Machining dynamics deals with the interaction between the machining process and machine tool and/or workpiece.
Machining vibrations affect the productivity and quality of machining processes. This may result in surface quality problems, decreased tool life and even a reduction in machine tool life as a long term effect. Due to the resulting non-conformances and the potential decrease of process parameters, productivity of machining processes under vibration suffers.
The Machining Dynamics Technology Group performs research and develops methods to predict, diagnose and control machining vibrations. Our state-of-the-art research creates outputs which are applied to industrial applications to optimise machining processes.
In addition to research and applied projects, the group delivers internal and external training in machining dynamics, experimental modal analysis ( tap testing) and in the mechanics and dynamics of machining processes.
- Predictive models
Machining dynamics deals with the interaction between the machining process and machine tool and/or workpiece. As a result of the machining process, machine tool and workpiece are exposed to cutting forces. This causes tool and workpiece displacements which contribute to the form errors.
Hence, high cutting forces lead to higher form errors which can lead to unacceptable parts due to the dimensional tolerances on the parts. Moreover, higher cutting forces may even break the tool due to the resulting bending stresses or they can cause the spindle to stall if torque and power limits of the machine are exceeded. On the other hand, vibrations in the system can cause surface marks on machined surface which result in surface quality problems. In order to prevent these issues, predictive process models can be used in parameter selection phase to find the optimum process parameters that maximises productivity while respecting the aforementioned constraints.
The Group makes use of process models in optimum parameter selection for many machining process like turning, milling, parallel machining, ceramic milling, composite milling, 5-axis milling, etc. against constraints such as cutting forces, torque, power, form errors and surface roughness.
- Spindle dynamics
Depending on the design of the machine tool spindle, spindle can demonstrate non-linear dynamic response under rotating or operational conditions. The non-linearity may be due to the centrifugal forces, gyroscopic effects, thermal loads, preload and/or cutting forces. These effects can cause erroneous predictions of process stability. Spindle dynamics research is instrumental to understand the source of these errors and take this non-linearity into account while calculating stable cutting parameters
- Vibration control
Application of active and passive vibration control techniques can considerably increase the material removal rate in machining processes while avoiding vibration problems. The group has delivered a project on passive vibration control using piezoelectric patches in turning. The projects to further this work and active vibration control projects are in the roadmap of the group.
- Process Optimisation
The Machining Dynamics Group apply their expertise in optimisation of the machining processes of the partners for reduced cycle time and increased quality.
The Group delivers in internal and external training in machining dynamics area. The group delivers training on tap testing and mechanics and dynamics of machining processes.
For the benefit of the AMRC partners, Machining Dynamics Group objectively benchmarks the available technologies for a given machining requirement.
- Robotic, Robotic assisted machining
Large part manufacturing, reconfigurable manufacturing and parallel machining are three drivers that will make robots appear more and more in machining operations. The Group is investigating calls for funding projects in the area of robotic and robotic assisted machining.
- Composite machining
Machining Dynamics Group has been working in close collaboration with composites group on developing analytical process models to guide the parameter selection and optimise composite machining processes.
- Kern Evo micro milling machine.
- Test spindle (procurement in progress).
- Variety of machine tools.
- Tap test kits for experimental modal analysis (Metalmax, Cutpro).
- Set-up for dynamic characterisation of cutting tools in rotating conditions.
- Kistler force dynamometers (lathe, drilling dynamometers, several table type dynamometers).
- Laser displacement sensor (Microepsilon).
- Laser vibrometer (Polytec).
- Variety of accelerometers.
- Variety of microphones.
- NI Data Acquisition Systems.
- Piezoelectric patches for shunt damping applications.
- Capacitive displacement sensors (Lion Precision).
- Spindle Analyser (Lion Precision).
- Metalmax TXF, Cutpro.
- Metalmax TXF, Cutpro for basic machining process simulations.
- In house developed codes for complex processes such as 5-axis milling, parallel machining.
Tool path optimisation
- Vericut Optipath.
- Thirdwave Production Module.
- Machining Studio.
Dr Erdem Ozturk– Technology Fellow
PhD, Mechatronics Engineering, Sabanci University, 2010
MSc, Mechatronics Engineering, Sabanci University, 2005
BSc, Mechanical Engineering, Middle East Technical University, 2003
PhD thesis: Mechanics and Dynamics of Multi-axis Machining Operations
MSc thesis: Modelling of 5-axis Milling Forces and Form Errors
Omer Ozkirimli (MSc) – Technical Lead
MSc, Industrial Engineering, Sabanci University, 2011
BSc, Mechatronics Engineering, Sabanci University, 2008
MSc thesis: Mechanical and Dynamical Process Model for General Milling Tools in Multi-Axis Machining
Nacho Blanco, Project Manager
MBA, Industrial Management, Sheffield Hallam University, 2005
MSc, Industrial Engineering, Mondragon University, 2004
BSc, Mechanical Engineering, Mondragon University, 2002
Wei Liang Choong (MSc) – Project Engineer
MSc, Mechanical Engineering and Industrial Management,
University of Sheffield, 2007
BEng, Mechanical Engineering, University of Sheffield, 2006
MSc thesis: Injury Prevention versus Performance in Football Boot Design
Thomas Gibbons, EngD student
BSc, Mathematics, University of Manchester, 2012