1) To receive basic knowledge about sensors, signals conditioning and experimental data acquisition. 2) To receive basic knowledge about methods for design of experiments (DOE). 3) To receive practical knowledge about experimental data processing. 4) To receive training in testing procedures for machine tools and machining systems. 5) To know how to make technical projects and testing report.

Upon successful completion of this course students should be able to: 1. Apply knowledge about sensors in the setting of experiments with electrical measuring of mechanical quantities. 2. Form a plan for preparing the experiment. 3. Complete the installation for measurement and data acquisition. 4. Complete the calibration of transducers and the prepare of components for signal conditioning. 5. Configure the application in the software for data acquisition, for measurements with visualization and storing of time series of measured quantities. 6. Use files with time series of measured quantities for subsequent digital processing for identification of unknown parameters of object or process. 7. Prepare Technical Elaborate and reports about testing.

New teaching contents: 1) Sensors for testing of machine tools and machining systems. Dynamometers. Accelerometers. 2) Design of experiment (DOE). 3) Signal conditioning and experimental data acquisition. 4) Experimental data processing. 5) Methods for identification of continuous-time models from sampled data. Elaboration of new teaching contents and instructions for doing the tasks: 1) Sensors preparing and calibrations. 2) Preparing for the designed experimеnts. 3) Experimental setup for data acquisition. 4) Methods and software for experimental data processing. 5) Examples for identification of continuous-time models from sampled data.

Practical teaching involves laboratory work in Laboratory for machine tools and machining systems, and seminar work writing. Planned experiments are carried out in the Laboratory with finishing the reports. These reports are a part of the seminar work.

Study curriculum and student motivation for learning experimental data acquisition and processing according to the goals set and outcomes offered.

Laboratory for machine tools and machining systems, which includes both hardware and software: 1) Different kinds of sensors (accelerometers, dynamometers etc.). 2) The systems for experimental data conditioning and acquisition. 3) Software for experimental data processing. 4) The systems for laboratory testing of machine tools accuracy. 5) The system for circular interpolation test. 6) Test bed for identifying parameters of mechanistic cutting forces models. 7) Test bed for cutting process optimization, feed scheduling, and integrated simulation of machine tool and process. 8) Software for virtual machining system simulations. 9) Test bed for parallel kinematics machine tools. 10) Test bed for configuring and programming of modular open architecture machine tools(MOMA). 11) Test bed for the STEP-NC protocol based programming of CNC machines. 12) Hardware needed for basic modal analysis (modal hammer, accelerometers etc.). 13) Software for basic modal analysis. 14) Functional simulator of the rapid prototyping machine tool. 15) Software for basic optimization of machine tools structures.

Total assigned hours: 65

New material: 30
Elaboration and examples (recapitulation): 20

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 10
Review and grading of the project: 0
Test: 0
Test: 0
Final exam: 5

Activity during lectures: 0
Test/test: 0
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 70
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 50

S. M. Pandit, S-H. Wu, Time series and system analysis, with applications, John Wiley & Sons, 1983, ISBN 0-471-86886-8. ; H. G. Natke, Einfuerung in Theorie und Praxis der Zeitreihen- und Modalanalyse, Vieweg, 1983, ISBN 3-528-08145-7. ; H. L. Wang, Eds, Identification of Continuous-time Models from Sampled Data, Springer, 2008, ISBN 978-1-84800-160-2. ; J. Park, S. Mackay, Practical Data Acquisition for Instrumentation and Control Systems, Elsevier, 2003, ISBN 07506 57960. ; T. L. Schmitz, K.S. Smith, Machining Dynamics, Frequency Response to Improved Productivity, Springer, 2009, ISBN 978-0-387-09644-5.