Modified short-run statistical process control for test and measurement process

Koh, Chin Kok; Chin, Jeng Feng; Kamaruddin, Shahrul

doi:10.1007/s00170-018-2776-1

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…These can be based on coding systems such as those proposed by Al-Salti and Statham 12 and Zhu et al 13 or on clustering algorithms as introduced by Wiederhold. 14 Shewhart-styled or similar charting techniques for grouped characteristics are also presented by Bothe, 15 Evans and Hubele, 16 Kimbler and Sudduth, 17 Koh et al, 18,19 Lin et al, 20 Koons and Luner, 21 and Wheeler. 22 A main problem with these charts is the requirement that samples from different processes follow nearly identical distributions.…”

Section: Introductionmentioning

confidence: 99%

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Kostyszyn

Brandstätter

Vollmer

et al. 2021

Quality & Reliability Eng

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With ISO 7870-8, a standardized application of charting techniques for short runs and small mixed batches was presented in 2017. Similar to various scientific approaches, it requires that sample values from grouped processes follow nearly identical distributions. In practice, however, there tend to be differences between distribution parameters. Moreover, equal parameters do not ensure that distributions are properly aligned to the center line and control limits of the chart. These facts can lead to undesired control chart performances which can be expressed by average run lengths (ARL) during in-control and out-of-control conditions. In this work, a statistical test for sufficient control chart performances during monitoring of grouped processes based on preliminary samples is proposed. Control chart performances are defined as sufficient when they deviate within acceptable ranges from usual performances during single process monitoring in mass production. The ARL resulting from estimated distributions and planned production sequences is used as test statistic and calculated via the Markov chain approach. Exemplary tests are executed for scenarios with individuals and cumulated sum (CUSUM) charts. A simulative determination of error rates resulting from the ARL-based testing demonstrates its effectiveness in testing for sufficient control chart performances compared to an indirect testing with Levene's test and a oneway analysis of variance (ANOVA). K E Y W O R D Saverage run length, control chart performances, grouped processes monitoring, ISO 7870-8, short run SPC This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Kostyszyn

Brandstätter

Vollmer

et al. 2021

Quality & Reliability Eng

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show abstract

“…Several possible sources of error are equipment accuracy, operator mistakes, environmental factors and random noise. Measurement errors can lead to unnecessary process adjustments and loss of confidence in SPC [32]. Third, the production of customised products and services are characterised by complex designs and processes, requiring more flexible quality management practices.…”

Section: Introductionmentioning

confidence: 99%

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Arellano

Nguyen

Hinton

et al. 2021

Preprint

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With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology and visualisation tool based on data analytics that allows the practitioner to anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine's settings are needed (tool replacement, repositioning the axis, etc). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.

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A data analytics model for improving process control in flexible manufacturing cells

Martínez-Arellano

Nguyen

Hinton³

et al. 2022

Decision Analytics Journal

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Modified short-run statistical process control for test and measurement process

Cited by 5 publications

References 35 publications

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

A data analytics model for improving process control in flexible manufacturing cells

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