Process variable

About: Process variable is a research topic. Over the lifetime, 3983 publications have been published within this topic receiving 43130 citations. The topic is also known as: process parameter.

Pattern evaluation method and apparatus for adaptive control

Abstract: A method is provided for automatic adaptation of process controller parameters according to the requirements of a non-linear process in order to achieve optimum control action. This method evaluates the response of a measured process variable to upset in the form of a change in set point or load. The upset initiates the adaptation procedure, which determines evaluation intervals related to process dead time and lag characteristics as evidenced by the response of the measured variable over predetermined portions of said upset; the deviation in the measured variable from set point or related demand reference is examined over the evaluation intervals and preferably integrated, the integrated error result being indicative of a required proportional gain change in the case of one type of evaluation interval and indicative of a required reset gain change in another type of evaluation interval. Proportional and reset gains are altered in accordance with this procedure until the gains are optimized as evidenced by maximum conformity to the demand references of the measured variable response to upsets generally.

Real-time monitoring and control in plasma etching

Abstract: Process control strategies have been developed for plasma etching of silicon (Si) and silicon dioxide (SiO/sub 2/) in a CF/sub 4//O/sub 2/ plasma. The analysis considered four measured variables, four manipulated variables, and up to seven performance variables. Empirical input-output models were developed by regression analysis. Relative gain array analysis and singular value decomposition were used to select manipulated/process variable control loop pairings and to evaluate potential difficulties in control system performance. Singular value decomposition was also used to determine process/performance variable pairings. Block relative gain analysis of multivariable interactions in the process indicated that partial decoupling was necessary for adequate control, and this was verified by simulation. >

Multiscale roughness analysis in injection‐molding process

Abstract: The roughness of polymer surfaces is often investigated to guarantee both the surface integrity and the surface functionality. One of the major problems in roughness measurement analyses consists in determining both the evaluation length and the reference line (i.e., the degree of the polynomial equation) from which roughness parameters are computed. This article outlines an original generic method based on the generalized analysis of variance and experimental design methodology for estimating the most relevant roughness parameter p, the most pertinent scale, s, and finally, the degree of the polynomial fitting, d. This methodology is then applied to characterize the influence of four process parameters on the final roughness of poly(polypropylene) samples obtained by injection molding. This method allows us to determine the most efficient triplet (p, s, d) that best discriminates the effect of a process parameter q. It is shown that different (p, s, d) values are affected to each process parameter giving finally the scale on which each process parameter modifies the roughness of a polymeric surface obtained by injection molding. POLYM. ENG. SCI., 2008. © 2008 Society of Plastics Engineers

Statistical investigation of die wear in metal extrusion processes

Abstract: The aim of this paper is the statistical process control analysis of the tool wear evolution during metal extrusion process for better understanding the principal causes that generate the variability of such a complex phenomenon. The wear prediction is carried out using finite element simulation including the Archard wear model. The tool wear modeling is presented briefly as well as the response surface methodology. The study is based on the application of the central composites designs and allows for the analysis of the response (wear) sensitivity of the tool. The statistical investigation of the process makes it possible to study the influence of each process parameter on the response sensitivity.

Selective Laser Melting of 316L Austenitic Stainless Steel: Detailed Process Understanding Using Multiphysics Simulation and Experimentation

Abstract: The parameter sets used during the selective laser melting (SLM) process directly affect the final product through the resulting melt-pool temperature. Achieving the optimum set of parameters is usually done experimentally, which is a costly and time-consuming process. Additionally, controlling the deviation of the melt-pool temperature from the specified value during the process ensures that the final product has a homogeneous microstructure. This study proposes a multiphysics numerical model that explores the factors affecting the production of parts in the SLM process and the mathematical relationships between them, using stainless steel 316L powder. The effect of laser power and laser spot diameter on the temperature of the melt-pool at different scanning velocities were studied. Thus, mathematical expressions were obtained to relate process parameters to melt-pool temperature. The resulting mathematical relationships are the basic elements to design a controller to instantly control the melt-pool temperature during the process. In the study, test samples were produced using simulated parameters to validate the simulation approach. Samples produced using simulated parameter sets resulting in temperatures of 2000 K and above had acceptable microstructures. Evaporation defects caused by extreme temperatures, unmelted powder defects due to insufficient temperature, and homogenous microstructures for suitable parameter sets predicted by the simulations were obtained in the experimental results, and the model was validated.