About: Process variable is a(n) research topic. Over the lifetime, 3983 publication(s) have been published within this topic receiving 43130 citation(s). The topic is also known as: process parameter.
Papers published on a yearly basis
TL;DR: In this paper, a mixture of different types of particles (Fe, Ni, Cu and Fe3P) specially developed for selective laser sintering (SLS) is described.
Abstract: Selective laser melting (SLM) is driven by the need to process near full density objects with mechanical properties comparable to those of bulk materials. During the process the powder particles are completely molten by the laser beam. The resulting high density allows avoiding lengthy post-processing as required with selective laser sintering (SLS) of metal powders. Unlike SLS, SLM is more difficult to control. Because of the large energy input of the laser beam and the complete melting of particles problems like balling, residual stresses and deformation occur. This paper will describe SLM applied to a mixture of different types of particles (Fe, Ni, Cu and Fe3P) specially developed for SLM. The different appearing phenomenons are discussed and the process optimization is described. The latter includes an appropriate process parameter adjustment and the application of special scanning strategies. Resulting parts are characterized by their microstructure, density and mechanical properties.
TL;DR: Applications are provided on the analysis of historical data from the catalytic cracking section of a large petroleum refinery, on the monitoring and diagnosis of a continuous polymerization process and on the Monitoring of an industrial batch process.
Abstract: Multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance are becoming more important because of the availability of on-line process computers which routinely collect measurements on large numbers of process variables. Traditional univariate control charts have been extended to multivariate quality control situations using the Hotelling T2 statistic. Recent approaches to multivariate statistical process control which utilize not only product quality data (Y), but also all of the available process variable data (X) are based on multivariate statistical projection methods (principal component analysis, (PCA), partial least squares, (PLS), multi-block PLS and multi-way PCA). An overview of these methods and their use in the statistical process control of multivariate continuous and batch processes is presented. Applications are provided on the analysis of historical data from the catalytic cracking section of a large petroleum refinery, on the monitoring and diagnosis of a continuous polymerization process and on the monitoring of an industrial batch process.
TL;DR: Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and the process variable space are considered.
Abstract: Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and the process variable space are considered. Traditional multivariate control charts based on X2 and T2 statistics ..
TL;DR: In this article, the selection of process parameters for obtaining an optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel is presented, and the modified Taguchi method is adopted to analyze the effect of each welding process parameter on the weldpool geometry, and then to determine the process parameters with the optimal welding pool geometry.
Abstract: In this paper, the selection of process parameters for obtaining an optimal weld pool geometry in the tungsten inert gas (TIG) welding of stainless steel is presented. Basically, the geometry of the weld pool has several quality characteristics, for example, the front height, front width, back height and back width of the weld pool. To consider these quality characteristics together in the selection of process parameters, the modified Taguchi method is adopted to analyze the effect of each welding process parameter on the weld pool geometry, and then to determine the process parameters with the optimal weld pool geometry. Experimental results are provided to illustrate the proposed approach.
••11 Feb 2016
TL;DR: The state-of-the-art in process monitoring and control for metal selective laser melting (SLM) processes is reviewed in this paper, where the authors present a review of the current state of the art.
Abstract: Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.
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