38 research outputs found
Application of Simulated Annealing in Improving the Performance of Stereolithography
Effective utilisation of Stereolithography (SL) mainly relies on orienting and packing parts optimally on the fabrication platform of the machine, so to achieve maximum space utilisation and minimum
build time, without of course compromising surface quality. The present work focuses on an effective way to pack parts optimally on the fabrication platform of SL machine. Due to technical constrains set by SL technology, the original 3-D packing problem is simplified by one dimension by projecting each one of the parts on the build platform (x-y plane) and packing their projections instead of the actual parts themselves. In order to solve the resulting 2-D packing problem a heuristic method has been adopted. The heuristic method consists of a Simulated Annealing algorithm employing a polynomial-time cooling schedule and a new improved placement rule
Production planning in 3D printing factories
[EN] Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF,
FEDER) with grant VA049P17.De Antón, J.; Senovilla, J.; González, J.; Acebes, F.; Pajares, J. (2020). Production planning in 3D printing factories. International Journal of Production Management and Engineering. 8(2):75-86. https://doi.org/10.4995/ijpme.2020.12944OJS758682Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875-886. https://doi.org/10.1016/j.cad.2012.12.002Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in additive manufacturing. Computers and Industrial Engineering, 126(May), 292-301. https://doi.org/10.1016/j.cie.2018.09.048Cui, Y. (2007). An exact algorithm for generating homogenous T-shape cutting patterns. Computers & Operations Research, 34(4), 1107-1120. https://doi.org/https://doi.org/10.1016/j.cor.2005.05.025Dvorak, F., Micali, M., & Mathieu, M. (2018). Planning and scheduling in additive manufacturing. 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Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In 19th International Working Seminar on Production Economics (pp. 195-201). Innsbruck, Austria.Li, Q., Kucukkoc, I., & Zhang, D. Z. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 1339-1351. https://doi.org/10.1016/j.cor.2017.01.013López-Paredes, A., Pajares, J., Martín, N., del Olmo, R., & Castillo, S. (2018). Application of combinatorial auctions to create a 3Dprinting market. Advancing in Engineering Network, Castro and Gimenez Eds. Lecture Notes in Management and Industrial Engineering (In Press), 12-13.Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. https://doi.org/10.3390/app9183865Piili, H., Happonen, A., Väistö, T., Venkataramanan, V., Partanen, J., & Salminen, A. (2015). Cost Estimation of Laser Additive Manufacturing of Stainless Steel. Physics Procedia, 78(August), 388-396. https://doi.org/10.1016/j.phpro.2015.11.053Shaffer, S., Yang, K., Vargas, J., Di Prima, M. A., & Voit, W. (2014). On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969-5979. https://doi.org/10.1016/j.polymer.2014.07.054Singhal, S. K., Pandey, A. P., Pandey, P. M., & Nagpal, A. K. (2005). Optimum Part Deposition Orientation in Stereolithography. Computer-Aided Design and Applications, 2(1-4), 319-328. https://doi.org/10.1080/16864360.2005.10738380Sung‐Hoon, A. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248-257. https://doi.org/10.1108/13552540210441166Thomas, D. S., & Gilbert, S. W. (2015). Costs and cost effectiveness of additive manufacturing: A literature review and discussion. Additive Manufacturing: Costs, Cost Effectiveness and Industry Economics, 1-96. https://doi.org/10.6028/NIST.SP.1176Toro, E., Garces, A., & Ruiz, H. (2008). Two dimensional packing problem using a hybrid constructive algorithm of variable neighborhood search and simulated annealing. Revista Facultad de Ingeniería Universidad de Antioquia, 119-131.Toro, E., & Granada-Echeverri, M. (2007). Problema de empaquetamiento rectangular bidimensional tipo guillotina resuelto por algoritmos genéticos. Scientia Et Technica.Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing-A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58(March), 145-157. https://doi.org/10.1016/j.rcim.2019.03.003Wodziak, J. R., Fadel, G. M., & Kirschman, C. (1994). A Genetic Algorithm for Optimizing Multiple Part Placement to Reduce Build Time. Proceedings of the Fifth International Conference on Rapid Prototyping., (May), 201,210.Zhang, Y., Gupta, R. K., & Bernard, A. (2016). Two-dimensional placement optimization for multi-parts production in additive manufacturing. Robotics and Computer-Integrated Manufacturing, 38, 102-117. https://doi.org/10.1016/j.rcim.2015.11.003Zhao, Z., Zhang, L., & Cui, J. (2018). A 3D printing task packing algorithm based on rectangle packing in cloud manufacturing. Lecture Notes in Electrical Engineering, 460, 21-31. https://doi.org/10.1007/978-981-10-6499-9_3Zhou, L., Zhang, L., Laili, Y., Zhao, C., & Xiao, Y. (2018). Multi-task scheduling of distributed 3D printing services in cloud manufacturing. International Journal of Advanced Manufacturing Technology, 96(9-12), 3003-3017. https://doi.org/10.1007/s00170-017-1543-zZhou, L., Zhang, L., & Xu, Y. (2016). Research on the relationships of customized service attributes in cloud manufacturing. ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016, 2, 1-8. https://doi.org/10.1115/MSEC2016-853
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AM Feature and Knowledge Based Process Planning for Additive Manufacturing in Multiple Parts Production Context
Additive Manufacturing (AM) has played an important role in manufacturing, especially in
customized production. It is an ideal 'Concurrent Manufacturing' which enables fabricating a
group of same or even different multiple parts simultaneously within one build volume due to
its unique layer by layer processing way. However, there is very few available methods or
tools for users, e.g. the AM manufacturing service bureaus, to optimize the process and
production plan in multiple parts production context. To deal with this problem, this paper
introduces an AM feature and knowledge based systematic process planning strategy. The
main contents and key issues of process planning for AM in multiple parts production context
are analyzed. Then, a developing CAPP system based on a systematic process planning
framework for AM in this multiple parts production context is presented. Finally, some test
examples are applied to demonstrate the functions and effectiveness of some key modules of
the developing system.Mechanical Engineerin
Optimization model to extend existing production planning and control systems for the use of additive manufacturing technologies in the industrial production
The use of additive manufacturing technologies for industrial production is constantly growing. This technology differs from the known production proecdures. The areas for scheduling, detailed and sequence planning are particularly important for additive production due to the long print times and flexible use of the production area. Therefore, production-relevant variables are considered and used for the production planning and control (PPC) of additive manufacturing machines. For this purpose, an optimization model is presented which shows a time-oriented build space utilization. In the implementation, a nesting algorithm is used to check the combinability of different models for each individual print job
The flexibility of industrial additive manufacturing systems
Purpose Flexibility is a fundamental performance objective for manufacturing operations, allowing them to respond to changing requirements in uncertain and competitive global markets. Additive manufacturing machines are often described as “flexible,” but there is no detailed understanding of such flexibility in an operations management context. The purpose of this paper is to examine flexibility from a manufacturing systems perspective, demonstrating the different competencies that can be achieved and the factors that can inhibit these in commercial practice. Design/methodology/approach This study extends existing flexibility theory in the context of an industrial additive manufacturing system through an investigation of 12 case studies, covering a range of sectors, product volumes, and technologies. Drawing upon multiple sources, this research takes a manufacturing systems perspective that recognizes the multitude of different resources that, together with individual industrial additive manufacturing machines, contribute to the satisfaction of demand. Findings The results show that the manufacturing system can achieve seven distinct internal flexibility competencies. This ability was shown to enable six out of seven external flexibility capabilities identified in the literature. Through a categorical assessment the extent to which each competency can be achieved is identified, supported by a detailed explanation of the enablers and inhibitors of flexibility for industrial additive manufacturing systems. Originality/value Additive manufacturing is widely expected to make an important contribution to future manufacturing, yet relevant management research is scant and the flexibility term is often ambiguously used. This research contributes the first detailed examination of flexibility for industrial additive manufacturing systems
Data Representation Methods For Environmentally Conscious Product Design
The challenge of holistically integrating environmental sustainability considerations with design decision-making requires novel representations for design and sustainability-related data that allow designers to understand correlations among them. Challenges such as (1) lack of suitable data & information models, (2) methods that simultaneously consider environmental sustainability as well as design constraints, and (3) uncertainty models for characterizing subjectivity in environmental sustainability-based decision making, pose serious impediments towards this goal
The flexibility of industrial additive manufacturing systems
The overall aim of this study is to explore the nature of Industrial Additive Manufacturing
Systems as implemented by commercial practitioners, with a specific focus on flexibility within
the system and wider supply chain. This study is conducted from an Operations Management
perspective to identify management implications arising from the application of contemporary
Industrial Additive Manufacturing in the fulfilment of demand.
The generation of the theoretical constructs and their evaluation is achieved through an abductive
approach. The concept of an Industrial Additive Manufacturing System is developed, through
which activities, enabling mechanisms, and control architectures are demonstrated. This is
complimented by the proposal of a typology of flexibilities both for the manufacturing system
and its supply chain. Twelve case studies are examined through practitioner interviews,
observation, and mapping of the production processes at three Industrial Additive Manufacturing
companies. These explorations are complimented by interviews with customers downstream of
the Additive Manufacturer, and with interviews and a survey of principal upstream machine and
material suppliers.
This study identifies and classifies types of flexibility relevant to Industrial Additive
Manufacturing Systems. It is shown that to achieve requisite flexibilities, it is necessary to
manage the whole manufacturing system, not just individual machines. By extension, the internal
manufacturing systems’ ability to achieve flexibility is shown to be both facilitated and
constrained by the environment in which it operates. In particular, inadequacies in the supply of
materials are shown to result in suboptimal practices within the manufacturing system.
The principal contribution of this thesis is therefore the development of Industrial Additive
Manufacturing from a manufacturing systems perspective, and an evaluation of its implications
for flexibilit