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This paper introduces a decentralized shape and vibration controller for structures with large and potentially unknown system order, model-parameter uncertainty, and unknown disturbances. Controller implementation utilizes distributed, colocated, and independent actuator–sensor pairs. Controller design requires knowledge of the relative degrees of the actuator and sensor dynamics and upper bounds on the diagonal elements of system's high-frequency gain matrix. Closed-loop performance is determined by a parameter gain, which can be viewed as the cutoff frequency of a low-pass filter. For sufficiently large parameter gain, the closed-loop performance is arbitrarily small. Numerical examples are used to demonstrate the application and effectiveness of the decentralized controller, and we present experimental results for a setup consisting of a cantilever beam with piezoelectric actuators and strain-gauge sensors.

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This paper investigates the problem of optimally locating passive vibration isolators to minimize unwanted vibration caused by exogenous disturbance forces. The stiffness and damping parameters of the isolators are assumed to be known, leaving the isolator locations, which are nonlinearly related to system states, as unknown optimization variables. An approach for reformulating the nonlinear isolator placement problem as a linear time-invariant (LTI) feedback control problem, by linking fictitious control forces to fictitious measured outputs using a nonzero feedforward term, is proposed. Accordingly, the isolator locations show up within a static output feedback gain matrix which can be optimized, using methods from optimal control theory, to minimize the H2 and/or H∞ norms of transfer functions representing unwanted vibration. The proposed framework also allows well-established LTI control theories to be applied to the analyses of the optimal isolator placement problem and its results. The merits of the proposed approach are demonstrated using single and multivariable case studies related to isolator placement in precision manufacturing machines. However, the framework is applicable to optimal placement of passive isolators, suspensions, or dampers in automotive, aerospace, civil, and other applications.

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One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.