Manufacturing of instructional aids for students at low cost by means of 3D printing

References

• Laperrière, L.; Reinhart, G. (Eds.) CIRP Encyclopedia of Production Engineering; Springer: Berlin, 2014.
   Google Scholar
• Kamrani, A.; Nasr, E.A. (Eds.) Rapid Prototyping: Theory and Practice; Springer: New York, 2006.
   Google Scholar
• Venuvinod, P.K.; Ma, W. Rapid Prototyping: Laser-based and Other Technologies; Springer: New York, 2004.
   Google Scholar
• Segal, D. Exploring Materials through Patent Information; Royal Society of Chemistry: Cambridge, 2014.
   Google Scholar
• Cooper, K.G. Rapid Prototyping Technology: Selection and Application; Marcel Dekker Inc.: New York, 2001.
   Google Scholar
• RepRap Options: http://reprap.org/wiki/RepRap_Options.
   Google Scholar
• Nasr, E.A.; Kamrani, A.K. Computer-Based Design and Manufacturing: An Information-Based Approach; Springer: New York, 2007.
   Google Scholar
• Fodran, E.; Koch, M.; Menon, U. Mechanical and dimensional characteristics of fused deposition modeling build styles. In Proceedings of 7th Solid Freeform Fabrication Symposium, Vol. 7. The University of Texas: Austin, 1996; 419–442.
   Google Scholar
• Bertoldi, M.; Yardimci, M.A.; Pistor, C.M.; Guceri, S.I.; Sala, G. Mechanical characterization of parts processed via fused deposition. In Proceedings of 9th Solid Freeform Fabrication Symposium, Vol. 9. The University of Texas: Austin, 1998; 557–565.
   Google Scholar
• Anitha, R.; Arunachalam, S.; Radhakrishnan, P. Critical parameters influencing the quality of prototypes in fused deposition modeling. Journal of Materials Processing Technology 2001, 118, 385–388.
   Web of Science ®Google Scholar
• Evans, B. Practical 3D Printers; Apress: New York, 2012.
   Google Scholar
• Henke, B.; Sawodny, O.; Schmidt, S.; Neumann, R. Modeling of hybrid stepper motors for closed-loop operation. In 6th IFAC Symposium on Mechatronic Systems, The International Federation of Automatic Control: Hangzhou, 2013; 177–183.
   Google Scholar
• Griewank, A. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation; SIAM: Philadelphia, PA, 2000.
   Google Scholar
• Karr, C. Practical Applications of Computational Intelligence for Adaptive Control; CRC Press: Boca Raton, FL, 1999.
   Google Scholar
• Mitra, S.; Mitra, A. A genetic algorithms based technique for computing the nonlinear least squares estimates of the parameters of sum of exponentials model. Expert Systems with Applications 2012, 39, 6370–6379.
   Web of Science ®Google Scholar
• El-Samahy, A.A.; Shamseldin, M.A. Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control. Ain Shams Engineering Journal 2016, in press.
   Google Scholar
• Ziegler, J.G.; Nichols, N.B. Optimum settings for automatic controllers. Transactions of the ASME 1942, 64, 759–768.
   Google Scholar
• Elsodany, N.M.; Rezeka, S.F.; Maharem, N.A. Adaptive PID control of a stepper motor driving a flexible rotor. Alexandria Engineering Journal 2011, 50, 127–136.
   Google Scholar
• Zaky, M.S. A self-tuning PI controller for the speed control of electrical motor drives. Electric Power Systems Research 2015, 119, 293–303.
   Web of Science ®Google Scholar
• Chang, W.D.; Hwang, R.C.; Hsieh, J.G. A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach. Journal of Process Control 2002, 12, 233–242.
   Web of Science ®Google Scholar
• Banihani, S.; Al-Widyan, K.; Al-Jarrah, A.; Ababneh, M. A genetic algorithm based lookup table approach for optimal stepping sequence of open-loop stepper motor systems. Journal of Control Theory and Applications 2013, 11, 35–41.
   Google Scholar
• Add a closed loop control to your 3D printer and never worry about missed steps again: http://www.3ders.org/articles/20150122-add-a-closed-loop-control-to-your-3d-printer-and-never-worry-about-missed-steps-again.html.
   Google Scholar
• Closed-loop stepper controller progress: http://forums.reprap.org/read.php?1,263680.
   Google Scholar
• Alexander, P.; Allen, S.; Dutta, D. Part orientation and build cost determination in layered manufacturing. Computer-Aided Design 1998, 30, 343–358.
   Web of Science ®Google Scholar
• Anitha, R.; Arunachalamb, S.; Radhakrishnan, P. Critical parameters influencing the quality of prototypes in fused deposition modelling. Journal of Materials Processing Technology 2001, 118, 385–388.
   Web of Science ®Google Scholar
• Lee, B.H.; Abdullah, J.; Khan, Z.A. Optimization of rapid prototyping parameters for production of flexible ABS object. Journal of Materials Processing Technology 2005, 169, 54–61.
   Web of Science ®Google Scholar
• Jin, Y.; Zhang, J.; Wang, Y.; Zhu, Z. Filament geometrical model and nozzle trajectory analysis in the fused deposition modeling process. Journal of Zhejiang University Science A 2009, 10, 370–376.
   Web of Science ®Google Scholar
• Chang, D.; Huang, B. Studies on profile error and extruding aperture for the RP parts using the fused deposition modeling process. The International Journal of Advanced Manufacturing Technology 2011, 53, 1027–1037.
   Web of Science ®Google Scholar
• Peng, A.; Xiao, X.; Yue R. Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. The International Journal of Advanced Manufacturing Technology 2014, 73, 87–100.
   Web of Science ®Google Scholar
• Qiu, D.; Langrana, N.A. Void eliminating toolpath for extrusion-based multi-material layered manufacturing. Rapid Prototyping Journal 2002, 8, 38–45.
   Web of Science ®Google Scholar
• Zhang, Y.; Chou, Y.K. Three-dimensional finite element analysis simulations of the fused deposition modelling process. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2006, 220, 1663–1671.
   Web of Science ®Google Scholar
• Jin, Y.; He, Y.; Xue, G.; Fu, J. Parallel-based path generation method for fused deposition modeling. The International Journal of Advanced Manufacturing Technology 2015, 77, 927–937.
   Web of Science ®Google Scholar
• Costa, S.; Duarte, F.; Covas, J.A. Using MATLAB to compute heat transfer in free form extrusion. In MATLAB – A Ubiquitous Tool for the Practical Engineer; Ionescu, C.M. Ed.; INTECH open: Rijeka, Croatia, 2011; 453–474. (www.intechopen.com)
   Google Scholar
• Rayegani, F.; Onwubolu, G.C. Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). The International Journal of Advanced Manufacturing Technology 2014, 73, 509–519.
   Web of Science ®Google Scholar
• Liu, X.; Zhang, M.; Li, S.; Si, L.; Peng, J.; Hu, Y. Mechanical property parametric appraisal of fused deposition modeling parts based on the gray Taguchi method. The International Journal of Advanced Manufacturing Technology 2016, 1–11. doi:10.1007/s00170-016-9263-3.
   Web of Science ®Google Scholar
• Boschetto, A.; Bottini, L. Triangular mesh offset aiming to enhance Fused Deposition Modeling accuracy. The International Journal of Advanced Manufacturing Technology 2015, 80, 99–111.
   Web of Science ®Google Scholar
• Panda, B.N.; Shankhwar, K.; Garg, A.; Jian, Z. Performance evaluation of warping characteristic of fused deposition modelling process. The International Journal of Advanced Manufacturing Technology 2016, 1–13, doi:10.1007/s00170-016-8914-8.
   Web of Science ®Google Scholar
• Jerez-Mesa, R.; Travieso-Rodriguez, J.A.; Corbella, X.; Busqué, R.; Gomez-Gras, G. Finite element analysis of the thermal behavior of a RepRap 3D printer liquefier. Mechatronics 2016, 36, 119–126.
   Web of Science ®Google Scholar
• Leary, M.; Merli, L.; Torti, F.; Mazur, M.; Brandt, M. Optimal topology for additive manufacture: A method for enabling additive manufacture of support-free optimal structures. Materials and Design 2014, 63, 678–690.
   Web of Science ®Google Scholar
• Singh, S.; Singh, R. Some investigations on surface roughness of aluminium metal composite primed by fused deposition modeling-assisted investment casting using reinforced filament. Journal of the Brazilian Society of Mechanical Sciences and Engineering 2016, 1–9. doi:10.1007/s40430-016-0524-8.
   Web of Science ®Google Scholar
• Lestan, Z.; Klancnik, S.; Balic, J.; Brezocnik, M. Modeling and design of experiments of laser cladding process by genetic programming and nondominated sorting. Materials and Manufacturing Processes 2015, 30, 458–463.
   Web of Science ®Google Scholar
• Thompson, M.K.; Moroni, G.; Vaneker, T.; Fadel, G.; Campbell, R.I.; Gibson, I.; Bernard, A.; Schulz, J.; Graf, P.; Ahuja, B.; Martina, F. Design for additive manufacturing: Trends, opportunities, considerations, and constraints. CIRP Annals - Manufacturing Technology 2016, article in press.
   PubMed Web of Science ®Google Scholar
• Mohamed, O.A.; Masood, S.H.; Bhowmik, J.L. Optimization of fused deposition modeling process parameters: A review of current research and future prospects. Advances in Manufacturing 2015, 3, 42–53.
   Web of Science ®Google Scholar
• Tan, K.C.; Khor, E.F.; Lee, T.H. Multiobjective Evolutionary Algorithms and Applications; Springer-Verlag: London, 2005.
   Google Scholar
• Yu, X.; Gen, M. Introduction to Evolutionary Algorithms; Springer-Verlag: London, 2010.
   Google Scholar
• Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002, 6, 182–197.
   Web of Science ®Google Scholar
• Srinivas, N.; Deb, K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 1994, 2, 221–248.
   Web of Science ®Google Scholar
• Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Boston, MA, 1989.
   Google Scholar
• Corne, D.W.; Knowles, J.D.; Oates, M.J. The Pareto envelope-based selection algorithm for multiobjective optimization. In Parallel Problem Solving from Nature PPSN VI: 6th International Conference, Paris, France, September 18–20, 2000 Proceedings; Schoenauer M.; Deb K.; Rudolph G.; Yao X.; Lutton E.; Merelo J.J.; Schwefel H.P. Eds.; Springer: Berlin, 2000; 839–848.
   Google Scholar
• Zitzler, E.; Laumanns, M.; Thiele, L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland, 2001.
   Google Scholar
• Knowles, J.D.; Corne, D.W. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation 2000, 8, 149–172.
   PubMed Web of Science ®Google Scholar
• Goldberg, D.E. Sizing populations for serial and parallel genetic algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms; Morgan Kaufmann Publishers Inc.: San Francisco, CA, 1989; 70–79.
   Google Scholar
• Tan, K.; Yang, Y.; Goh, C. A distributed cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation 2006, 10, 527–549.
   Web of Science ®Google Scholar
• Jurecka, F. Robust Design Optimization Based on Metamodeling Techniques. Dissertation for the attainment of the academic degree of Doktor-Ingenieur, Fakultät Bauingenieur- und Vermessungswesen, Technische Universität: München, 2007.
   Google Scholar
• Ryberg, A.; Bäckryd, R.D., Nilsson, L. Metamodel-Based Multidisciplinary Design Optimization for Automotive Applications. Technical Report LIU-IEI-R-12/003. Division of Solid Mechanics, Linköping University: Linköping, 2012.
   Google Scholar
• Ryberg, A. Metamodel-based design optimization – a multidisciplinary approach for automotive structures. Thesis No. 1565. Linköping Studies in Science and Technology: Linköping, 2013.
   Google Scholar
• Sobieszczanski-Sobieski, J. Overcoming Bellman’s curse of dimensionality in large optimization problems. In Applied Mathematics in Aerospace Science and Engineering; Miele, A.; Salvetti, A. Eds.; Springer: Boston, MA, 1994; 437–443.
   Google Scholar
• Shan, S.; Wang, G.G. Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Structural and Multidisciplinary Optimization 2010, 41, 219–241.
   Web of Science ®Google Scholar
• Kwak, N.S.; Lee, J. An enhancement of selection and crossover operations in real-coded genetic algorithm for large-dimensionality optimization. Journal of Mechanical Science and Technology 2016, 30, 237–247.
   Web of Science ®Google Scholar
• Deb, K.; Karthik, S.; Okabe, T. Self-adaptive simulated binary crossover for real-parameter optimization. In Proceedings of 9th Conference on Genetic and Evolutionary Computation; Association for Computing Machinery: London, 2007; 1187–1194.
   Google Scholar
• Ono, I.; Kita, H.; Kobayashi, S. A real-coded genetic algorithm using the unimodal normal distribution crossover. In Advances in Evolutionary Computing: Theory and Applications; Ghosh, A.; Tsutsui, S. Eds.; Springer-Verlag: New York, NY, 2003; 213–237.
   Google Scholar
• Deep, K.; Thakur, M. A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation 2007, 188, 895–911.
   Web of Science ®Google Scholar
• Adams, B.M.; Dalbey, K.R.; Eldred, M.S.; Swiler, L.P.; Bohnhoff, W.J.; Eddy, J.P.; Vigil, D.M.; Hough, P.D.; Lefantzi, S. DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis. Version 5.2, User’s Manual, SAND2010–2183, Unlimited Release. Sandia National Laboratories: Livermore, CA 94551, Updated November 30, 2011.
   Google Scholar
• Koziel, S.; Ciaurri, D.E.; Leifsson, L. Surrogate-based methods. In Computational Optimization, Methods and Algorithms; Koziel, S.; Yang, X. Ed.; Springer-Verlag: Berlin, 2011; 33–59.
   Google Scholar
• Arisoy, Y.M.; Özel, T. Machine learning based predictive modeling of machining induced microhardness and grain size in Ti–6Al–4V alloy. Materials and Manufacturing Processes 2015, 30, 425–433.
   Web of Science ®Google Scholar
• Tutum, C.C.; Deb, K.; Baran, I. Constrained efficient global optimization for pultrusion process. Materials and Manufacturing Processes 2015, 30, 538–551.
   Web of Science ®Google Scholar
• Chakraborti, N. Critical assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Materials Science and Technology 2014, 30, 1259–1262.
   Web of Science ®Google Scholar
• Chakraborti, N. Promise of multiobjective genetic algorithms in coating performance formulation. Surface Engineering 2014, 30, 79–82.
   Web of Science ®Google Scholar
• Mohanty, K.; Roy, G.G.; Chakraborti, N. Simulation and meta-modeling of electron beam welding using genetic algorithms. La Metallurgia Italiana 2016, 3, 45–48.
   Google Scholar
• Ong, Y.S.; Nair, P.B.; Keane, A.J.; Wong, K.W. Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In Knowledge Incorporation in Evolutionary Computation; Jin, Y. Ed.; Springer-Verlag: Berlin, 2005, 305–331.
   Google Scholar
• van Veldhuizen, D.A.; Lamont, G.B. Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation 2000, 8, 125–147.
   PubMed Web of Science ®Google Scholar
• Mashwani, W.K. Hybrid multiobjective evolutionary algorithms: A survey of the state-of-the-art. IJCSI International Journal of Computer Science Issues 2011, 8, ISSN (online www.IJCSI.org): 1694–0814.
   Google Scholar
• Zhou, A.; Qu, B.; Li, H.; Zhao, S.; Suganthan, P.N.; Zhang, Q. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 2011, 1, 32–49.
   Web of Science ®Google Scholar
• Gen, M.; Lin, L. Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing 2014, 25, 849–866.
   Web of Science ®Google Scholar
• Li, B.; Li, J.; Tang, K.; Yao, X. Many-objective evolutionary algorithms: A survey. ACM Computing Surveys 2015, 48, Article 13, 13:1–13:35.
   PubMed Web of Science ®Google Scholar
• Jackiewicz, J. Optimization of geometrical structures of modern materials by using a hybrid evolution strategy. Computational Materials Science 2015, 15, 94–102.
   Google Scholar
• Coello Coello, C.; Lamont, G.B.; van Veldhuizen, D.A. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed.; Springer-Verlag US: New York, NY, 2007.
   Google Scholar
• Yu, X.; Gen, M. Introduction to Evolutionary Algorithms; Springer-Verlag: London, 2010.
   Google Scholar
• Zegard, T.; Paulino, G.H. GRAND - Ground structure based topology optimization for arbitrary 2D domains using MATLAB. Structural and Multidisciplinary Optimization 2014, 50, 861–882.
   Web of Science ®Google Scholar
• Hirano, Y. Application of Modelica to development of future new-concept vehicles. In 7th IFAC Symposium on Advances in Automotive Control, Kawabe, T. Ed.; The International Federation of Automatic Control: Tokyo, September 4–7, 2013, 428–433.
   Google Scholar
• Roberts, N.; Dempsey, M.; Picarelli, A. Detailed powertrain dynamics modelling in Dymola – Modelica. In 7th IFAC Symposium on Advances in Automotive Control; Kawabe, T. Ed.; The International Federation of Automatic Control: Tokyo, Japan, September 4–7, 2013, 434–439.
   Google Scholar
• Prokhorov, D.V. Toyota Prius HEV neurocontrol and diagnostics. Neural Networks 2008, 21, 458–465.
   PubMed Web of Science ®Google Scholar
• Toyota Prius - Power Split Device (PSD): http://eahart.com/prius/psd/
   Google Scholar
• Puzyrewski, R.; Flaszyński, P. Hyperbolically shaped centrifugal compressor. Journal of Thermal Science 2003, 12, 214–218.
   Web of Science ®Google Scholar
• Nili-Ahmadabadi, M.; Hajilouy-Benisi, A.; Durali, M.; Ghadak, F. Investigation of a centrifugal compressor and study of the area ratio and TIP clearance effects on performance. Journal of Thermal Science 2008, 17, 314–323.
   Web of Science ®Google Scholar
• Chu, F.; Wang F.; Wang, X.; Zhang, S. A model for parameter estimation of multistage centrifugal compressor and compressor performance analysis using genetic algorithm. Science China Technological Sciences 2012, 55, 3163–3175.
   Web of Science ®Google Scholar
• Li, J.; Yin, Y.; Li, S.; Zhang, J. Numerical simulation investigation on centrifugal compressor performance of turbocharger. Journal of Mechanical Science and Technology 2013, 27, 1597–1601.
   Web of Science ®Google Scholar
• Townsend, V.; Urbanic, R.J. A systems approach to hybrid design: Fused deposition modeling and CNC machining. In Global Product Development; Bernard, A. Ed.; Springer-Verlag: Berlin, 2011; 711–720.
   Google Scholar