Integrated analytics for advanced machinery – IAM
The goal is to improved resource utilisation of advanced machinery through an integrated metrology and analytics approach.
2019 – 2022
Dr Archenti is active in Precision Engineering research. The concept of Precision Manufacturing and Metrology refers to the ability to produce and characterize a component’s dimensional-, geometric- and surface properties as well as to measure, monitor and control manufacturing systems capability.
Dr Archenti´s research area is primarily concentrated in the disciplines related to precision engineering applied in manufacturing. Precision here refers to the dimensional and geometric characteristics as well as surface characteristics of manufactured products. It concerns the ability to make and measure the key features on the parts and also to measure and act upon key process-machine characteristics (capability), those that directly impact the part quality. Fundamental concept in my research activity and present research in precision engineering and metrology covers the following focus areas:
Focus area 1 – Production equipment: design and build of ultra-precision machine systems;
Focus area 2 – Production processes: ultra/nano-precision manufacturing;
Focus area 3 – Produced parts: dimensional, geometric characteristics and surface finish, through the entire life cycle, from design to the recycle;
Focus area 4 – Industrial metrology: characterization of metrology systems, instruments and techniques
Research focus:
Dr Archenti is also the director of center for Design and Management of Manufacturing Systems – DMMS, KTH Royal Institute of Technology, with responsability to coordinate activities in Research, Education and Information decimination between academy and industry.
The goal is to improved resource utilisation of advanced machinery through an integrated metrology and analytics approach.
2019 – 2022
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
2017 – 2019
The project aims to increase productivity, robustness, and resource efficiency in production systems using data-driven maintenance planning.
2016 – 2019