Abstract
Manufacturing can be defined as the application of mechanical, physical, and chemical processes to convert the geometry, properties, and/or shape of raw material into finished parts or products. This includes all intermediate processes required for the production and integration of the final product. Manufacturing involves interrelated activities which include product design, material selection, production process planning, production, quality assurance, management and marketing of products.
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Armağan M, Arici AA (2017) Cutting performance of glass-vinyl ester composite by abrasivewater jet. Mater Manuf Processes 32(15):1715–1722
Asad ABMA, Masaki T, Rahman M, Lim HS, Wong YS (2007) Tool-based micro-machining. J Mater Process Technol 192:204–211
Bao W, Chen P, Tansel I, Reen NS, Yang S, Rincon D (2003) Selection of optimal cutting conditions by using the genetically optimized neural network system (GONNS). In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Artificial neural networks and neural information processing—ICANN/ICONIP 2003. ICANN 2003, ICONIP 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg
Bhattacharyya B, Gangopadhyay S, Sarkar BR (2007) Modelling and analysis of EDMed job surface integrity. J Mater Process Technol 189(1–3):169–177
Camposeco-Negrete C (2019) Prediction and optimization of machining time and surface roughness of AISI O1 tool steel in wire-cut EDM using robust design and desirability approach. Int J Adv Manuf Technol 1–12
Dang XP (2018) Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm. Mater Manuf Processes 33(4):397–404
Das MK, Kumar K, Barman TK, Sahoo P (2014) Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel. Procedia Mater Sci 6:741–751
Dewangan S, Gangopadhyay S, Biswas CK (2015) Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Eng Sci Technol Int J 18(3):361–368
Di Orio G, Cândido G, Barata J, Scholze S, Kotte O, Stokic D (2013) Self-learning production systems (SLPS)-optimization of manufacturing process parameters for the shoe industry. In: 2013 11th IEEE international conference on industrial informatics (INDIN). IEEE, pp 386–391
Dow TA, Scattergood RO (2003) Mesoscale and microscale manufacturing processes: challenges for materials, fabrication and metrology. In: Proceedings of the ASPE winter topical meeting, vol. 28. pp 14–19)
Durairaj M, Gowri S (2013) Parametric optimization for improved tool life and surface finish in micro turning using genetic algorithm. Procedia Eng 64:878–887
Filiz S, Ozdoganlar OB (2010) A model for bending, torsional, and axial vibrations of microand macro-drills including actual drill geometry-part 1: model development and numerical solution. J Manuf Sci Eng 132:041017–1–8
Ganapathy S, Balasubramanian P, Senthilvelan T, Kumar R (2019) Multi-response optimization of machining parameters in EDM using square-shaped nonferrous electrode. In: Adv Manuf Process. Springer, Singapore, pp 287–295
Gopalakannan S, Senthilvelan T (2014) Optimization of machining parameters for EDM operations based on central composite design and desirability approach. J Mech Sci Technol 28(3):1045–1053
Gopalsamy BM, Mondal B, Ghosh S (2009) Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel
Gostimirovic M, Pucovsky V, Sekulic M, Rodic D, Pejic V (2019) Evolutionary optimization of jet lag in the abrasive water jet machining. Int J Adv Manuf Technol 101(9–12):3131–3141
Gupta K, Jain NK, Laubscher R (2017) Chapter 4-advances in gear manufacturing. In: Advanced gear manufacturing and finishing, 67–125
Huo D, Cheng K, Wardle F (2010) Design of a five-axis ultra-precision micro-milling machine—ULTRAMIll. Part 1: holistic design approach, design considerations and specifications. Int J Adv Manuf Technol 47(9–12): 867–877
Jagadeesh T (2015) Non traditional machining. Mechanical Engineering Department, National Institute of Technology, Calicut
Jain VK (2008) Advanced (non-traditional) machining processes. In: Machining. Springer, London, pp 299–327
Kolli M, Kumar A (2015) Effect of dielectric fluid with surfactant and graphite powder on electrical discharge machining of titanium alloy using Taguchi method. Eng Sci Technol Int J 18(4):524–535
Kumar SL, Jerald J, Kumanan S, Aniket N (2014) Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput Appl 25(7–8):1941–1950
Malekian M, Park SS, Jun MBG (2009) Modeling of dynamic micro-milling cutting forces. Int J Mach Tools Manuf 49:586–598
Momber AW, Kovacevic R (2012) Principles of abrasive water jet machining. Springer Science& Business Media
Muthuramalingam T, Mohan B (2015) A review on influence of electrical process parameters in EDM process. Arch Civil Mech Eng 15(1):87–94
Palani S, Natarajan U, Chellamalai M (2013) On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS). Mach Vis Appl 24(1):19–32
Rahman M, Kumar AS, Prakash JRS (2001) Micro milling of pure copper. J Mater Process Technol 116(1):39–43
Rahman AA, Mamat A,Wagiman A (2009) Effect of machining parameters on hole quality of micro drilling for brass. Modern Appl Sci 3(5):221–230
Rao RV (2010) Advanced modelling and optimization of manufacturing processes: international research and development. Springer Science & Business Media
Schwartzentruber J, Narayanan C, Papini M, Liu HT (2016) Optimized abrasive waterjet nozzle design using genetic algorithms. In: The 23rd international conference on water jetting, At Seattle, USA
Shanmugam DK, Nguyen T, Wang J (2008) A study of delamination on graphite/epoxy composites in abrasive waterjet machining. Compos A Appl Sci Manuf 39(6):923–929
Shastri AS, Nargundkar A, Kulkarni AJ (2020) Multi-cohort intelligence algorithm for solving advanced manufacturing process problems. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04858-y
Shukla R, Singh D (2017) Experimentation investigation of abrasive water jet machining parameters using Taguchi and Evolutionary optimization techniques. SwarmEvolut Comput 32:167–183
Sofuoğlu MA, Çakır FH, Kuşhan MC, Orak S (2019) Optimization of different non-traditional turning processes using soft computing methods. Soft Comput 23(13):5213–5231
Teimouri R, Baseri H (2014) Optimization of magnetic field assisted EDM using the continuous ACO algorithm. Appl Soft Comput 14:381–389
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214
Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37(6):4650–4659
Zain AM, Haron H, Sharif S (2011) Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput 11(8):5350–5359
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Shastri, A., Nargundkar, A., Kulkarni, A.J. (2021). Introduction to Advanced Manufacturing Processes and Optimization Methodologies. In: Socio-Inspired Optimization Methods for Advanced Manufacturing Processes. Springer Series in Advanced Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-15-7797-0_1
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DOI: https://doi.org/10.1007/978-981-15-7797-0_1
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