Comparative analysis of stability and capability of two drilling processes in the cast product using SPC tools

• Autorzy: Knop Krzysztof

Identyfikatory

DOI

10.2478/czoto-2025-0016

• Języki publikacji: EN

Abstrakty

EN

This article presents the results of comparative studies of the machining processes of holes B and C, conducted under batch production conditions at a foundry enterprise. The analyzed qualitative characteristic was the hole diameter, which is a key dimensional parameter of the tested casting product. The aim of the study was to evaluate both drilling processes using Statistical Process Control (SPC) tools to identify the superior process in terms of stability over time and quality capability. The methodology included descriptive statistics, histogram and box plot analysis, Bland-Altman agreement testing, normality verification, and Johnson-transformed control charts. Capability indices (Cp, Cpk) and target-oriented indices (Cpm, Cpmk) were calculated using the Clements percentile method to ensure reliable interpretation for non-normal data. The results confirmed that both processes were statistically stable and demonstrated very high capability, with Cp and Cpk values exceeding 2.0, thus indicating a negligible risk of producing out-of-tolerance parts. Nonetheless, important differences were observed: process B showed lower short-term variability, nearly perfect centering within the tolerance field, and stronger capability toward the lower specification limit. In contrast, Process C, although characterized by slightly higher variability, achieved closer alignment with the nominal dimension and more balanced capability across tolerance limits, as reflected in its higher Cpm and Cpmk values. These findings highlight the need to combine stability, capability, and accuracy-to-target analyses to obtain a comprehensive picture of process quality performance, especially under conditions of asymmetric specification limits and non-normal data. From a practical perspective, both processes are stable and capable, suitable for further batch production. However, Process C can be considered generally superior due to its better alignment with the nominal value and greater actual process capability, which translates into a lower risk of producing out-of-spec products.

Słowa kluczowe

EN

foundry engineering; Statistical Process Control; SPC; control charts; process stability analysis; process capability analysis; comparative analysis

PL

inżynieria odlewnicza; Statystyczna Kontrola Procesu; SPC; wykresy kontrolne; analiza stabilności procesu; analiza zdolności procesu; analiza porównawcza

• Wydawca: De Gruyter
• Czasopismo: System Safety : Human - Technical Facility - Environment
• Rocznik: 2025
• Tom: Vol. 7, iss. 1
• Strony: 153--171
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Knop Krzysztof

• Czestochowa University of Technology, Poland

• https://orcid.org/0000-0003-0842-9584

Bibliografia

• 1.Ahmad, S., Abdollahian, M., Zeephongsekul, P., 2007. Process capability estimation for non-normal quality characteristics using Clements, Burr, and Box-Cox methods. ANZIAM Journal, 49, C642-C665.
• 2.Bland, J. M., Altman, D. G., 1986. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307-310. DOI: 10.1016/S0140-6736(86)90837-8
• 3.Breyfogle, F.W., 2003. Implementing Six Sigma: Smarter Solutions Using Statistical Methods. New Jersey: Wiley.
• 4.Brook, Q., 2010. Lean Six Sigma and Minitab. Haddenham: OPEX Resources.
• 5.Chang, L. K., Cheng, S. W., Spiring, F. A., 1988. A new measure of process capability: Cpm. Journal of Quality Technology, 20(3), 162-175. DOI: 10.1080/00224065.1988.11979102
• 6.Czajkowska, A., Ingaldi, M., 2021. Application of SERVQUAL and SERVPERF methods to assess the quality of teaching services - comparative analysis. Manufacturing Technology, 21(3), pp. 294-305. DOI: 10.21062/mft.2021.041
• 7.Czajkowska, A., Ingaldi, M., 2022. Influence of Steel Fibers Content on Selected Mechanical Properties - Experimental Tests. Manufacturing Technology, 22(3), pp. 267-278. DOI: 10.21062/mft.2022.039
• 8.Clements, J. A., 1989. Process capability calculations for non-normal distributions. Quality Progress, 22, 95-100.
• 9.Doğan, N. Ö., 2018. The Bland-Altman analysis is a frequently applied technique in studies that investigate the agreement between two methods of the same medical measurement. Journal of Clinical Epidemiology, 52(3), 307-310.
• 10.Giavarina, D., 2015. Understanding Bland Altman analysis. Biochemia Medica, 25(2), 141-151. DOI: 10.11613/BM.2015.015
• 11.Hamrol, A., 2018. Quality Management and Engineering. Warsaw: PWN. (in Polish)
• 12.Ignaszak, Z., Sika, R., 2012). Specificity of SPC procedures application in foundry in aspect of data acquisition and data exploration. Archives of Foundry Engineering, 12(4), pp. 65-70.
• 13.ISO 22514-2, 2017. Statistical methods in process management: Capability and performance - Party 2. Geneva: ISO.
• 14.Kane, V.E., 1986. Process capability indices. Journal of Quality Technology, 18(1), pp. 41-52.
• 15.Kiemele, M.J., Schmidt, S.R. Berdine, R.J., 2005. Basic Statistics: Tools for Continuous Improvement. Colorado Springs: Air Academy Press.
• 16.Knop, K., 2021. Managing and Improving the Drilling Process of Woodwork Furniture with the Use of SPC Tools. Manufacturing Technology, 21(4), pp. 492-501.
• 17.Knop, K., 2023. Statistical Multivariate Analysis of the Dosing Process Results for Predictive Production and Quality Management - a Case Study from the Food Industry. Scientific Papers of Silesian University of Technology. Organization and Management Series, 176, pp. 277-292.
• 18.Kotz, S., Johnson, N.L., 1993. Process Capability Indices. Londres: Chapman & Hall/CRC.
• 19.Montgomery, D.C., 2019. Introduction to Statistical Quality Control. New York: Wiley.
• 20.Leby, Ch.L., Knop, K., 2025. The impact of control plan on quality management in the automotive industry. Archives of Engineering Knowledge, 11(1), pp. 25-29.
• 21.Oakland, J., Oakland, R., 2018. Statistical Process Control (7th ed.). Abingdon, UK: Taylor & Francis Ltd.
• 22.Okokpujie, I. P., Bolu, C. A., Ohunakin, O. S., Akinlabi, E. T., & Adelekan, D. S., 2019. A review of recent application of machining techniques, based on the phenomena of CNC machining operations. Procedia Manufacturing, 35, 1054-1060.
• 23.Pearn, W.L., Kotz, S. Johnson, N.L., 1992. Distributional and Inferential Properties of Process Capability Indices. Journal of Quality Technology, 24, pp. 216 -231.
• 24.Ryan, T.P., 2011. Statistical Methods for Quality Improvement. New Jersey: Wiley.
• 25.TIBCO Statistica 14 Electronic Manual.
• 26.Wheeler, D.J., Chambers, D.S., 1992. Understanding Statistical Process Control. Knoxville: SPC Press.
• 27.Wheeler, D.J., 2000. Understanding Variation: The Key to Managing Chaos. Knoxville: SPC Press.
• 28.Wheeler, D.J., 2004. Advanced Topics in Statistical Process Control. The Power of Shewhart’s Charts. Second Edition. Knoxville: SPC Press.
• 29.Zontec Press, 2010. The book of statistical process control (2nd ed.). The Zontec Press.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).