Model-based Quality Control Research Articles

Model-Based Quality Control System for Error Reduction in the Thermoforming Process

Model-based quality control has the potential to reduce the reject rate in the production of fiber-reinforced plastics (FRP) components. After all the cross-market establishment of FRP, undesirable quality deviations often occur with new materials or component shapes. The quality control uses the component quality (e.g. component angle, crystallinity, fiber orientation, pore content) as the control variable. As a key component of the control, a process model is developed to link the process parameters (press pressure, press duration and tool temperature) with the quality parameters. Knowledge of the process-determining cause-effect relationships is necessary to ensure that different quality parameters are in the target value at the same time. Based on experimental tests, these interrelationships are determined using methods of statistical test planning and serve as the basis for model-based quality control. As a result, it has been shown that the targeted control of the component angle is possible in a range of about ±1° by using the control parameters, tool temperature and pressure, which have a significant influence on the quality. In the next step, further quality characteristics are included in the control system in order to demonstrate the ability to control the quality of complex component specifications. Model-based quality control is particularly promising for the reduction of the process run-in phase and thus for the reduction of the reject rate.

Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogeneticstimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.

Principal Surface Model Based Quality Control Approach for Batch/Semi-Batch Processes

Batch/semi-batch quality control has attracted much attention from chemical engineering researchers recently. In this work, a novel empirical model based quality control approach for batch processes is developed. The model implemented is based on independent nonlinear principal components obtained by means of principal surface analysis- a very hot topic in the area of statistical modeling. The partial initial conditions and intermediate measurements obtained from each batch are analyzed using this approach to extract the independent nonlinear principal components. An empirical model with the nonlinear principal components as inputs is built to predict product quality. Correcting actions based on the initial conditions and intermediate measurements are taken based on the predictions obtained using the empirical model. In this work, the problem formulation and solution method are presented. The illustrative examples include a simulation example and an experimental study on the batch reactive distillation process. The simulation and experimental results show that the proposed approach is highly promising.

Using qualitative observations for process tuning and control [IC manufacture

Many qualitative properties of the product and the process are of interest during semiconductor manufacturing. One of the typical examples is the sidewall surface roughness of an etched polysilicon line. These properties are important since they affect directly the quality and performance of the integrated circuit (IC) devices being built. Traditionally, however, they are treated informally and subjectively as tacit knowledge in the processing arena. In this paper, we present a systematic approach to modeling and controlling such qualitative properties. This approach is based on treating qualitative process variables as categorical data that can be better understood with the help of formal statistical analysis known as logistic regression. This analysis reveals important relationships between the input process settings and the qualitative process output responses in a way that is similar to linear regression analysis for conventional numerical variables. Similarly, categorical process variables can be used for process control, which is driven by a probabilistic model of the categorical variables. We show how categorical models can be used to tune a process and, later, to control it via statistical process control (SPC) charts, model-based quality control techniques, and adaptive run-by-run controllers.

050 Model-based quality control of baker's yeast Sacchromyces Cerevisiae

050 Model-based quality control of baker's yeast Sacchromyces Cerevisiae

Electrovacuum Glass Quality Control System Transferring

Abstract In this paper we discuss the problems of the model based quality control of the electrovacuum glass production processes. We focuse on the problem of such systems transfer in order to intensify their implementation at the typical plants. A flexible analytical decision rules are proposed to support a prior fault detection of the typical model estimated using training data set at the new experimental conditions with the different level of addtive noise. Theoretical, as well as practical aspects of these methodology will be considered. Finally, our industrial experience with these is discussed.