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.

Efficient Homogeneously Weighted Moving Average Chart for Monitoring Process Mean Using an Auxiliary Variable

Abstract: In this paper, we propose an efficient control chart for monitoring small shifts in a process mean for scenarios where the process variable is observed with a correlated auxiliary variable. The proposed chart, called an auxiliary homogeneously weighted moving average (AHWMA) chart, is a homogeneously weighted moving average type control chart that uses both the process and auxiliary variables in the form of a regression estimator to provide an efficient and unbiased estimate of the mean of the process variable. We provide the design structure of the chart and examine its performance in terms of its run length properties. Using a simulation study, we compare its run length performance with several existing methods for detecting a small shift in the process mean. Our simulation results show that the proposed chart is more efficient in detecting a small shift in the process mean than its competitors. We provide a detailed study of the chart's robustness to non-normal distributions and show that the chart may also be designed to be less sensitive to non-normality. We give some recommendations on the application of the chart when the process parameters are unknown and provide an example to show the implementation of the proposed new technique.

Gain-adaptive process control

Abstract: In a process control system, a control signal to the process is provided to a process model having a predictive element and a delay element adapted to reproduce as closely as possible the action of the measured process variable to the control signal. The variable input signal to the process controller, upon which generation of the process control signal is based, is generated by multiplying the signal from the predictive portion of the process model by a correction factor generated in response to the ratio of the actual process measurement signal to the delayed model output signal.

Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization

Abstract: Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding industry. Selecting the proper process conditions for the injection molding process is treated as a multi-objective optimization problem, where different objectives, such as minimizing product weight, volumetric shrinkage, or flash present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an experiment-based optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process. The development integrates Taguchi’s parameter design method, neural networks based on PSO (PSONN model), multi-objective particle swarm optimization algorithm, engineering optimization concepts, and automatically search for the Pareto-optimal solutions for different objectives. According to the illustrative applications, the research results indicate that the proposed approach can effectively help engineers identify optimal process conditions and achieve competitive advantages of product quality and costs.

Automated process parameter resetting for injection moulding: a fuzzy-neuro approach

Abstract: Optimal process parameter setting for injection moulding is difficult to achieve due to a large number of factors involved. Current practice in industry is to adjust the parameters based on the products' defects of test-run through trial and error. This process, however, requires enormous experience and is often time consuming. This paper reports an intelligent system employing fuzzy sets and neural networks which is able to predict the process parameter resetting automatically to achieve better product quality. The system is designed to be used in the test-run of injection moulding. Seven commonly encountered injection moulded product defects (short shot, flash, sink-mark, flow-mark, weld line, cracking, and warpage) and two key injection mould parameters (part flow length and flow thickness) are used as system input which are described using fuzzy terms. On the other hand, nine process parameter adjusters (pressure, speed, resin temperature, clamping force, holding time, mould temperature, injection holding pressure, back pressure, and cooling time) are the system output. A back-propagation neural network has been constructed and trained using a large number of {defects}→ {parameter adjusters} expert rules. The system is able to predict the exact amount to be adjusted for each parameter towards reducing or eliminating the observed defects. Testing in several real cases showed that the system produced satisfying results.