Recent research from Chalmers have shown that by slightly tuning robot motions, the energy use can be reduced by 10 –30%, with preserved cycle time. These tuned motions are smoother than the original ones, thus reducing the wear of the robot and its components and so increasing their life-time. In production plants many resources, such as trucks, robots, conveyors, are constantly moving, turning, clamping etc. Traditionally, cycle time is the major requirement when preparing these kinds of systems, thus sustainability issues such as energy efficiency are not taken into account. The SmoothIT project will develop tools and methods for energy optimization of programmed motions, and for monitoring the production to identify motions that may be improved, and planning of the maintenance work. In addition, online tool s will be developed that can automatically optimize the production by tuning the motions to reduce the energy use and increase the life-time of the devices.
The aim is to develop new models for visualizing and predicting delivery schedule variations in supply chains.
Maintenance in existing plants is becoming increasingly important, where predictive maintenance has become an emerging technology. The use of decision support tools contributes to environmentally and economically sustainable production. Within this project, different types of digital twins have been designed and evaluated. Specifically, new predictive model types have been tested in two different industrial case studies; a heat exchanger at SSAB and a profiled header at Svenska Fönster AB.
To create an inventory of AI techniques for maintenance services, apply AI techniques to three industrial cases, and evaluate their economic and environmental implications.
Improve the efficiency of sawmills, including improved monitoring and maintenance of the production line. This by sharing data via digital twin between the actors in the maintenance chain.
The objective is to bring together expertise from AI and LCE to Product/Service Systems for Swedish manufacturing firms in a multidisciplinary research effort to utilise latest techniques efficiently for Swedish production industry. The goal is to make a plan of developing demonstrators in production and maintenance using artificial intelligence techniques, digital technologies and lifecycle engineering methods
The project aims to digitalize established tools for production disturbance handling.
The goal is to mirror the production and make custom information available for industry personnel.
The project aims at facilitating the implementation of Smart Maintenance through extended collaboration within the maintenance community.
Increased digitalization brings new possibilities for Swedish manufacturing companies. This project focuses on what data are needed to feed assembly variation simulation and how this data can be captured and stored efficiently and effectively. The project contributes to increased geometrical quality. The technical value chain from part inspection, to extraction of relevant data, storage of data, usage of data in variation simulation (as a digital twin) and to visualization of simulation results as decision support will be covered. This has the potential to replace prototypes/test series and saves cost, time and reduces the environmental impact.
To demonstrate the new technology with robots that enable Swedish companies to develop innovative new products for automated production o maintenance.