Machine Tool Reliability by Bhupesh K. Lad

Author:Bhupesh K. Lad
Format: epub
ISBN: 9781119038948
Publisher: John Wiley & Sons, Inc.
Published: 2016-02-16T16:00:00+00:00

7.1 Development of Statistical Process Control (SPC)

Continual quality improvement as an imperative for the survival of a company requires the establishment of a process measuring system. In order to determine the possibilities for improving process effectiveness and efficiency, measurement data have to be arranged, processed and analyzed using adequate methods and techniques. A large number of factors influence each process and lead to output characteristics (process results) variations. As much as one tries to control the influencing factors, variations are unavoidable and therefore should be maintained on an acceptable level, while we strive towards their continual reduction. The first theoretical bases and mathematic statistic formulations applied in research papers can be found in the papers written by R. Fisher [157] after the First World War. The first practical attempts to apply statistics to quality control dated were in 1923, when the notion of producer’s risk and consumer’s risk in sampling was introduced. Great significance has been attributed to the work of Walter Shewhart [158], who developed the theory of control charts. Since then, the standard control charts originated by Shewhart in the 1930s have been improved in many ways due to the expansion of SPC applications. Interested readers can refer to review papers for detailed descriptions of various improvements in the field. For example, review papers by Epprecht [159], Jensen et al. [160], Sousa and Voss [161], Stoumbos et al. [162] and Woodall [163], provide excellent reviews of current uses of control charts, including the Cumulative Sum (CUSUM) and related methods. Celano [164] has presented a review of the most recent research contributions dealing with modeling the SPC inspection workstation and the randomness of the shift size. His paper can potentially open a new stream of research trying to improve the efficiency of the economic models in representing the manufacturing process stages where the chart should be implemented. A book by Al-Sultan and Rahim [165] presents a broad survey of state-of-the-art in optimization in quality control.

New methodologies are developed to provide tools that are more suitable for specific applications such as SPC for short-runs, SPC with auto-correlated process data, multivariate process control, process adjustment with feedback control, etc. In recent times, Engineering Process Control (EPC) has been used to control the continuous processes manufacturing discrete parts. It is also known as Automatic Process Control (APC) in which an appropriate feedback or feedforward control is used to decide when and by how much the process should be adjusted to achieve the quality target.

It is an integrated approach in which the concepts of design of experiments and robust design are also effectively used for designing control charts. EPC has been developed to provide an instantaneous response, counteracting changes in the balance of a process and applying corrective action to bring the output close to the desired target. The approach is to forecast the output deviation from the target that would occur if no control action were taken and then to act so as to cancel out this deviation [166].

Integrated or unified methods

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