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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 last phase of the resin flow during CRTM process is simplified and modeled as a one dimensional flow to obtain estimates for process time if the applied force is known.

34 citations

Journal ArticleDOI
TL;DR: In this article, the authors applied Taguchi's parameter design method, regression analysis, and the Davidon-Fenton-Powell method to determine the optimal process parameter settings of plastic injection molding under single quality characteristic considerations.
Abstract: Determining optimal process parameter settings is critical work that extraordinarily influences productivity, quality, and costs of production. Previously, numerous engineers conventionally used trial-and-error processes or Taguchi's parameter design method to determine optimal process parameter settings. However, the application of these methods has some shortcomings. This research applies Taguchi's parameter design method, regression analysis, and the Davidon-Fletcher-Powell method to propose a novel approach for determining the optimal process parameter settings of plastic injection molding under single quality characteristic considerations. This novel approach can avoid shortcomings that originate from the application of trial-and-error processes or the conventional Taguchi parameter design method. The research results revealed that the proposed novel approach can effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs.

33 citations

Journal ArticleDOI
TL;DR: In this article, the interaction effects between temperature, catalyst properties, fluidization conditions, and deposition time during carbon nanotube (CNT) synthesis by chemical vapor deposition in a fluidized bed were investigated.
Abstract: The interaction effects between temperature, catalyst properties, fluidization conditions, and deposition time during carbon nanotube (CNT) synthesis by chemical vapor deposition in a fluidized bed were investigated. While numerous investigations have attempted to correlate process parameters with CNT characteristics, selectivity and yield, the interaction between process parameters is often ignored. Parametric interactions in this process have been investigated using a factorial design methodology. Besides the main effects of synthesis temperature, deposition time, and catalyst type, the interaction parameters temperature−time and temperature−catalyst were found to significantly influence the resultant carbon and CNT yields. These results lay the foundation for a detailed parametric analysis toward the optimization of CNT synthesis in fluidized beds, which takes into account these interaction effects.

33 citations

Journal ArticleDOI
TL;DR: In this article, the effect of slurry process parameters on semisolid A356 alloy microstructures produced by the RheoMetalTM process was investigated and the effect on the performance of the slurry was evaluated.
Abstract: Determining the effect of slurry process parameters on semisolid A356 alloy microstructures produced by the RheoMetalTM process

33 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present two integrated models for understanding the behavior of a simple, single load-lock cluster tool, including a network model that evaluates the total lot processing time for a given sequence of activities.
Abstract: Cluster tools are highly integrated machines that can perform a sequence of semiconductor manufacturing processes. Their integrated nature can complicate analysis when evaluating how process changes affect the overall tool performance. This paper presents two integrated models for understanding the behavior of a simple, single loadlock cluster tool. The first model is a network model that evaluates the total lot processing time for a given sequence of activities. By including a manufacturing process model (in the form of a response surface model, or RSM), the model calculates the lot makespan, the total time to process a lot of wafers, as a function of the process parameter values and other operation times. This model allows us to quantify the sensitivity of total lot processing time with respect to process parameters and times. In addition, we present an integrated simulation model that includes a process model. For a given scheduling rule that the cluster tool uses to sequence wafer movements, we can use the simulation to evaluate the impact of process changes, including changes to product characteristics and changes to process parameter values. In addition, we can construct an integrated network model to quantify the sensitivity of total lot processing time with respect to process times and process parameters in a specific scenario. We also present an evaluation of the effectiveness of two different scheduling rules, push and pull. The examples presented here illustrate the types of insights that we can gain from using such methods. Namely, the lot makespan is a function not simply of each operation's process time, but specifically of the chosen process parameter values. Modifying the process parameter values may also have significant impacts on the manufacturing system performance, a consequence of importance that is not readily obvious to a process engineer when tuning a process. This result can be seen either with the decrease of raw process time causing little change to the makespan, or the extreme example in which this could cause an increase in makespan because of an inefficient scheduling rule. Additionally, because the cluster tool's maximum throughput, which is the inverse of the lot makespan, depends on the process parameters, the tradeoffs between process performance and throughput should be considered when evaluating potential process changes and their manufacturing impact.

33 citations


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