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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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the effect of FSW parameters such as tool rotational speed, welding speed and tool tilt angle on the mechanical properties of yield strength and% of elongation of friction stir welded dissimilar AA5083 and AA6061 aluminium alloy was studied.

35 citations

Journal ArticleDOI
Y.H. Kim1, Jong-Rae Cho1, H.S. Jeong1, Ki-Taek Kim, S.S. Yoon1 
TL;DR: In this paper, the effects of several significant process parameters on the process characteristics in the CONFORM process, such as material flow, defect occurrence, temperature and effective strain distributions, using DEFORM commercial FEM code are investigated.

35 citations

Journal ArticleDOI
22 May 2020
TL;DR: A hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations and demonstrates that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting.
Abstract: Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

35 citations

Journal ArticleDOI
TL;DR: In this article, an attempt has been made to optimize the process parameters of friction stir welding (FSW) for tensile strength and percentage elongation using Taguchi-based gray relation analysis (GRA).
Abstract: In this experimental study, an attempt has been made to optimize the process parameters of friction stir welding (FSW) for tensile strength and percentage elongation using Taguchi-based gray relation analysis (GRA). An orthogonal array of L9 has been implemented to fabrication of joints. The experiments have conducted according to the combination of rotational speed, tool tilt (TLT) and types of tool pin profile (TPP). The results revealed that the rotational speed is most significant process parameter with percentage contribution of 96.24 %. On optimization, tool rotation speed of 1,550 rpm, TLT angle of 4° and octagonal-type TPP have been found to be the best parameter setting for FSW process of aluminum 6061 and 6082 alloy welds. To ensure the robustness of GRA, a confirmation test was performed at selected optimal process parameter setting.

35 citations

Journal ArticleDOI
TL;DR: In this article, a hierarchical kernel partial least squares (HKPLS) is proposed for batch process monitoring, which does not need to estimate or fill in the unknown part of the variable trajectory deviation from the current time until the end.
Abstract: In this paper, new monitoring approach, hierarchical kernel partial least squares (HKPLS), is proposed for the batch processes. The advantages of HKPLS are: (1) HKPLS gives more nonlinear information compared to hierarchical partial least squares (HPLS) and multi-way PLS (MPLS) and (2) a new batch process monitoring using HKPLS does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The proposed method is applied to the penicillin process and continuous annealing process and is compared with MPLS and HPLS monitoring results. Applications of the proposed approach indicate that HKPLS effectively capture the nonlinearities in the process variables and show superior fault detectability.

35 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202329
202266
2021289
2020318
2019281
2018274