, robust processing informatics, smart algorithms, handy robots and high-tech mechatronics. There is still a long way to go for the average SME.
Consortium partners
In the Researchgroup for Digital Driven Manufacturing, Saxion and Windesheim in close collaboration with six core partners, supported by TechForFuture - Center of Expertise HTSM Oost, will help entrepreneurs to meet this challenge. We bundle knowledge in the fields of robotics, industrial automation & artificial intelligence. We develop knowledge in the Industrial Automation & Robotics (W), Ambient Intelligence (S) and Mechatronics (S) lectorates. We strengthen that knowledge by creating crossovers at the interface between key technology and practice. We implement that knowledge through the involvement of the TValley Field Labs, The Garden, Perron038 and Industrial Robotics. We embed this knowledge in several bachelor's and master's programs at the universities of applied sciences, including Technical Computer Science, Electrical Engineering, Mechatronics, HBO-ICT, Industrial Product Design and Mechanical Engineering. And we disseminate this knowledge through TechForFuture network.
Process description
The Researchgroup for Digital Driven Manufacturing means a reinforcement of the current three professorships with additional focus points on sensing, automation and digital twinning. Three additional research teams will be set up at the interfaces of the lectorates, led by an associate lector or PhD researcher (PD) from the business community, who will conduct research in these areas together with students, lecturers and entrepreneurs from the region. Saxion and Windesheim offer extra manpower and investments are made in state-of-the-art facilities at Saxion, Windesheim and the four Smart Industry Field Labs involved. The investments bring a distributed high-tech manufacturing facility, where students and researchers collaborate on Digital Driven Manufacturing. The driving force lies in data, in product and process information. This concerns both forecasting and improving processes in a broad sense with data. It is about forecasting with data, for example to manage process control instead of end of pipe Quality Control. It is also about shortening the step from design to testing with digital representation (digital twinning) and the transition from corrective maintenance to predictive maintenance. It is about improving processes using smart data, artificial intelligence, machine learning and smart automation. This forms the basis for zero-defect production, process up-time improvement and integrated smart IoT. We take the whole idea behind Smart Industry to the next level, by adding intelligence to the smart-connected machines, processes and systems. With this focus we align with KIA key technology 'digital technologies' and 'Engineering and Fabrication Technologies'. We lay the foundation for research into machine learning process technology within (opto) mechanical and (opto) mechatronic systems and adaptive, self-steering, machine learning and autonomous robotics through sensory interaction, sensor grids, machine learning sensors and actuators and data looping; the 'connected factory – industry 4.0'. In doing so, we look for qualitative predictability by linking the collection and analysis of sensory data and product and process qualifications, possibly fixed in blockchain, with the security of open grid structures in IoT with crucial control data and via/in the cloud-switched systems that are jointly ensure the integration of autonomous specifications, production, logistics and warehousing. This research direction requires thorough background knowledge of data manipulation, system automation and sensing. Knowledge gained over many years of research tradition at the lectorates of Saxion and Windesheim: Industrial Automation & Robotics (W), Ambient Intelligence (S) and Mechatronics (S).
View the kick-off of the SPRONG project here
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