Capability Analysis of Drift-Inherent Processes: Case of Nail Wire Drawing

Authors

  • S. M. Sackey Kwame Nkrumah University of Science and Technology, Ghana.
  • I. Ahmed Kwame Nkrumah University of Science and Technology, Ghana.

DOI:

https://doi.org/10.26437/ajar.31.10.2022.18

Abstract

Purpose: The central purpose of the study is to model the process capability of drift-inherent manufacturing processes by testing the efficacy of a novel approach that filters trend from raw process data before applying statistical process control tools. A secondary aim was to ascertain the intrinsic capability of the process following the filtering.

Design/Methodology/Approach: Specifically, the study focused on processes in a nail-wire drawing and tested a method for analysing data from naturally-drifting processes that involves filtering trends from data before applying appropriate tools to verify the state of statistical control and capability of the process. The physical foundation for this work is based on data collected from a nail-wire drawing process A total of 250 data points were gathered over 50 days in two successive instances of 125 points, each spanning 25 days. Data were checked for normality followed by mathematical conditioning to filter out the wear trend before analysis by normal statistical process capability and control chart procedures.

Findings: Results show that the proposed method is effective for tracking hidden effects in steadily drifting processes such as those associated with wear. After filtering, the data is found to fall within product specifications, though robust statistical control was still required through appropriate measures.

Research Limitation: To investigate the intrinsic nature of the process outside of the process, material wear is assumed to be the sole source of the inherent drift. In processes where several sources of inherent drift are present, this may pose a problem. Additionally, the study focused on just one plant; however, data from other similar plants will be needed to buttress the findings and widen the scope of applicability of the findings.

Practical implication:  The competitive pressures of today’s marketplace are increasingly forcing companies to place premium emphasis on product quality while aiming at the lowest costs possible. The study recommends continuous and sustained efforts to reduce variation in manufacturing processes to brighten firms’ competitive survival.

Social implication: The study will bring new knowledge to metal product manufacturers that can help them deliver high-quality products and value for money to consumers.  

Originality / Value: New insights afforded by the study’s approach include revelations of otherwise hidden measurement errors as well as undersized finishing-die. Any other out-of-control occurrences can then be more easily tracked and identified and root-cause analysis applied to eliminate them.  This is a practical study that seeks to develop an innovative way to monitor the quality of processes whose tracking is made difficult by inherent drift. The easy-to-adopt methodology can be implemented by metal product manufacturers grappling with drift-inherent processes.

Author Biography

S. M. Sackey, Kwame Nkrumah University of Science and Technology, Ghana.

He is an Associate Professor at the Department of Mechanical Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

References

Aravind, S., Shunmugesh, K., & Akhil, K. T. (2017). Process Capability Analysis and

Chalisgaonkar, R., & Kumar, J. (2014). Process capability analysis and optimization in

WEDM of commercially pure titanium. Procedia Engineering, 97, 758-766.

de-Felipe, D., & Benedito, E. (2017). A review of univariate and multivariate process

capability indices. The International Journal of Advanced Manufacturing Technology, 92(5), 1687-1705.

Duro (2017) Analyze drift and shift processing of data variation during volume production. https://www.durolabs.co/blog/2017-5-23-process-shifts-and-drifts/ [Accessed Oct 13 2022].

Koppel, S., & Chang, S. I. (2016). A process capability analysis method using adjusted

modified sample entropy. Procedia Manufacturing, 5, 122-131.

Kumar, V., Ranjan, N., & Singh, R. (2022). Process Parametric Optimization for 3D Printing

of ABS Based Multi-Structured Functional Prototypes.

Li, W., & Liu, G. (2022). Dynamic failure mode analysis approach based on an improved

Taguchi process capability index. Reliability Engineering & System Safety, 218, 108152

Liu, G., & Li, W. (2021). New dynamic reliability assessment method based on process

capability index and fault importance index. Quality Engineering, 33(1), 43-59.

Maaradji, A., Dumas, M., Rosa, M. L., & Ostovar, A. (2016, September). Fast and accurate

business process drift detection. In International Conference on Business Process Management (pp. 406-422). Springer, Cham.

McMahon, C., & Browne, J. (1998). CADCAM: principles, practice and manufacturing

management.

Montgomery, D. C. (2009). Statistical quality control (Vol. 7). New York: Wiley.

Ostovar, A., Leemans, S. J., & Rosa, M. L. (2020). Robust drift characterization from event

streams of business processes. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(3), 1-57.

Pawar, H. U., Bagga, S. K., & Dubey, D. K. (2021). Investigation of production parameters

for process capability analysis: A case study. Materials Today: Proceedings, 43, 196-202.

QI Macros Knowledge International Inc. Process Stability and Capability Analysis https://www.qimacros.com/lean-six-sigma-articles/process-capability-stability/ [Accessed 14 Oct 2022]

Rao, G. S., Albassam, M., & Aslam, M. (2019). Evaluation of bootstrap confidence intervals

using a new non-normal process capability index. Symmetry, 11(4), 484.

Saha, A., & Majumder, H. (2018). Performance analysis and optimization in turning of ASTM

A36 through process capability index. Journal of King Saud University-Engineering Sciences, 30(4), 377-383.

Selmi, S., Ben Amara, S., Ben Fredj, N., Kobi, A., & Ben Salah, I. (2018). Process capability

indices and X $$overline {X} $$, R control chart limit adjustments by taking into account measurement system errors. The International Journal of Advanced Manufacturing Technology, 95(5), 1919-1930.

Sharma, R., Singh, R., & Batish, A. (2020). On multi response optimization and process

capability analysis for surface properties of 3D printed functional prototypes of PVC reinforced with PP and HAp. Materials Today: Proceedings, 28, 1115-1122.

Singh, R., Ahuja, I. P. S., & Grover, S. (2018). Process Capability Analysis for Frictionally

Welded Dissimilar Polymeric Materials. Materials Today: Proceedings, 5(9), 18502-18509.

Singh, G., Singh, R., Singh, S., Bhardwaj, A., Singh, S., & Prakash, C. (2022). Comparison

of Ni-Cr based partial dentures prepared by thermoplastic and wax based investment casting: Mechanical, morphological and in-vitro analysis. Materials Today: Proceedings, 48, 938-945.

Tomohiro, R., Arizono, I., & Takemoto, Y. (2020). Economic design of double sampling

Cpm control chart for monitoring process capability. International Journal of Production Economics, 221, 107468.

White, K., Szarka III, J., Childress, A., & Jensen, W. (2020). A recommended set of indices

for evaluating process health. Quality Engineering, 33(1), 1-12.

Yeshchenko, A., Ciccio, C. D., Mendling, J., & Polyvyanyy, A. (2019, November).

Comprehensive process drift detection with visual analytics. In International Conference on Conceptual Modeling (pp. 119-135). Springer, Cham. [Accessed Oct 13 2022]

Zheng, C., Wen, L., Wang, J. (2017): Detecting process concept drifts from event logs. In: OTM CoopIS, pp. 524–542.

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Published

2022-11-03

How to Cite

Sackey, S. M., & Ahmed, I. (2022). Capability Analysis of Drift-Inherent Processes: Case of Nail Wire Drawing. AFRICAN JOURNAL OF APPLIED RESEARCH, 8(2), 264–279. https://doi.org/10.26437/ajar.31.10.2022.18