Manufacturing is evolving toward a flexible production with autonomous operations to respond to new customer requests, reduce manual labor, and streamline processes.
The growing engineering complexity is forcing machine builders to adopt new design practices to ensure machines are safe, perform as expected, and cost-effectively designed and built.
This on-demand webinar highlights how system simulation models support engineers during the development and lifecycle of production machines.
System simulation models can efficiently drive the design of production machines by virtually benchmarking new technologies in terms of performance, reliability, carbon footprint, energy efficiency, costs, and more.
This webinar showcases real-world examples of how multiphysics system simulations in the early design phase investigated the best sizing of a delta picker and analyzed energy savings in a hydraulic press by comparing different configurations.
Watch the webinar replay and see firsthand how system simulation can accelerate the design process and save costs at the same time.
Once a precise virtual representation of a physical system is available, automation engineers can integrate and validate smart control strategies before a prototype is available.
Virtual commissioning reduces the time and costs associated with getting new machines up and running.
Watch the on-demand webinar and see how an organization used multiphysics simulation to perform virtual commissioning on a thermal subsystem within a unit-dose packaging machine and a tire manufacturer optimized the PID controller of the control valve regulating the internal heating process in a curing press.
System models connected to real-time machine data can measure parameters that are unmeasurable or too expensive to be measured by real sensors.
Insights from these digital models can optimize manufacturing operations and improve maintenance planning.
See real-world examples of operators using multiphysics simulation models connected to real-time data to estimate the value of variables using virtual sensors and optimizing in-service performance to implement model-based predictive maintenance in this on-demand webinar.
System Engineer, Technical Sales Support
Aldo has spent his career focused on engineering modelling, simulation, and advanced control. He earned a Bachelors in Management Engineering and a Masters in Automation and Control Engineering from the Politecnico di Milano in Italy. He began working at Siemens in 2018, and his current role involves modeling, simulation, control, and condition monitoring in industrial machinery.
Business Developer for System Simulation
Francesca Furno has received her MSc Degree in Mechanical Engineering as well as her Ph.D Degree in Fluid Power from the Politecnico di Torino in Italy.
She furtherly moved to France to join Siemens in 2007 and today she is promoting Simcenter Systems solutions as engineering tool to drive innovative technologies. Francesca is supporting the Industrial Machinery market Worldwide in adopting digitalization.