Researchers from the University of Valladolid in Spain discuss ways to improve efficiency and organization in 3D printing, releasing the details of their study in the recently published ‘Production planning in 3D printing factories.’
As 3D printing facilities become more common around the globe, it is no surprise to see challenges emerging; after all, 3D printing may be on the verge of revolutionizing industrial practices from automotive to aerospace and far beyond, but it is still relatively new. There is no manual (yet) and solutions must be considered and implemented as obstacles present themselves.
Some of the greatest benefits of 3D printing include the ability to produce parts faster and more affordably (along with the ability to print multiple parts at once), but without proper scheduling in a factory setting, the advantages begin to vaporize quickly. While traditional factory settings may run smoothly with prioritization techniques backed by qualitative parameters, for more progressive manufacturing they tend to be ineffective regarding saving time and managing resources.
The key is to schedule 3D printers in a streamlined, automated manner, allowing for better planning—and ultimately, optimum profit. In this study, the researchers created a software program using Python to load data of parts, receiving a platform layout in return.
In previous studies, researchers have identified problems in production, scheduling, and nesting. Mathematical formulations were created, along with heuristics, investigating how to meet deadlines faster—and especially when working under constraints. The most common solutions included two-step procedures for optimization, such as organizing nesting parts into builds, and then the builds into machines.
The LONJA3D model optimizes grouping of customer orders that are to be fabricated with the same materials and specifications. In 3D printing multiple parts for different customers at the same time, production costs are cut substantially. This also allows the parties performing the printing to offer better prices—passing the savings on to the customers. To solve issues with packing, appropriate distribution of parts must be considered; however, the use of this method can offer impressive results.
“In AM the production cost (and in consequence the expected income) of a good directly depends on its mass (volume and filling percentage),” explained the researchers. “To maximize the productivity of each 3D printer we should solve the puzzle that ensures for each manufacturing batch that the largest proportion of the manufacturing area will be occupied with the parts which have the highest filling percentage.”
While grouping of parts must be efficient, so must placing of parts. This means that in manufacturing large volumes, each part must be assigned to a group and then to a printer, depending on requirements and parameters. Some parts cannot be printed at the same time and must be scheduled for later, but the real issue to be addressed is how parts are situated on the bed, solving “nesting of parts.”
“The input data will be the width, length, height, and filling percentage of each part from the set. Also, the width, length, and height of the 3D printer will be introduced as data,” explained the researchers.
“The inputs of the parts are the name (Pi ), filling percentage (ri ), length (li ), width (wi ), height (hi ). The build platform area is given by name (Aj ), length (Lj ), width (Wj ) and height (Hj ), all of them in mm.”
An optimized layout is produced, scheduling printer production.
The program worked best for suppliers when they sorted parts from largest to smallest, maintaining a maximum limit of 40 pieces to handle. The researchers noted “lower quality” when dealing with numbers as high as 100 parts. Details such as filling percentage and height also had to be considered too, due to the added amount of material used. Future studies may include examination of scheduling for multiple machines.Production planning in 3D printing factories’]
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