Frontiers of Engineering Reports on Leading Edge Engineering from the 1997 NAE Symposium on Frontiers of Engineering (1998) / Chapter Skim
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Sensors and Control for Manufacturing Processes
Sensors and Control for Manufacturing Processes
Pages 21-38
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Navy. The objective of the program was to develop sensor and control technology to monitor the critical process conditions and to modify parameters during the spray metal-forming process to produce components with repeatable microstructural quality.
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In the intelligent spray-forming system, a fuzzy logic controller monitors the critical process conditions and modifies parameters during the process to produce components with repeatable microstructural quality. The first step in developing the controller was to construct sensors and controls to monitor the effects of several independent process parameters such as melt superheat, metal flow rate, gas pressure, spray motion, spray height, and substrate motion.
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The spray collector uses hydraulic actuators and has five axes of motion, including withdrawal, spray height, wrist roll, wrist pitch, and tool roll. Asymmetric components such as hemispheres and tapered tubes can now be produced via spray forming.
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Graphs generated by the neural network prediction help define the optimal operating region for the spray-forming process and indicate the effect of changing input process parameters on final parts quality. In summary, intelligent control techniques have been applied to the sprayforming process, which has significant metallurgical and economic benefits but requires sophisticated control technology to achieve the level of reliability and reproducibility required for widespread commercialization.
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1993. Spray forming quality predictions via neural networks.
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F&S's initial product was a fiber optic strain sensor called the extrinsic Fabry-Perot interferometer (EFPI) , which has been shown to be an excellent choice for both surface-attached and embedded strain measurement applications.
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For example, conventional strain gauges are on a small polyimide patch. Initially, F&S's fiber optic strain gauge looked like a thin strand of glass, and the user had no idea what to do with it.
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In conclusion, fiber optic sensors offer the opportunity to measure many different things, from physical parameters such as strain, temperature, and pressure to chemical and biological materials. These sensors are being used to 1)
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This dual task of monitoring process condition indicator variables and inducing change in the appropriate process variables in order to alter process conditions favorably is the job of the process control system. Process control is that aspect of engineering concerned with the analysis, design, and implementation of control systems that facilitate the achievement of the stated objectives of safety, production rate, and product quality.
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From the early 1960's onward, with increased process and product demands (increased production volume demands coupled with tighter product quality specifications, increased stringency in environmental regulations, etc.) and even more complex process interconnections with energy integration, novel
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MODEL PREDICTIVE CONTROL In the 50-year period since the end of World War II, the success story of process control practice in the chemical industry is, arguably, model predictive control (MPC) a computer control scheme that utilizes an explicit model of process dynamics in conjunction with optimization techniques for the effective control of multivariable, poorly understood, difficult-to-model industrial processes that are subject to multiple constraints (Garcia et al., 1989~.
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.~ <~ . ~ 1 1 - k+m-1 kelp Honzon FIGURE 1 Example of elements in model predictive control: x x: reference trajectory, y*
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Mathematical programming approaches for assessing dynamic operability the ability of a process plant to guarantee high product quality (low variability) ; quantifying trade-offs between economics and dynamic operability, providing an objective systematic procedure for discriminating between competing designs.
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SUMMARY AND CONCLUSIONS The chemical process industry is broad, diverse, and continuously evolving. The processes are becoming more complicated, the operating requirements are more stringent, and to meet all the objectives of safety production rate and product quality in such an environment requires control systems that are far more effective than ever before.
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1994. On-line modeling and predictive control of an industrial terpolymerization reactor.
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