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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

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Delamination is recognized as one of the most critical defects that can result from the machining composites. Delamination has been a major form of failure in drilled composite materials due to the composites lack of strength in the drilling direction, which results in poor surface finish, reduction in bearing strength, reduction in structural integrity and ultimately poor performance of the composite. Currently, most of the major research reported delamination address specific of machining fiber glass, graphite fiber or carbon fiber reinforced polymer composites. It is not yet clear how different drilling parameters affect the machinability of natural fiber reinforced polymer composite materials and quality of drilled holes. This paper report the investigation in drilling holes on natural fibre reinforced polyester composites and evaluate its hole quality by measuring delamination. Three different type of drill: twist 118o drill, brad drill and end mill were used. Drilling process is carried out for three spindle speed (1500 rpm, 2000 rpm and 2500 rpm) and three feed rate (0.1 mm/rev, 0.15 mm/rev and 0.2 mm/rev). Brad drill experienced higher delamination values compared to twist and end mill. Increasing of feed rate and spindle speed also caused a relevant increase in the delamination values. It is found that Rice husk reinforced polyester composites delamination value is lower when compared to the glass fiber reinforced polymer.

Structural optimization was important nowadays in getting the optimum design and usage of the material use, where size optimization is part of it. This research is focusing on the application of size optimization on steel wheel rim. Existing steel wheel rim dimension was measured and Finite Element Analysis (FEA) was done to get actual dimension and mechanical properties (stresses), as baseline data. CAD modelling on steel wheel rim was done using Pro-Engineer/Creo 1.0. One structural element was selected to be optimized. Then optimization program was generated using MAPLE to get optimum value (size). Steel wheel rim will be re-modelled with optimum size to get optimum design. FEA was done again on optimized steel wheel rim to compare with actual steel wheel rim data. Optimized steel wheel rim was showing better in mechanical stress with optimum size (minimize the weight and volume).