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. Inteligencia Artificial, 21(62), 40-52. https://doi.org/10.4114/intartif.vol21iss62pp40-52Gogate, A. S., & Pande, S. S. (2008). Intelligent layout planning for rapid prototyping. International Journal of Production Research, 46(20), 5607-5631. https://doi.org/10.1080/00207540701277002Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations and Production Management, 28(10), 991-1012. https://doi.org/10.1108/01443570810903122Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research, 88(1), 165-181. https://doi.org/10.1016/0377-2217(94)00166-9Kucukkoc, I. (2019). MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers and Operations Research, 105, 58-67. https://doi.org/10.1016/j.cor.2019.01.006Kucukkoc, I., Li, Q., & Zhang, D. Z. (2016). 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

Genetic algorithms for the scheduling in additive manufacturing

[EN] Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.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 VA049P17Castillo-Rivera, S.; De Antón, J.; Del Olmo, R.; Pajares, J.; López-Paredes, A. (2020). Genetic algorithms for the scheduling in additive manufacturing. International Journal of Production Management and Engineering. 8(2):59-63. https://doi.org/10.4995/ijpme.2020.12173OJS596382Ahsan, A., Habib, A., Khoda, B. (2015). Resource based process planning for additive manufacturing. Computer-Aided Design, 69, 112-125. https://doi.org/10.1016/j.cad.2015.03.006Araújo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., Hague, R. (2015). Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks. 26th Annual International Solid Freeform Fabrication Symposium - an Additive Manufacturing Conference, 401-410.Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55: 155-162. https://doi.org/10.1016/j.bushor.2011.11.003Canellidis, V., Dedoussis, V., Mantzouratos, N., Sofianopoulou, S. (2006). Preprocessing methodology for optimizing stereolithography apparatus build performance. Computers in Industry, 57, 424-436. https://doi.org/10.1016/j.compind.2006.02.004Chergui, A., Hadj-Hamoub, K., Vignata, F. (2018). Production scheduling and nesting in additive manufacturing. Computers & Industrial Engineering, 126, 292-301. https://doi.org/10.1016/j.cie.2018.09.048Demirel, E., Özelkan, E.C., Lim, C. (2018). Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 202, 45-58. https://doi.org/10.1016/j.ijpe.2018.05.001Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations, 9, 423-438. https://doi.org/10.5267/j.ijiec.2018.1.001Hopper, E., Turton, B. (1997). Application of genetic algorithms to packing problems - A Review. Proceedings of the 2nd Online World Conference on Soft Computing in Engineering Design and Manufacturing, Springer Verlag, London, 279-288. https://doi.org/10.1007/978-1-4471-0427-8_30Ikonen, I., Biles, W.E., Kumar, A., Wissel, J.C., Ragade, R.K. (1997). A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. ICGA, 591-598.Kim, K.H., Egbelu, P.J. (1999). Scheduling in a production environment with multiple process plans per job. International Journal of Production Research, 37, 2725-2753. https://doi.org/10.1080/002075499190491Lawrynowicz, A. (2011). Genetic algorithms for solving scheduling problems in manufacturing systems. Foundations of Management, 3(2), 7-26. https://doi.org/10.2478/v10238-012-0039-2Li, Q., Kucukkoc, I., Zhang, D. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 157-172. https://doi.org/10.1016/j.cor.2017.01.013Milošević, M., Lukić, D., Đurđev, M., Vukman, J., Antić, A. (2016). Genetic Algorithms in Integrated Process Planning and Scheduling-A State of The Art Review. Proceedings in Manufacturing Systems, 11(2), 83-88.Pour, M.A., Zanardini, M., Bacchetti, A., Zanoni, S. (2016). Additive manufacturing impacts on productions and logistics systems. IFAC, 49(12), 1679-1684. https://doi.org/10.1016/j.ifacol.2016.07.822Wilhelm, W.E., Shin, H.M. (1985). Effectiveness of Alternate Operations in a Flexible Manufacturing System. International Journal of Production Research, 23(1), 65-79. https://doi.org/10.1080/00207548508904691Xirouchakis, P., Kiritsis, D., Persson, J.G. (1998). A Petri net Technique for Process Planning Cost Estimation. Annals of the CIRP, 47(1), 427-430. https://doi.org/10.1016/S0007-8506(07)62867-4Zhang, Y., Bernard, A., Gupta, R.K., Harik, R. (2014). Evaluating the design for additive manufacturing: a process planning perspective. Procedia CIRP, 21, 144-150. https://doi.org/10.1016/j.procir.2014.03.17

Cavity-Enhanced Two-Photon Interference using Remote Quantum Dot Sources

Quantum dots in cavities have been shown to be very bright sources of indistinguishable single photons. Yet the quantum interference between two bright quantum dot sources, a critical step for photon based quantum computation, has never been investigated. Here we report on such a measurement, taking advantage of a deterministic fabrication of the devices. We show that cavity quantum electrodynamics can efficiently improve the quantum interference between remote quantum dot sources: poorly indistinguishable photons can still interfere with good contrast with high quality photons emitted by a source in the strong Purcell regime. Our measurements and calculations show that cavity quantum electrodynamics is a powerful tool for interconnecting several devices.Comment: 5 pages, 4 figures (Supp. Mat. attached

Continuous multi-criteria methods for crop and soil conservation planning on La Colacha (Río Cuarto, Province of Cordoba, Argentina)

Agro-areas of Arroyos Menores (La Colacha) west and south of Rand south of R?o Cuarto (Prov. of Cordoba, Argentina) basins are very fertile but have high soil loses. Extreme rain events, inundations and other severe erosions forming gullies demand urgently actions in this area to avoid soil degradation and erosion supporting good levels of agro production. The authors first improved hydrologic data on La Colacha, evaluated the systems of soil uses and actions that could be recommended considering the relevant aspects of the study area and applied decision support systems (DSS) with mathematic tools for planning of defences and uses of soils in these areas. These were conducted here using multi-criteria models, in multi-criteria decision making (MCDM); first of discrete MCDM to chose among global types of use of soils, and then of continuous MCDM to evaluate and optimize combined actions, including repartition of soil use and the necessary levels of works for soil conservation and for hydraulic management to conserve against erosion these basins. Relatively global solutions for La Colacha area have been defined and were optimised by Linear Programming in Goal Programming forms that are presented as Weighted or Lexicographic Goal Programming and as Compromise Programming. The decision methods used are described, indicating algorithms used, and examples for some representative scenarios on La Colacha area are given

First study on volatile composition of three Vitis vinifera cultivars from Betanzos (NW Spain): Blanco lexítimo, Agudelo and Serradelo

7 páginas, 2 tablas, 1 figura.- Trabajo presentado al Congreso celebrado en Verona (Italia) entre los días 15 y 20 de junio de 2008.[EN] Wine aroma is formed by volatile compounds of different chemical nature and origins. Compounds arising from grape metabolism vary as a function not only of the variety but also of climatic conditions and others. Betanzos is the most northern viticole geographic area from Galicia (NW Spain). Wines produced from Vitis vinifera cv. Blanco lexítimo, Agudelo and Serradelo from Betanzos (NW Spain), harvest in 2006 and 2007, were submitted to gas chromatography (GC/FID). A total of 35 varietal and fermentative aroma compounds were identified and quantified which include terpenes and C13-norisoprenoids, alcohols, acids, esters, phenol volatiles and C6 compounds. The results for quantification of the aroma compounds in the three varieties from Betanzos shows that the mean concentration between years was 25039.42ug/L for Blanco lexítimo 21913.2ug/L for Agudelo and 19804.6μg/L for Serradelo variety. Alcohols, acids and esters were the most important volatile compounds in the varieties from Betanzos.[ES] El aroma del vino está formado por compuestos volátiles de diferente naturaleza y origen. Estos compuestos varian no solo con la variedad de uva sino también con las condiciones climáticas y otros factores. Betanzos es la zona vitícola situada más al norte de Galicia (Noroeste de España). Los vinos producidos con tres variedades de Betanzos, Blanco lexítimo, Agudelo y Serradelo cultivadas en las cosechas 2006 y 2007, fueron analizados mediante GC/FID. En los vinos fueron identificados y cuantificados un total de 35 compuestos varietales y fermentativos, en cuales se incluyen C13-norisoprenoides, alcoholes, acidos, esters, fenoles volatiles y otros. Los resultados de la cuantificación de los compuestos del aroma en los vinos de las tres variedades de Betanzos muestra que la concentración mediaentre los años estudiados fue de 25039.42ug/L para Blanco lexítimo 21913.2ug/L para Agudelo y 19804.6μg/L para Serradelo. Alcoholes, acidos and esteres fueron los compuestos más importantes en los tres vinos elaborados con las variedades de Betanzos, siendo el más aromático el elaborado con la variedad Blanco lexítimo, caracterizado por aromas frutales.The authors thank to the Xunta de Galicia (Spain) and Adega Lorenzo Bescansa S.U.L. for the financial support to this work.Peer reviewe

Influence of elevation and slope exposure on must volatiles of Mencía cultivar from Ribeira Sacra (NW Spain)

Resumen del póster presentado al Congreso que bajo el lema "Science and horticulture for people" se celebró en Lisboa del 22 al 27 de Agosto de 2010.Peer reviewe

A novel score for predicting alzheimer’s disease risk from late life psychopathological and health risk factors

With the increasing size of the aging population, dementia risk reduction has become a main public health concern. Dementia risk models or indices may help to identify individuals in the community at high risk to develop dementia. We have aimed to develop a novel dementia risk index focused on the late-life (65 years or more) population, that addresses risk factors for Alz-heimer’s disease (AD) easily identifiable at primary care settings. These risk factors include some shown to be associated with the risk of AD but not featured in existing indices, such as hearing loss and anxiety. Our index is also the first to account for the competing risk of death. The Zaragoza Dementia and Depression Project (ZARADEMP) Alzheimer Dementia Risk Score predicts an indi-vidual´s risk of developing AD within 5 years. The probability of late onset AD significantly in-creases in those with risk scores between 21 and 28 and, furthermore, is almost 4-fold higher for those with risk scores of 29 or higher. Our index may provide a practical instrument to identify subjects at high risk of AD and to design preventive strategies targeting the contributing risk factors