Manufacturing Control and Dynamics Laboratories
Director: Mustapha S. Fofana
BSc/MSc (Budapest) MASc, PhD (Waterloo)
Associate Professor, Mechanical Engineering Department
The Manufacturing Control and Dynamics Laboratories (MCD labs) focus on the development of systematic and theoretically rigorous, yet comprehensive hands-on approaches to ensure that manufacturing adhere to performance opportunities and expectations based on the ability to recognize and eliminate wastes, and to predict and suppress undesirable instabilities or vibrations. As manufacturing infrastructures are becoming leaner and technologically responsive to opportunities and expectations, they seek to predict and control instabilities and wastes that can come from different sources ranging from overproduction to modulated and unmodulated time delay excitations. Manufacturing instabilities and wastes are the chief determinant for maximization of opportunities and expectations because their presence can produce wide forms of performance failures. The focus of MCD labs is emphasized on the current thrust areas:
- Manufacturing Systems Dynamics
We investigate nonlinear manufacturing systems dynamics with respect to parametric time delays and probabilistic excitations. Stability switches and their bifurcation types to regenerative chatter in high speed machining with time-varying spindle speed modulations are examined. Experimentally and numerically derived interrelationships between machining stability, tool-life, cutting forces and cutting conditions to nanostructured coatings are emphasized. Construction of stability charts and bifurcation diagrams depicting single and double Hop supercritical and subscritical bifurcations is carried out. The dynamics of vibratory feeder bowls, pneumatic pick and place systems, rotary index drives with respect to periodic and nonlinear excitations are examined. Autonomous pattern recognition of robust dynamics for complex manufacturing systems is constructed.
- Manufacturing Systems Control
Integrate modern control and dynamical systems theories with information technology to design and monitor autonomous manufacturing processes and systems, and to predict boundaries of nonlinear control bifurcations. Development of control design and feedback linearization techniques based on nonlinear time varying delay dynamical systems theory is carried out. The construction of lean manufacturing DNA and -sigma distribution charts to eradicate wastes and to evaluate performances of manufacturing systems and processes with respect to maximization of opportunities, expectations and sustainability are emphasized. Developing automatic displacement, speed and force modulations through multiple time-varying delays and Guassian excitations for precision machining, cryogenic processes and other areas of biomedical and pharmaceutical applications is considered. Cryogenic advancements of cutting tool-life, surgical implants and neuroprostheses are emphasized. Automated calibration and control synthesis of large-scale autonomous integrated manufacturing systems to ensure instability- free performances are constructed. Fundamental understanding of technological issues for interfacing manufacturing with sensors, actuators, asymptotic methods, computer-algebra systems and data acquisition systems is emphasized.
- Stochastic Manufacturing Systems
Fluctuations of manufacturing opportunities and expectations with respect to internal and external uncertainties are investigated. Auto-quality development through statistical process control, sigma-sigma and nonlinear programming is emphasized. Probabilistic nonlinear time delay dynamics with modulated Wiener processes and resonance bifurcations of specific manufacturing systems are studied. We employ stochastic centre manifold theorem and the integral stochastic averaging methods to reduce the infinite dimensional character of stochastic manufacturing systems and processes with excitations to finite- dimensional Markovian scalar equations. Asymptotic stability switches are determined with probability one by the signs of, Lyapunov exponents, entropy, rotational numbers and moment- Lyapunov exponents. Numerical computations of moment Lyapunov exponents for distributed and variable time delays in autonomous systems are carried out.
- Manufacturing Health Monitoring
Pattern recognition of traditions and behavioural adaptations to manufacturing physics and technologies are emphasized. Correlations between brain behavioural patterns, blood flow rates, lumbar spine stability and manufacturing relationships with respect to repetitive and mixed processes, operations and loads are investigated. Integrative modern dynamical systems theory with computer algebra systems and data acquisition systems will provide opportunity to distinguish between chaotic and stochastic behavioural patterns. The roles of Lyapunov exponents, moment Lyapunov exponents, entropy, rotational numbers, fractal dimensions, power spectrum, wavelets Fourier synthesis are emphasized in the pattern recognition of stochastic and chaotic time signals.
- Delay Dynamical Systems
Concepts such as spectral theory, semigroups, infinitesimal generators, bilinear pairings, variation of constants integral formulas, the integral averaging method and degenerate Hopf bifurcation theorem are candidates for deriving scalar bifurcation equations at centre manifolds for specific manufacturing and biomechanical applications. Local and global bifurcation theory as applied to time-varying delay differential equations, resonant bifurcations, multi- dimensional reduction, and time scaling of time delay systems are emphasized. Developing qualitative and quantitative methods for studying nonlinear time delay models with nonlinear and periodic excitations in the context of manufacturing, biomechanical systems and structural systems are encouraged. Numerical computations of characteristic eigenvalues of linearized stability bifurcations and Hopf interactions for nonlinear delay differential equations at degenerate bifurcations are carried out.
Last modified: October 06, 2006 14:48:30