Additive manufacturing using cloud
- 1. Additive Manufacturing process parameters optimization and monitoring process with Industry4.0
- 2. DEPARTMENT OF MECHANICAL AND PRODUCTION ENGINEERING (MPE) AHSANULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY (AUST) DHAKA-1208, BANGLADESH Name of the Supervisor : Dr. Muhommad Azizur Rahman Name of the Examiner : Md. Rezaul Karim Nayeem 2 Asif Adnan Rashid 16.01.07.016 Shazid Hasan 16.01.07.063 MD. Sharjil Ahamed 16.01.07.094
- 3. OUTLINE • Introduction • Literature Review • Gap Analysis • Objective • Methodology • Artificial Neural Network (ANN) • Results and Discussions • Conclusion • Future Work 3 2
- 4. INTRODUCTION 2 Types of manufacturing process • Casting • Labeling and painting • Moulding • Forming • Machining • Joining • Additive manufacturing
- 5. 2 Background of the research • Subtractive manufacturing involves removing sections of a material by machining or cutting it away. • Additive manufacturing is a process that adds successive layers of material to create an object, often referred to as 3D printing.
- 6. 2 Visual difference of this two process
- 7. 2 Machining on Acrylic material Similar product can be made by both of these machining process.
- 8. 2 3D printing process • Fused deposition modeling (FDM) • Composite Filament Fabrication (CFF) Material Extrusion • Stereolithography (SLA) • Digital Light Processing (DLP) Light polymerized • Selective laser melting (SLM) • Selective laser sintering (SLS) Powder Bed • Laminated object manufacturing (LOM) Laminated
- 10. Agenda Style 01 You can simply impress your audience and add a unique zing and appeal to your Presentations. Easy to change colors, photos and Text. Customer Order 02 You can simply impress your audience and add a unique zing and appeal to your Presentations. Easy to change colors, photos and Text. Cyber Physical System (CPS) 03 You can simply impress your audience and add a unique zing and appeal to your Presentations. Easy to change colors, photos and Text. Optimization 04 You can simply impress your audience and add a unique zing and appeal to your Presentations. Easy to change colors, photos and Text. CPS to Machine
- 11. 2 To establish a relationship between FDM input parameters-layer thickness, infill percentage & build orientation and the output parameter tensile strength. To design an Artificial Neural Network (ANN) machine learning algorithm for PLA based product optimization To find the optimum value of the FDM input parameters to get the highest tensile strength. To perform the optimization process with the help of Cyber Physical System. Objectives
- 12. LITERATURE REVIEW Author Year publish Findings Zhang, Jinwena Peng, Anhuab 2012 Optimized four parameters- wire-width compensation, extrusion velocity, filling velocity and layer thickness using Taguchi method combined with fuzzy comprehensive evaluation Uzair Khaleeq uz Zaman, Emilien Boesch, Ali Siadat,Mickael Rivette & Aamer Ahmed Baqai 2018 parametric optimization of FDM using Taguchi design of experiments (DOE). Layer thickness, shells, infill pattern, and infill percentage are used as input parameters to get the optimum tensile strength. 2
- 13. Author Year publish Findings Sandeep Desw al, Rajan Narang, Deepak Chhab ra 2019 • Artificial Neural Network (ANN) is a mathematical or computational model inspired by biological neurons. • ANN models are used to make nonlinear interrelationship between FDM parameters input and output . Mateo Jimenez 2017 Components of Cyber Physical System & how it my apply in manufacturing industry 2
- 14. GAP Comparatively less research with PLA material ANN is less used Different parameters were used FDM parameter optimization isn’t used with Cyber Physical System 2
- 15. METHODOLOGY • Intelligent machine learning (Artificial Neural Network) will be used to find the tensile strength. • FDM process will be used • All operations will be conducted on a CREALITY CR-10 3D printer Layer thickness, Infill percentage & Build Orientation will be used as input parameters to get the optimum tensile strength • Automatically access the quality and parameters of printer with integration of Intellient Machine Learning.
- 16. Connection with Cyber-Physical System in Industry4.0 CPS will be created based on the paper by lee et al. called the 5C level 1.Connection 2.Conversion 3.Cyber 4.Cognition 5.Configuration
- 17. 2 CONVERSION 1.The 3D printer is connected to a Raspberry Pi 3B microprocessor through an Arduino RAMBO 1.2G board. Raspberry. 2. It has Wi-Fi and Bluetooth embedded 1.Organize, Convert and Store. 2. functional software like lambda 3. Cloud service for storage used is Redshift. 4. Stored and graphical representation 1.Analyzing, Storing and Monitoring the information. 2.Kinesis service should analyze the information 3.Insights about failure modes 4.Quicksight will compare previous vs existing data. 1.understanding capacity can only be reached using some AI code. 2. Bi-directional interaction. 3.Users can deploy code using simple amazon sagemaker. 4.Feedback the probabilty of failure. 1.translating decision from previous cyber system into physical actions. 2.Without supervision or any human control 3.Any disturbance will stop the printing manually or automatically
- 19. EXPECTED OUTCOMES Different combinations of input parameters produce different values in tensile and flexural strength. The highest value of the tensile test shows the highest strength of the gear. Using the Amazon Web Services (AWS) the data will be optimized & reach the machine to act on that data The experiment shows the effects of the optimized parameters on the strength of the 3D printing process for each factor
- 20. REFERENCES 1.Agrawal, S., & Dhande, S. G. (2007). Analysis of mechanical error in a fused deposition process using a stochastic approach. International Journal of Production Research. https://doi.org/10.1080/00207540600791624 2.Ahn, S. H., Montero, M., Odell, D., Roundy, S., & Wright, P. K. (2002). Anisotropic material properties of fused deposition modeling ABS. In Rapid Prototyping Journal (Vol. 8, Issue 4). https://doi.org/10.1108/13552540210441166 3.Ang, K. C., Leong, K. F., Chua, C. K., & Chandrasekaran, M. (2006). Investigation of the mechanical properties and porosity relationships in fused deposition modelling-fabricated porous structures. Rapid Prototyping Journal, 12(2), 100–105. https://doi.org/10.1108/13552540610652447 4.Anitha, R., Arunachalam, S., & Radhakrishnan, P. (2001). Critical parameters influencing the quality of prototypes in fused deposition modelling. Journal of Materials Processing Technology, 118(1–3), 385–388. https://doi.org/10.1016/S0924-0136(01)00980-3 5.Authors, F. (2016). Article information : Baturynska, I., Semeniuta, O., & Martinsen, K. (2017). ScienceDirect Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework Selection and peer-review under responsibility of the International Scientif. Procedia CIRP, 00, 227–232.
- 21. 6.Canellidis, V., Giannatsis, J., & Dedoussis, V. (2016). Evolutionary computing and genetic algorithms: Paradigm applications in 3D printing process optimization. Studies in Computational Intelligence, 627, 271–298. https://doi.org/10.1007/978-3-662-49179-9_13 7.Delli, U., Chang, S., Radhwan, H., Shayfull, Z., Abdellah, A. E. H., Irfan, A. R., Kamarudin, K., Saad, M. S., Nor, A. M., Baharudin, M. E., Zakaria, M. Z., Aiman, A. ., Qureshi, A. J., Mahmood, S., Wong, W. L. E., Talamona, D., Part, R. C., Iuganson, R., Vihtonen, M., … Reddy, N. V. (2019). Optimizing product manufacturability in 3D printing. International Journal of Rapid Manufacturing, 26(6), 1–17. https://doi.org/10.1007/978-981-13-2375-1 8.Galantucci, L. M., Lavecchia, F., & Percoco, G. (2008). Study of compression properties of topologically optimized FDM made structured parts. CIRPAnnals - Manufacturing Technology. https://doi.org/10.1016/j.cirp.2008.03.009 9.Gibson, I., Rosen, D., & Stucker, B. (2015). Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing, second edition. In Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, Second Edition. https://doi.org/10.1007/978-1-4939-2113-3 10.Horvath, D., Noorani, R., & Mendelson, M. (2007). Improvement of surface roughness on ABS 400 polymer materials using Design of Experiments (DOE). Materials Science Forum, 561–565(PART 3), 2389–2392. https://doi.org/10.4028/www.scientific.net/msf.561-565.2389
- 22. 2 PLANNING TIMELINE 0 5 10 15 20 25 30 35 40 Gear Design and Printing(7 sets) Recheck the design and data collection Connecting with machine with cloud ANN-GA model preparation Model optimization Product Print based on result(3sets) Final Result Days Days