Topic
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.
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Patent•
12 Jun 2009
TL;DR: In this article, a method for determining process variable-measured values as a function of a control variable is presented. But the method is not suitable for the measurement of process variables and does not consider the statistical variation of the process variable.
Abstract: The method involves iteratively determining process variable-measured values as a function of control variable, so as to receive a set of process variable-characteristics. An expected value assigned to each value of the control variable is determined from a measured value-amount at the process variable-measured values. A determination is made whether the process variable-measured values depend on a statistic variation of the process variable. An alarm is outputted, and processing speed of the machining process is changed, if the measured values are not depending on the statistic variation. An independent claim is also included for a controller of a machine tool.
16 citations
TL;DR: In this article, the least square model based on feed rate, cutting speed, and grain size was integrated with a genetic optimization algorithm to provide the optimal process parameter, and the results showed that the feed rate was the most significant factor for minimizing Fy force and torque.
16 citations
01 Jan 2013
TL;DR: In this paper, the influence of various GTAW process parameters, i.e. Pulse Current, Base current and Pulse Frequency on welds were studied for properties of weld like microhardness, ASTM grain size number and heat affected zone (HAZ) width.
Abstract: Aluminium alloy 7039 is an Al-Mg-Zn alloy employed in aircraft, automobiles, infantry combat vehicles and high speed trains due to their low density, high specific strength and excellent corrosion resistance. Pulsed Gas Tungsten Arc Welding is used in this study for welding AA7039. The influence of the various GTAW process parameters, i.e. Pulse Current, Base current and Pulse Frequency on welds were studied for properties of weld like microhardness, ASTM grain size number and heat affected zone (HAZ) width. The settings of the process parameters were determined by using Taguchi's experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA), and regression analyses are employed to find the optimal process parameter levels and to analyze the effect of these parameters on the weld properties. Confirmation test with the optimal levels of welding parameters was carried out in order to illustrate the effectiveness of the Taguchi optimization method.
16 citations
TL;DR: In this paper, a neuro-fuzzy modeling approach is presented to provide adaptive control for the automatic process parameter adjustment, providing control over weld bead formation during the welding process.
Abstract: Process parameters modeling have always been one of the key aspects in development of an adaptive control of arc welding process. The welding process parameters are inherently nonlinear, time-delayed, and interdependent, and their on-time adjustment highly influences a sound weld bead formation and process monitoring. During the welding process, parameters control is the primary goal to leads a quality welding. Moreover, the final weld joint behavior, i.e., residual stress, welding strength, and micro-crack formation are generally observed after cooling of the weld product. Thus, it has always been a difficult task to control mechanical properties of a final weld joint. To obtain the best mechanical properties, the final weld joint characteristics needed to be controlled and predicted during the process itself by precise adjustment of the process parameters. The paper presents a neuro-fuzzy modeling approach to provide adaptive control for the automatic process parameter adjustment. Three input parameters wire feed speed, welding gap, and torch speed are modeled with welding current output, providing control over weld bead formation during the welding. The same input process parameters are also modeled to predict final weld joint characteristics, i.e., dilution ratio, hardness of weld bead, hardness of fused zone, and bead width. In order to ascertain the effectiveness of the neuro-fuzzy modeling approach, multiple regression models were also developed to compare the performances.
16 citations
Patent•
29 May 2014
TL;DR: In this paper, a set point obtaining portion of a feedback controlling system for calculating, and outputting to an actuator, a manipulated variable based on the set point and a process variable, and a actuator controlling portion that changes a gain of the actuator so as to have an effect of a change in the process variable.
Abstract: The controlling device includes a set point obtaining portion that obtains a set point from a feedback controlling system for calculating, and outputting to an actuator, a manipulated variable based on the set point and a process variable, and an actuator controlling portion that changes a gain of an actuator so as to have an effect of a change in the process variable that is produced through the change in the gain of the actuator tracking a change in the set point.
16 citations