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.

Processing AlSi10Mg by selective laser melting: parameter optimisation and material characterisation

Abstract: Owing to their attractive combination of mechanical properties, high heat conductivity and low weight, the Al–Si alloys found a large number of applications in the Additive Manufacturing field for automotive, aerospace and domestic industries. However, due to their high reflectivity and heat conductivity, they are harder to process by Selective Laser Melting. This work elaborates on both the optimisation of process parameters, in order to get nearly fully dense parts, and the material properties resulting from this specific material process combination. A process parameter window is defined, in which the formed melt pool is stable and meets the set requirements. In this process window, the parameter set for optimal density is defined. It is shown that AlSi10Mg parts produced by SLM have mechanical properties higher or at least comparable to the cast material because of the very fine microstructure.

Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE)

Abstract: This paper presents the research done to determine the functional relationship between process parameters and tensile strength for the fused deposition modelling (FDM) process using the group method for data modelling for prediction purposes. An initial test was carried out to determine whether part orientation and raster angle variations affect the tensile strength. It was found that both process parameters affect tensile strength response. Further experimentations were carried out in which the process parameters considered were part orientation, raster angle, raster width and air gap. The process parameters and the experimental results were submitted to the group method of data handling (GMDH), resulting in predicted output, in which the predicted output values were found to correlate very closely with the measured values. Using differential evolution (DE), optimal process parameters have been found to achieve good strength simultaneously for the response. The mathematical model of the response of the tensile strength with respect to the process parameters comprising part orientation, raster angle, raster width and air gap has been developed based on GMDH, and it has been found that the functionality of the additive manufacturing part produced is improved by optimizing the process parameters. The results obtained are very promising, and hence, the approach presented in this paper has practical application for the design and manufacture of parts using additive manufacturing technologies.

Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions

Abstract: There has been a lot of research activity in the area of batch process analysis and monitoring for abnormal situation detection since the pioneer work of Nomikos and MacGregor [1–5]. However, some of the key ideas and the thought process that led to those first papers have been forgotten. Batch process data are dynamic data. The whole philosophy of looking at batch process data with latent variables was developed because batch process variables are both autocorrelated and cross-correlated. Statistical process control by definition checks deviations from a nominal behavior (a target). Therefore for statistical process control of batch processes we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross-correlations. An added advantage to modeling the deviations from the target trajectory is that a non-linear problem is converted to a linear one that it is easy to tackle with linear latent variable methods such as principal component analysis (PCA) and partial least squares (PLS). This paper first takes a critical look at the true nature of batch process data. The general case where variables are not present during the entire duration of the batch is addressed. It is then illustrated how proper centering (by taking the deviations from the target trajectory) can retain valuable information on auto- and cross-correlation of the process variables. This auto- and cross-correlation is only modeled with a certain types of models. Topics such as scaling and trajectory alignment are revisited and issues arising when using the indicator variable approach are addressed. The development of control charts for multiblock, multiway PCA/PLS is discussed. Practical issues related to applications in industry are addressed. Then some of the methods that have appeared in the literature are examined as to their assumptions, their advantages and disadvantages and their range of applicability. Finally the nature of transition data (start-ups, grade transitions) is discussed and issues related to aligning, centering and scaling such types of data are presented. Copyright © 2003 John Wiley & Sons, Ltd.

A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

Abstract: Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.

Method and apparatus for the production of process sensitive lithographic features

Abstract: A method for controlling the variation in process parameters using test structures sensitized to process parameter changes. Wavefront engineering techniques are used to make features of the test structure more sensitive to process changes. Focus and exposure parameters are adjusted in response to the measurements of the test structures. In another embodiment, the wavefront engineering features are placed to permit the test structure appearing on the reticle out of focus. The wavefront engineering feature is an OPC technique applied to the test structure to modify it. The OPC features are applied in an asymmetrical manner to the test structure and enable identifying the direction of process focus changes.