Off-line quality control

The performance of a product or process depends on a large number of factors. The problem is to find such settings of these factors that the product performs well under a number of conditions and during its intended life time. For this demand the product has to be robust against a number of factors and/or variables that disturb the operation of it, these factors are called noise factors. 

Taguchi has classified the noise factors into three types  

Outer noise - environmental variables that affect the performance of a product. For example temperature, dust, humidity. 

Inner noise - changes in material properties through usage, product deterioration, wear. 

Variational noise - differences between the individual manufactured units, manufacturing variations. 

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Kackar, R.N. (1985), Off-Line Quality Control, Parameter Design, and the Taguchi Method, J. Qual. Technol, 17, 176-209. 

In pharmaceutical technology, quality assurance of the pharmaceutical formulation is important. When a pharmaceutical formulation is produced, on-line quality monitoring and control has to be performed in order to check the quality of the outgoing products. Methodology to perform this task is Statistical Process Control (SPC) and is not included in this book. Good text books in the area of SPC exists [6-9]. In this book the focus is on off-line quality control, e.g. how to make products that are intrinsic robust against process variations. 

R.N. Kackar, Off-line Quality Control, Parameter Design, and The Taguchi Method, Journal of Quality Technology, 17 (1985) 176-188. 

Off-line quality control. A collection of methods to achieve the demanded quality. These methods enclose the following stages ... 

Because Taguchi has made no immediate coupling between the concept of loss-functions (the more philosophical part) and the off-line quality control methods (the more concrete part), the loss functions probably serve best as an important background concept rather than a practical working tool. 

Only the orthogonal arrays are described in this chapter because they show in the clearest way how the off-line quality control works. The linear graphs and the signal-to-noise ratios are not dealt with here because the first does not add anything to the understanding (it is only a practical tool to set up an array), the latter is not recommended to be used by a number of authors because of the ambiguous character of these ratios. 

G. Taguchi and Y. Wu, Introduction to Off-Line Quality Control, Central J an Quality Control Association, Nagoya, 1980. 

Decisions about the fitness of the design of a product. This is off-line quality control, as... 

Taguchi, G., emd Wu, Y. (1979), Introduction of Off-Line Quality Control, Meieki Nakamura-Ku Magaya, Japan. 

Higle, J.L. and Sen, S., 1996, Stochastic decomposition, Kluwer Academic Publisher. Kalgnanam, J.R. and Diwekar, U.M., 1997, An efficient sampling technique for off-line quality control. Technometrics, 39(3), 308. 

We now consider Taguchi s methods for off-line quality control. Kacker " defines off-line quality control methods as technical aids for quality and cost control at the product and process design stages in the product s development cycle . In contrast to this he defines on line quality control methods as technical aids for quality and cost control in manufacturing . 

G. Taguchi, Off-line and on-line quality control systems , in Proceedings of the International Conference on Quality Control ,.Tokyo, Japan, 1978. 

Abstract The application of off-line robot programming techniques for tasks which require a high precision is hampered by the absence on-line quality control techniques. An essential component for quality control is a low cost recalibration system which can be applied on-line during the production cycles of the robot. In this paper we present a low-cost calibration system for partial recalibration after repair or failure and for preventive maintenance. The system consists of a measuring device and software to perform parameter estimation. The system uses an non-tactile optical sensor. A suitable error model is used so that only a relatively small number of parameters has to be estimated to update the kinematic model of the robot. 

Statistical process control (SPC) is an important on-line method in real time by which a production process can be monitored and control plans can be initiated to keep quality standards within acceptable limits. Statistical quality control (SQC) provides off-line analysis of the big picture such as what was the impact of previous improvements. It is important to understand how SPC and SQC operate. 

There are in-line LC/spectroscopic systems available, but in most cases it is easier to carry out a semi-preparative separation, collect the material and carry out the spectroscopic examination off-line. However, for routine quality control analyses, where the sample... 

Many traditional laboratory/off-line methods are now moving in the direction of in-process applications. Online GC had already been introduced in the 1950s. Using chip- and microsystem technology, xGC is now being introduced, which achieves analysis times of 30 seconds and is therefore suitable for quality control. SPME-p,GC is potentially useful for process analysis. 

In general, the laboratory in a plant is mainly a quality control laboratory. It will consist of all the off-line equipment necessary to determine whether the product and raw materials meet the desired specifications, and whether all the waste streams meet the criteria set by local, federal, and state authorities. 

First and most importantly, real-time NIR monitoring enabled real-time control of the process. For a given product, the molecular weight and end-group balance in the prepolymer exiting the front end or melt part of the process must be controlled at specified levels in order for the back end or solid-phase part of the process to successfully produce the intended polymer composition. In addition, the variability in prepolymer composition must be controlled with very tight tolerances to keep the variation in final product composition within specification limits. Since the process dynamics in the front end were more rapid than those in conventional PET processes, the conventional analytical approach involving off-line analysis of samples obtained every 2-A hours was not sufficient to achieve the desired product quality. 

Expert systems can also be used off-line as advisory systems. If some leeway is left in the process specifications, operators can even be instructed to make simple variations in the process. Hajicek developed an expert system for troubleshooting injection molding, which was designed to be used by operators to adjust controls as needed whenever part quality suffered [20], These expert systems do not necessarily plan an entire cycle. As we will see in the section on in-process control, however, they can be used to do so. There are even specifications that include rules for altering the cure cycle in response to the results of quality acceptance tests such as flow and volatiles, a primitive set of heuristics. 

The relatively extensive availability of laboratory resources for quality control is mostly a reaction to the regulatory framework of product release criteria testing. The existence of quality control laboratories in most API manufacturing sites then leads to the development of in-process tests with an off-line laboratory method. 

Wenz and colleagues at Bayer Polymers Inc. describe the use of Raman spectroscopy to monitor the progress of a graft emulsion polymerization process, specifically the manufacture of ABS graft copolymer, in order to select the appropriate reaction termination point.40 Early termination reduces product yield and results in an extra product purification step termination too late reduces product quality. As Figure 5.5 illustrates, the reaction composition over time is not smooth and predictable, making it unlikely that off-line analysis would ever be fast enough to ensure correct control decisions. 

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