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.

Method for multiple process parameter matching

Abstract: A computer-implemented method for matching parameters of outputs generated by a first and second process The first process generates a first output having a characteristic measurable by a first parameter, and the second process generates a second output having the characteristic measurable by a second parameter A computer having a processing unit and memory is provided The computer generates a first model of the first parameter for the first process and a second model of the second parameter for the second process The computer generates a first simulated output of the first process using the first model A correction, which is a function of the second model and which compensates for the effect of the second process on the second parameter, is applied to the first simulated output to obtain a corrected output The second process is applied to the corrected output to generate with the computer thereby a third output matching the first parameter of the first output

The Effects of Laser and Electron Beam Spot Size in Additive Manufacturing Processes

Abstract: In this work, melt pool size in process mapped in power-velocity space for multiple processes and alloys. In the electron beam wire feed and laser powder feed processes, melt pool dimensions are then related to microstructure in the Ti-6Al-4V alloy. In the electron beam wire feed process, work by previous authors that related prior beta grain size to melt pool area is extended and a control scheme is suggested. In the laser powder feed process, in situ thermal imaging is used to monitor melt pool length. Real time melt pool length measurements are used in feedback control to manipulate the resulting microstructure. In laser and electron beam direct metal additive manufacturing, characteristics of the individual melt pool and the resulting final parts are a product of a variety of process parameters. Laser or electron beam spot size is an important input parameter that can affect the size and shape of a melt pool, and has a direct influence on the formation of lack-of-fusion and keyholing porosity. In this work, models are developed to gain a better understanding of the effects of spot size across different alloys and processes. Models are validated through experiments that also span multiple processes and alloys. Methods to expand the usable processing space are demonstrated in the ProX 200 laser powder bed fusion process. In depth knowledge of process parameters can reduce the occurrence of porosity and flaws throughout processing space and allow for the increased use of non-standard parameter sets. Knowledge of the effects of spot size and other process parameters can enable an operator to expand the usable processing space while avoiding the formation of some types of flaws. Based on simulation and experimental results, regions where potential problems may occur are identified and process parameter based solutions are suggested. Methods to expand the usable processing space are demonstrated in the ProX 200 laser powder bed fusion process. In depth knowledge of process parameters can reduce the occurrence of porosity and flaws throughout processing space and allow for the increased use of non-standard parameter sets.

Analysis and prediction on geometric characteristics of multi-track overlapping laser cladding

Abstract: Building the analytical model between process parameter and results is the prerequisite for explaining the complex process of laser cladding “laser-powder” interaction, as well as the critical guidance for processing conduction, prediction, and control of geometrical characteristics. This paper utilized response surface methodology (RSM) to study the effect on multi-track cladding width, flatness ratio, and dilution among the following major process parameters: laser power (LP), scanning speed (SS), gas flow (GF), and overlapping rate (OR). The experiment was based on four factors and five levels designed to build the model between processing parameters as input and processing results as output by RSM multiple regression analysis. Variance analysis and significant test was utilized to validate the model. By predicting the geometrical characteristics to optimize processing parameters, get the multi-track cladding layer with the maximum width, the minimum flatness ratio, and dilution. The predictive value and experiment results had a difference of 4.17, 3.68, and 3.56%, for width, flatness ratio, and dilution respectively, which provided another validation for this model. The model could be used on controlling of multi-tracking cladding geometrical characteristics, prediction of performance, and optimization of process parameters.

Method and apparatus for obtaining process characteristics in a self-tuning controller

Abstract: The characteristics of the field process system controlled by a self tuning controller are obtained by analyzing the open loop response of the process variable to a step applied at the controller's control output. A first-order system response with pure time delay is used to approximate a higher-order field process system's actual response. The overall deadtime and time constant of the first-order approximation are determined. The maximum slope of the process variable response to the step is stored along with an assigned time of its occurrence and an assigned process variable. The stored information is used to approximate the overall deadtime. In one embodiment the stored information is assumed to be the data obtained from a second-order response having a 10:1 time constant ratio and is used to find the assumed second-order time constants which are used to find the first-order time constant. In a second embodiment the first order time constant is determined by using a convergence point falling within a predetermined range between the actual response data and the first-order approximation. The step is not applied to the control output until the process variable has reached the initial steady state condition and the calculation of slope ceases when the process variable response reaches the final steady state condition. Alternative techniques are disclosed for determining the steady state condition.