| Peer-Reviewed

Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components

Received: 6 February 2021    Accepted: 24 February 2021    Published: 4 March 2021
Views:       Downloads:
Abstract

The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA’s), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment “Down Time” of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.

Published in American Journal of Computer Science and Technology (Volume 4, Issue 1)
DOI 10.11648/j.ajcst.20210401.12
Page(s) 11-18
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

CNC, Quality Service, Maintenance, Life Cycle, Predictive Attributes

References
[1] Ameli M, Mansour S, Ahmadi-Javid A (2016), A multi-objective model for selecting design alternatives and end-of-life options under uncertainty: A sustainable approach. Resources, Conservation and Recycling 109: 123–136, DOI 10.1016/j.resconrec.2016.01.011, URL http://dx.doi.org/10.1016/j.resconrec.2016.01.011.
[2] Aydina, R, Brown A, Badurdeena F, Lia W, Roucha K, I. S. Jawahira, (2018), Quantifying impacts of product return uncertainty on economic and environmental performances of product configuration design, Journal of Manufacturing Systems, 48 (2018) 3-11.
[3] Bi L, Zuo Y, Tao F, Liao TW, Liu Z (2017) Energy- Aware Material Selection for Product with Multicomponent Under Cloud Environment. Journal of Computing and Information Science in Engineering 17 (3): 031007, DOI 10.1115/1.4035675.
[4] Chen SC, Yang CC, Lin WT, Yeh TM, Lin YS. (2007) Construction of key model for knowledge management system using AHP-QFD for semiconductor industry in Taiwan. Journal of Manufacturing Technology Management, 18: 576-598.
[5] Fazleena Badurdeen a, b, Ridvan Aydin a, Adam Brown a, b (2018), A multiple lifecycle-based approach to sustainable product. configuration design, Journal of Cleaner Production 200 (2018) 756e769.
[6] Govindan K, Madan Shankar K, Kannan D (2015) Sustainable material selection for construction industry - A hybrid multi criteria decision making approach. Renewable and Sustainable Energy Reviews 55: 1274–1288, DOI 10.1016/j.rser.2015.07.100.
[7] Hosseinijou SA, Mansour S, Shirazi MA (2014) Social life cycle assessment for material selection: A case study of building materials. International Journal of Life Cycle Assessment 19 (3): 620–645, DOI 10.1007/s11367-013-0658-1.
[8] Jayakrishna K, Vinodh S, Sakthi Sanghvi V, Deepika C (2016) Application of GRA for Sustainable Material Selection and Evaluation Using LCA. Journal of The Institution of Engineers (India): Series C 97 (3): 309, DOI 10.1007/s40032-016-0283-4, URL http://dx.doi.org/10.1007/s40032-016-0283-4.
[9] Jee D-H, Kang K-J. (2000) A method for optimal material selection aided with decision making theory. Materials & Design, 22: 199-206.
[10] Kaewunruen S, Lian Q.(2018) Digital twin aided sustainability-based lifecycle management for railway turnout systems. Journal of Cleaner Production 2019; 228: 1537–51.
[11] Kucukkoc I, Buyukozkan K, Satoglu SI, Zhang DZ (2015) A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem. Journal of Intelligent Manufacturing pp 1–13.
[12] Lim KYH Zheng P, Chen C-H. (2019) A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing 2019: 1–25.
[13] Mayyas A, Shen Q, Mayyas A, Abdelhamid M, Shan D, Qattawi A, Omar M (2011) Using Quality Function Deployment and Analytical Hierarchy Process for material selection of Body-In-White. Materials & Design 32 (5): 2771–2782, DOI 10.1016/j.matdes.2011.01.001.
[14] Mayyas A, Qattawi A, Omar M, Shan D (2012a) Design for sustainability in automotive industry: A comprehensive review. Renewable and Sustainable Energy Reviews 16 (4): 1845–1862.
[15] Mayyas A, Omar MA, Hayajneh MT (2016) Eco material selection using fuzzy TOPSIS method. International Journal of Sustainable Engineering 9 (5): 292–304, DOI 10.1080/19397038.2016.1153168.
[16] Mia, M., Morshed, M. S., Kharshiduzzaman, M., Razi, M. H., Mostafa, M. R., Rahman, S. M. S., & Kamal, A. M. (2018). Prediction and optimization of surface roughness in minimum quantity coolant lubrication applied turning of high hardness steel. Measurement: Journal of the International Measurement Confederation, 118, 43–51. https://doi.org/10.1016/j.measurement.2018.01.012.
[17] Rao RV, Davim JP. (2008) A decision-making framework model for material selection using a combined multiple attribute decision-making method. The International Journal of Advanced Manufacturing Technology, 35: 751-760.
[18] Saaty, T. (1990) How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48 (1): 9-26.
[19] Sapuan S. M. (2001) A knowledge-based system for materials selection in mechanical engineering design. Materials & Design 22 (8): 687-695.
[20] Sapuan SM, Jacob MSD, Mustapha F, Ismail N. (2002) A prototype knowledge based system for material selection of ceramic matrix composites of automotive engine components. Materials and Design; 23: 701–708.
[21] Sapuan SM, Abdalla HS. (1998) A prototype knowledge-based system for the material selection of polymeric-based composites for automotive components. Composites Part A: Applied Science and Manufacturing, 29: 731-742.
[22] Shehab E, Abdalla H. (2002) An Intelligent Knowledge-Based System for Product Cost Modelling. International journal of advanced manufacturing technology; 19: 49–65 Sharma S. (1996) Applied multivariate techniques. John Wiley & Sons, Inc. New York, NY, USA.
[23] Steuer RE, Piercy CA (2005) A regression study of the number of efficient extreme points in multiple objective linear programming. European Journal of Operational Research 162 (2): 484–496.
[24] Volpe Lovato, A., Hora Fontes, C., Embiruçu, M., & Kalid, R. (2018). A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control. Computers & Industrial Engineering, 115, 167–189. https://doi.org/10.1016/j.cie.2017.11.008.
[25] Witik RA, Payet, Michaud V, Ludwig C, E JA (2011) Assessing the life cycle costs and environmental performance of lightweight materials in automobile applications. Composites Part A: Applied Science and Manufacturing 42 (11): 1694–1709, DOI 10.1016/j.compositesa.2011.07.024, URL http://dx.doi.org/10.1016/j.compositesa.2011.07.024, World Commission on Environment and Development (1987) Our common future. Oxford University Press.
[26] Xia T, Dong Y, Xiao L, Du S, Pan E, Xi L. (2018) Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering and System Safety 2018; 178: 255–68.
[27] Zarandi MHF, Mansour S, Hosseinijou SA, Avazbeigi M (2011) A material selection methodology and expert system for sustainable product design. International Journal of Advanced Manufacturing Technology 57 (9-12): 885–903, DOI 10.1007/s00170-011-3362-y.
[28] Zheng P, Sivabalan (2020) AS. A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment. Robotics and Computer- Integrated Manufacturing 2020; 64: 101958.
Cite This Article
  • APA Style

    Moriarty Kevin. (2021). Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components. American Journal of Computer Science and Technology, 4(1), 11-18. https://doi.org/10.11648/j.ajcst.20210401.12

    Copy | Download

    ACS Style

    Moriarty Kevin. Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components. Am. J. Comput. Sci. Technol. 2021, 4(1), 11-18. doi: 10.11648/j.ajcst.20210401.12

    Copy | Download

    AMA Style

    Moriarty Kevin. Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components. Am J Comput Sci Technol. 2021;4(1):11-18. doi: 10.11648/j.ajcst.20210401.12

    Copy | Download

  • @article{10.11648/j.ajcst.20210401.12,
      author = {Moriarty Kevin},
      title = {Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components},
      journal = {American Journal of Computer Science and Technology},
      volume = {4},
      number = {1},
      pages = {11-18},
      doi = {10.11648/j.ajcst.20210401.12},
      url = {https://doi.org/10.11648/j.ajcst.20210401.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210401.12},
      abstract = {The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA’s), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment “Down Time” of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.},
     year = {2021}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components
    AU  - Moriarty Kevin
    Y1  - 2021/03/04
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajcst.20210401.12
    DO  - 10.11648/j.ajcst.20210401.12
    T2  - American Journal of Computer Science and Technology
    JF  - American Journal of Computer Science and Technology
    JO  - American Journal of Computer Science and Technology
    SP  - 11
    EP  - 18
    PB  - Science Publishing Group
    SN  - 2640-012X
    UR  - https://doi.org/10.11648/j.ajcst.20210401.12
    AB  - The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA’s), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment “Down Time” of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.
    VL  - 4
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, the United Sates

  • Sections