Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 36 Sayı: 2, 735 - 750, 01.06.2023
https://doi.org/10.35378/gujs.883367

Öz

Destekleyen Kurum

Başkent Üniversitesi

Proje Numarası

BA/FM-15

Kaynakça

  • [1] Eguti, C.C.A, Trabasso, E.L.G., “The virtual commissioning technology applied in the design process of a flexible automation system”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40: 396-408, (2018).
  • [2] Knopp, S., Dauzère-Pérès, S., and Yugma, C., “Flexible job-shop scheduling with extended route flexibility for semiconductor manufacturing”, In Proceedings of the Winter Simulation Conference, 2478-2489, (2014).
  • [3] Gonðcalves, J.P.M., “Robot interface in a flexible manufacturing cell”, Master's Thesis, Lehigh University, Lehigh (1992).
  • [4] Cutkosky, M. R., Fussell, P. S., and Milligan Jr, R., “Precision flexible machining cells within a manufacturing system (No. CMU-RI-TR-84-12)”, Carnegie-Mellon Univ. Pittsburgh Pa Robotics Inst., (1984).
  • [5] Yadav, A., Jayswal, S. C., “Evaluation of batching and layout on the performance of flexible manufacturing system”, The International Journal of Advanced Manufacturing Technology, 101: 1435–1449, (2019).
  • [6] Chan, F.T., “The effects of routing flexibility on a flexible manufacturing system”, International Journal of Computer Integrated Manufacturing, 14(5): 431-445, (2001).
  • [7] Lozano, S., Teba, J., Larrañeta, J., Onieva, L., and de Toledo, P. A., “Dynamic part—routing in a flexible manufacturing system”, Belgian Journal of Operations Research, 34(4): 16-28, (1994).
  • [8] Chandra, P., Tombak, M.M., “Models for the evaluation of routing and machine flexibility”, European Journal of Operational Research, 60(2): 156-165, (1992).
  • [9] Özkirim, M., Durmuşoğlu, M.B., “Dışsal rota esnekliğine sahip hücresel üretim sistemlerinin benzetim analizi” İTÜ Journal (D), 6(2): 41-52, (2010).
  • [10] Wadhwa, R.S., “Flexibility in manufacturing automation: A living lab case study of Norwegian metal casting SMEs”, Journal of Manufacturing Systems, 31: 444–454, (2012).
  • [11] Galbraith, L., Greene, T.J., “Manufacturing system performance sensitivity to selection of product design metrics”, Journal of Manufacturing Systems, 14(2): 71-79, (1995).
  • [12] Pérez-Pérez, M., Bedia, A-M., S., López-Fernández, M-C., and García-Piqueres, G., “Research opportunities on manufacturing flexibility domain: A review and theory-based research agenda”, Journal of Manufacturing Systems, 48: 9–20, (2018).
  • [13] Yadav, A., Jayswal, S.C., “Modelling of flexible manufacturing system: a review”, International Journal of Production Research, 56(7): 2464-2487, (2018).
  • [14] Bazargan-Lari, M., “Layout designs in cellular manufacturing”, European Journal of Operational Research, 112:258-272, (1999).
  • [15] Stecke, K.E., “Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems”, Management Science, 29 (3): 273–288, (1983).
  • [16] Stecke, K.E., “A hierarchical approach to solve machine grouping and loading problem of fms”, European Journal of Operation Research, 24: 369–378, (1986).
  • [17] He, Y., Stecke, K.E., and Smith, M. L., “Robot and machine scheduling with state-dependent part input sequencing in flexible manufacturing systems”, International Journal of Production Research, 54(22): 6736–6746, (2016).
  • [18] Kia, A., Baboli., N., Javadian, R., Tavakkoli-Moghaddam, M., and Khorrami, J., “Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible configuration by simulated annealing”, Computers & Operations Research, 39: 2642–2658, (2012).
  • [19] Khannan, M.S.A., Maruf, A., “Development of robust and redesigning cellular manufacturing system model considering routing flexibility, setup cost, and demand changes”, Proceedings of the Asia Pacific Industrial Engineering Management Systems Conference, Puket, (2012).
  • [20] Ahkioon, S., Bulgak, A.A., Bektas T., “Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration”, International Journal of Production Research, 47(6):1573–1600, (2009).
  • [21] Singholi, A., Ali, M., Sharma C., “Evaluating the effect of machine and routing flexibility on flexible manufacturing system performance”, International Journal of Services and Operations Management, 16(2): 240–261, (2013).
  • [22] Jain, V., Raj, T., “Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach”, International Journal of Production Economics, 171: 84–96, (2016).
  • [23] Mahmood, K., Karaulova, T., Otto, T., and Shevtshenko, E., “Performance analysis of a flexible manufacturing system performance.” Proceedings of the 50th CIRP Conference on Manufacturing Systems, Taichung City, (2017).
  • [24] Rauch, E., Spena, P.R., Matt, D.T., “Axiomatic design guidelines for the design of flexible and agile manufacturing and assembly systems for SMEs”, International Journal of Interactive Design and Manufacturing, 13: 1–22 (2019).
  • [25] Ojstersek, R., Buchmeister, B., “The impact of manufacturing flexibility and multi-criteria optimization on the sustainability of manufacturing systems”, Symmetry, 12: 157-179, (2020).
  • [26] Tsai, T.-N., Chen, L.-H., Guh, R.S., “Measuring machine-group flexibility: a case study for surface mount assembly line with different configurations”, Journal of Industrial and Production Engineering, 34(4):261-273, (2017).
  • [27] Suresh, N.C., “Toward an integrated evaluation of flexible automation investment”, International Journal of Production Research, 28: 1657–1672, (1990).
  • [28] Reddy, B.S.P., Rao, C.S.P., “A Hybrid multi objective GA for simultaneous scheduling of machines and AGV in FMS”, International Journal of Advance Manufacturing Technology, 31: 602–613, (2006).
  • [29] Shin, K.S., Park, J.O., and Kim. Y.K., “Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm”, Computers and Operations Research, 38: 702–712, (2011).
  • [30] Chan, F.T.S., Jiang, B., Tang, N.K.H., “The development of intelligent decision support tools to aid the design of flexible manufacturing systems”, International Journal of Production Economics, 65: 73–84, (2000).
  • [31] Borenstein, D., “A visual interactive multi criteria decision analysis model for FMS design”, International Journal of Advance Manufacturing Technology, 14: 848–857, (1998).
  • [32] Borenstein, D., “Intelligent decision support system for flexible manufacturing system design”, Annals of Operation Research, 77: 129–156, (1998).
  • [33] Bramhane, R., Arora, A., Chandra, H., “Simulation of flexible manufacturing system using adaptive neuro fuzzy hybrid structure for efficient job sequencing and routing”, International Journal of Mechanical Engineering and Robotics Research, 3(4): 33–48, (2014).
  • [34] Dosdogru, A. T., Gocken, M., Geyik, F., “Integration of genetic algorithm and monte carlo to analyze the effect of routing flexibility”, International Journal of Advanced Manufacturing Technology, 81: 1379–1389, (2015).
  • [35] Liu, Y-H., Huang, H.P., Lin, Y.S., “Attribute selection for the scheduling of flexible manufacturing systems based on fuzzy set-theoretic approach and genetic algorithm”, Journal of the Chinese Institute of Industrial Engineers, 22(1): 46-55, (2005).
  • [36] Gholipour-Kanani, Y., Tavakkoli-Moghaddam, R., Khorrami, A., “Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search”, Journal of the Chinese Institute of Industrial Engineers, 28(3): 192-205, (2011).
  • [37] Su, T.-L., Chen, H.-W., Lu, C-F., “Systematic Optimization for the evaluation of the microinjection molding parameters of light guide plate with TOPSIS-based Taguchi Method”, Advanced Polymer Technology, 29(1): 54–63, (2010).
  • [38] Hong, G.B., Su, T.L., “Statistical analysis of experimental parameters in characterization of ultraviolet-resistant polyester fiber using a TOPSIS-Taguchi method”, Iranian Polymer Journal, 21(12): 877-885, (2012).
  • [39] Lan, T.-S., “Taguchi optimization of multi objective CNC machining using TOPSIS”, Information Technology Journal, 8(6): 917-922, (2009).
  • [40] Liao, H.C., “Using PCR-TOPSIS to optimize Taguchi’s multi response problem”, International Journal of Advanced Manufacturing Technology, 22: 649-655, (2003).
  • [41] Lu, J.-C., Yang, T., Su, C.-T., “Analysing optimum push/pull junction point location using multiple criteria decision-making for multistage stochastic production system”, International Journal of Production Research, 50(19): 5523-5537, (2012).
  • [42] Yang, T., Chou, P., “Solving a multi-response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method”, Mathematics and Computers in Simulation, 68 (1): 9–21, (2005).
  • [43] İç, Y.T., Yıldırım, S., “MOORA-Based Taguchi Optimization for Improving Product or Process Quality”, International Journal of Production Research, 51(11): 3321–3341, (2013).
  • [44] Şimşek, B., İç, Y.T., and Şimşek, E.H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125: 18–32, (2013).
  • [45] Şimşek, B., İç, Y.T., “Multi-response simulation optimization approach for the performance optimization of an alarm monitoring center”, Safety Science, 66: 61–74, (2014).
  • [46] Şimşek, B., İç, Y.T., and Şimşek, E.H., “Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems”, International Journal of Computational Intelligence Systems, 9(3): 525-543, (2016).
  • [47] İç, Y.T., Elaldı, F., and Keçeci, B., “Topsis based taguchi optimization of machining characteristics in end milling operation of kevlar-epoxy composites”, Journal of The Chinese Society of Mechanical Engineers, 37(6): 653-662, (2016).
  • [48] İç, Y.T., Saraloğlu Güler, E., and Erbil Çakır Z., “Reducing uncertainty in a type j thermocouple calibration process”, International Journal of Thermophysics, 40(53): 1-22, (2019).
  • [49] Chavan P., Patil A., “Taguchi-based optimization of machining parameter in drilling spheroidal graphite using combined TOPSIS and AHP method”, Advances in Intelligent Systems and Computing, 949: 787-797, (2020).
  • [50] Gopal, P.M., Prakash, K.S., “Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC”, Measurement, 116:178–192, (2018).
  • [51] Nguyen, H.-P., Pham, V.-D., and Ngo, N.-V., “Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid”, The International Journal of Advanced Manufacturing Technology, 98: 1179–1198, (2018).
  • [52] Rajamanickam S., Prasanna J, and Sastry C.C., “Analysis of high aspect ratio small holes in rapid electrical discharge machining of super alloys using Taguchi and TOPSIS”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42: 99-105, (2020).
  • [53] Pradeep, N., Sundaram, K.S., and Kumar, M.P., “Multi-response optimization of electrochemical micromachining parameters for SS304 using polymer graphite electrode with NaNO3 electrolyte based on TOPSIS technique”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41: 323-333, (2019).
  • [54] Jain, A., Jain, P., Chan, F.T., and Singh, S., “A review on manufacturing flexibility”, International Journal of Production Research, 51(19): 5946–5970, (2013).
  • [55] Malhotra, M., Sharma, S., “Measurement equivalence using generalizability theory: an examination of manufacturing flexibility dimensions”, Decision Sciences, 39(4): 643–669, (2008).
  • [56] Wahab, M., Wu, D., and Lee, C., “A generic approach to measuring the machine flexibility of manufacturing systems”, European Journal of Operations Research, 186(1): 137–149, (2008).
  • [57] Ranjit, K.R., “A Primer on the Taguchi Method”. USA: Van Nostrand Reinhold Press, (1990).
  • [58] Phadke, M.S. “Quality Engineering Using Robust Design”. USA: Prentice Hall, (1989).
  • [59] Unal, R., Dean, E.B., “Taguchi Approach to Design Optimization for Quality and Cost: An Overview”. USA: NASA Press, (1990).
  • [60] http://seykoc.com.tr/. Access date: 14.12.2015.
  • [61] http://www.cadcamsektoru.com/makaleler/Talasli-Imalatta-Kullanilan-Kesici-TOOLlar-9949.htm. Access date: 09.15.2015.

Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance

Yıl 2023, Cilt: 36 Sayı: 2, 735 - 750, 01.06.2023
https://doi.org/10.35378/gujs.883367

Öz

Machine sequence flexibility is defined as the combination of operation and routing flexibilities in this study. Its importance in the performance level of a flexible manufacturing cell (FMC) is investigated in this study. Studies related to the effects of various flexibility types, such as routing flexibility, are available in the literature. For example, studies related to routing flexibility try to measure the effects of routing flexibility on the performance levels in the operation of manufacturing systems under their own manufacturing environments. Similarly, this study also aims to present a performance measurement model based on Taguchi methods to evaluate the effects of machine sequence flexibility factors on the FMC performance and obtain an optimum and robust performance level. Two crucial responses, such as manufacturing lead time (MLT) and surface roughness (SR) are analysed to optimize the FMC performance. Robot speed, cutting tool type, and work-part material type are taken as the three other input factors to show the importance of machine sequence flexibility with respect to the other inputs. The study presented in this paper points out that machine sequence flexibility is the most effective input factor among the four input factors in the performance of the FMC.

Proje Numarası

BA/FM-15

Kaynakça

  • [1] Eguti, C.C.A, Trabasso, E.L.G., “The virtual commissioning technology applied in the design process of a flexible automation system”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40: 396-408, (2018).
  • [2] Knopp, S., Dauzère-Pérès, S., and Yugma, C., “Flexible job-shop scheduling with extended route flexibility for semiconductor manufacturing”, In Proceedings of the Winter Simulation Conference, 2478-2489, (2014).
  • [3] Gonðcalves, J.P.M., “Robot interface in a flexible manufacturing cell”, Master's Thesis, Lehigh University, Lehigh (1992).
  • [4] Cutkosky, M. R., Fussell, P. S., and Milligan Jr, R., “Precision flexible machining cells within a manufacturing system (No. CMU-RI-TR-84-12)”, Carnegie-Mellon Univ. Pittsburgh Pa Robotics Inst., (1984).
  • [5] Yadav, A., Jayswal, S. C., “Evaluation of batching and layout on the performance of flexible manufacturing system”, The International Journal of Advanced Manufacturing Technology, 101: 1435–1449, (2019).
  • [6] Chan, F.T., “The effects of routing flexibility on a flexible manufacturing system”, International Journal of Computer Integrated Manufacturing, 14(5): 431-445, (2001).
  • [7] Lozano, S., Teba, J., Larrañeta, J., Onieva, L., and de Toledo, P. A., “Dynamic part—routing in a flexible manufacturing system”, Belgian Journal of Operations Research, 34(4): 16-28, (1994).
  • [8] Chandra, P., Tombak, M.M., “Models for the evaluation of routing and machine flexibility”, European Journal of Operational Research, 60(2): 156-165, (1992).
  • [9] Özkirim, M., Durmuşoğlu, M.B., “Dışsal rota esnekliğine sahip hücresel üretim sistemlerinin benzetim analizi” İTÜ Journal (D), 6(2): 41-52, (2010).
  • [10] Wadhwa, R.S., “Flexibility in manufacturing automation: A living lab case study of Norwegian metal casting SMEs”, Journal of Manufacturing Systems, 31: 444–454, (2012).
  • [11] Galbraith, L., Greene, T.J., “Manufacturing system performance sensitivity to selection of product design metrics”, Journal of Manufacturing Systems, 14(2): 71-79, (1995).
  • [12] Pérez-Pérez, M., Bedia, A-M., S., López-Fernández, M-C., and García-Piqueres, G., “Research opportunities on manufacturing flexibility domain: A review and theory-based research agenda”, Journal of Manufacturing Systems, 48: 9–20, (2018).
  • [13] Yadav, A., Jayswal, S.C., “Modelling of flexible manufacturing system: a review”, International Journal of Production Research, 56(7): 2464-2487, (2018).
  • [14] Bazargan-Lari, M., “Layout designs in cellular manufacturing”, European Journal of Operational Research, 112:258-272, (1999).
  • [15] Stecke, K.E., “Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems”, Management Science, 29 (3): 273–288, (1983).
  • [16] Stecke, K.E., “A hierarchical approach to solve machine grouping and loading problem of fms”, European Journal of Operation Research, 24: 369–378, (1986).
  • [17] He, Y., Stecke, K.E., and Smith, M. L., “Robot and machine scheduling with state-dependent part input sequencing in flexible manufacturing systems”, International Journal of Production Research, 54(22): 6736–6746, (2016).
  • [18] Kia, A., Baboli., N., Javadian, R., Tavakkoli-Moghaddam, M., and Khorrami, J., “Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible configuration by simulated annealing”, Computers & Operations Research, 39: 2642–2658, (2012).
  • [19] Khannan, M.S.A., Maruf, A., “Development of robust and redesigning cellular manufacturing system model considering routing flexibility, setup cost, and demand changes”, Proceedings of the Asia Pacific Industrial Engineering Management Systems Conference, Puket, (2012).
  • [20] Ahkioon, S., Bulgak, A.A., Bektas T., “Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration”, International Journal of Production Research, 47(6):1573–1600, (2009).
  • [21] Singholi, A., Ali, M., Sharma C., “Evaluating the effect of machine and routing flexibility on flexible manufacturing system performance”, International Journal of Services and Operations Management, 16(2): 240–261, (2013).
  • [22] Jain, V., Raj, T., “Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach”, International Journal of Production Economics, 171: 84–96, (2016).
  • [23] Mahmood, K., Karaulova, T., Otto, T., and Shevtshenko, E., “Performance analysis of a flexible manufacturing system performance.” Proceedings of the 50th CIRP Conference on Manufacturing Systems, Taichung City, (2017).
  • [24] Rauch, E., Spena, P.R., Matt, D.T., “Axiomatic design guidelines for the design of flexible and agile manufacturing and assembly systems for SMEs”, International Journal of Interactive Design and Manufacturing, 13: 1–22 (2019).
  • [25] Ojstersek, R., Buchmeister, B., “The impact of manufacturing flexibility and multi-criteria optimization on the sustainability of manufacturing systems”, Symmetry, 12: 157-179, (2020).
  • [26] Tsai, T.-N., Chen, L.-H., Guh, R.S., “Measuring machine-group flexibility: a case study for surface mount assembly line with different configurations”, Journal of Industrial and Production Engineering, 34(4):261-273, (2017).
  • [27] Suresh, N.C., “Toward an integrated evaluation of flexible automation investment”, International Journal of Production Research, 28: 1657–1672, (1990).
  • [28] Reddy, B.S.P., Rao, C.S.P., “A Hybrid multi objective GA for simultaneous scheduling of machines and AGV in FMS”, International Journal of Advance Manufacturing Technology, 31: 602–613, (2006).
  • [29] Shin, K.S., Park, J.O., and Kim. Y.K., “Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm”, Computers and Operations Research, 38: 702–712, (2011).
  • [30] Chan, F.T.S., Jiang, B., Tang, N.K.H., “The development of intelligent decision support tools to aid the design of flexible manufacturing systems”, International Journal of Production Economics, 65: 73–84, (2000).
  • [31] Borenstein, D., “A visual interactive multi criteria decision analysis model for FMS design”, International Journal of Advance Manufacturing Technology, 14: 848–857, (1998).
  • [32] Borenstein, D., “Intelligent decision support system for flexible manufacturing system design”, Annals of Operation Research, 77: 129–156, (1998).
  • [33] Bramhane, R., Arora, A., Chandra, H., “Simulation of flexible manufacturing system using adaptive neuro fuzzy hybrid structure for efficient job sequencing and routing”, International Journal of Mechanical Engineering and Robotics Research, 3(4): 33–48, (2014).
  • [34] Dosdogru, A. T., Gocken, M., Geyik, F., “Integration of genetic algorithm and monte carlo to analyze the effect of routing flexibility”, International Journal of Advanced Manufacturing Technology, 81: 1379–1389, (2015).
  • [35] Liu, Y-H., Huang, H.P., Lin, Y.S., “Attribute selection for the scheduling of flexible manufacturing systems based on fuzzy set-theoretic approach and genetic algorithm”, Journal of the Chinese Institute of Industrial Engineers, 22(1): 46-55, (2005).
  • [36] Gholipour-Kanani, Y., Tavakkoli-Moghaddam, R., Khorrami, A., “Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search”, Journal of the Chinese Institute of Industrial Engineers, 28(3): 192-205, (2011).
  • [37] Su, T.-L., Chen, H.-W., Lu, C-F., “Systematic Optimization for the evaluation of the microinjection molding parameters of light guide plate with TOPSIS-based Taguchi Method”, Advanced Polymer Technology, 29(1): 54–63, (2010).
  • [38] Hong, G.B., Su, T.L., “Statistical analysis of experimental parameters in characterization of ultraviolet-resistant polyester fiber using a TOPSIS-Taguchi method”, Iranian Polymer Journal, 21(12): 877-885, (2012).
  • [39] Lan, T.-S., “Taguchi optimization of multi objective CNC machining using TOPSIS”, Information Technology Journal, 8(6): 917-922, (2009).
  • [40] Liao, H.C., “Using PCR-TOPSIS to optimize Taguchi’s multi response problem”, International Journal of Advanced Manufacturing Technology, 22: 649-655, (2003).
  • [41] Lu, J.-C., Yang, T., Su, C.-T., “Analysing optimum push/pull junction point location using multiple criteria decision-making for multistage stochastic production system”, International Journal of Production Research, 50(19): 5523-5537, (2012).
  • [42] Yang, T., Chou, P., “Solving a multi-response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method”, Mathematics and Computers in Simulation, 68 (1): 9–21, (2005).
  • [43] İç, Y.T., Yıldırım, S., “MOORA-Based Taguchi Optimization for Improving Product or Process Quality”, International Journal of Production Research, 51(11): 3321–3341, (2013).
  • [44] Şimşek, B., İç, Y.T., and Şimşek, E.H., “A TOPSIS-based Taguchi optimization to determine optimal mixture proportions of the high strength self-compacting concrete”, Chemometrics and Intelligent Laboratory Systems, 125: 18–32, (2013).
  • [45] Şimşek, B., İç, Y.T., “Multi-response simulation optimization approach for the performance optimization of an alarm monitoring center”, Safety Science, 66: 61–74, (2014).
  • [46] Şimşek, B., İç, Y.T., and Şimşek, E.H., “Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems”, International Journal of Computational Intelligence Systems, 9(3): 525-543, (2016).
  • [47] İç, Y.T., Elaldı, F., and Keçeci, B., “Topsis based taguchi optimization of machining characteristics in end milling operation of kevlar-epoxy composites”, Journal of The Chinese Society of Mechanical Engineers, 37(6): 653-662, (2016).
  • [48] İç, Y.T., Saraloğlu Güler, E., and Erbil Çakır Z., “Reducing uncertainty in a type j thermocouple calibration process”, International Journal of Thermophysics, 40(53): 1-22, (2019).
  • [49] Chavan P., Patil A., “Taguchi-based optimization of machining parameter in drilling spheroidal graphite using combined TOPSIS and AHP method”, Advances in Intelligent Systems and Computing, 949: 787-797, (2020).
  • [50] Gopal, P.M., Prakash, K.S., “Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC”, Measurement, 116:178–192, (2018).
  • [51] Nguyen, H.-P., Pham, V.-D., and Ngo, N.-V., “Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid”, The International Journal of Advanced Manufacturing Technology, 98: 1179–1198, (2018).
  • [52] Rajamanickam S., Prasanna J, and Sastry C.C., “Analysis of high aspect ratio small holes in rapid electrical discharge machining of super alloys using Taguchi and TOPSIS”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42: 99-105, (2020).
  • [53] Pradeep, N., Sundaram, K.S., and Kumar, M.P., “Multi-response optimization of electrochemical micromachining parameters for SS304 using polymer graphite electrode with NaNO3 electrolyte based on TOPSIS technique”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41: 323-333, (2019).
  • [54] Jain, A., Jain, P., Chan, F.T., and Singh, S., “A review on manufacturing flexibility”, International Journal of Production Research, 51(19): 5946–5970, (2013).
  • [55] Malhotra, M., Sharma, S., “Measurement equivalence using generalizability theory: an examination of manufacturing flexibility dimensions”, Decision Sciences, 39(4): 643–669, (2008).
  • [56] Wahab, M., Wu, D., and Lee, C., “A generic approach to measuring the machine flexibility of manufacturing systems”, European Journal of Operations Research, 186(1): 137–149, (2008).
  • [57] Ranjit, K.R., “A Primer on the Taguchi Method”. USA: Van Nostrand Reinhold Press, (1990).
  • [58] Phadke, M.S. “Quality Engineering Using Robust Design”. USA: Prentice Hall, (1989).
  • [59] Unal, R., Dean, E.B., “Taguchi Approach to Design Optimization for Quality and Cost: An Overview”. USA: NASA Press, (1990).
  • [60] http://seykoc.com.tr/. Access date: 14.12.2015.
  • [61] http://www.cadcamsektoru.com/makaleler/Talasli-Imalatta-Kullanilan-Kesici-TOOLlar-9949.htm. Access date: 09.15.2015.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Industrial Engineering
Yazarlar

Yusuf Tansel İç 0000-0001-9274-7467

Mustafa Yurdakul 0000-0002-1562-5738

Berna Dengiz 0000-0002-2806-3308

Turgut Şaşmaz Bu kişi benim 0000-0002-9179-5106

Proje Numarası BA/FM-15
Yayımlanma Tarihi 1 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 36 Sayı: 2

Kaynak Göster

APA İç, Y. T., Yurdakul, M., Dengiz, B., Şaşmaz, T. (2023). Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science, 36(2), 735-750. https://doi.org/10.35378/gujs.883367
AMA İç YT, Yurdakul M, Dengiz B, Şaşmaz T. Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science. Haziran 2023;36(2):735-750. doi:10.35378/gujs.883367
Chicago İç, Yusuf Tansel, Mustafa Yurdakul, Berna Dengiz, ve Turgut Şaşmaz. “Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance”. Gazi University Journal of Science 36, sy. 2 (Haziran 2023): 735-50. https://doi.org/10.35378/gujs.883367.
EndNote İç YT, Yurdakul M, Dengiz B, Şaşmaz T (01 Haziran 2023) Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science 36 2 735–750.
IEEE Y. T. İç, M. Yurdakul, B. Dengiz, ve T. Şaşmaz, “Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance”, Gazi University Journal of Science, c. 36, sy. 2, ss. 735–750, 2023, doi: 10.35378/gujs.883367.
ISNAD İç, Yusuf Tansel vd. “Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance”. Gazi University Journal of Science 36/2 (Haziran 2023), 735-750. https://doi.org/10.35378/gujs.883367.
JAMA İç YT, Yurdakul M, Dengiz B, Şaşmaz T. Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science. 2023;36:735–750.
MLA İç, Yusuf Tansel vd. “Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance”. Gazi University Journal of Science, c. 36, sy. 2, 2023, ss. 735-50, doi:10.35378/gujs.883367.
Vancouver İç YT, Yurdakul M, Dengiz B, Şaşmaz T. Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance. Gazi University Journal of Science. 2023;36(2):735-50.