Reducing Waste in 3D Printing Using a Neural Network Based on an Own Elbow Exoskeleton

Materials

卷:14

Piotr Kotlarz¹  Izabela Rojek¹  Jakub Kopowski¹  Dariusz Mikołajewski¹  Maciej Piechowiak¹  Ewa Dostatni² 

[1] Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland;

[2] Institute of Materials Technology, Faculty of Mechanical Engineering, Poznan University of Technology, pl. M. Skłodowskiej-Curie 5, 60-965 Poznan, Poland;

关键词: neural network;    3D printing;    reduction of waste;    elbow exoskeleton;   

DOI  :  10.3390/ma14175074

来源: DOAJ

【 摘 要 】

Traditional rehabilitation systems are evolving into advanced systems that enhance and improve rehabilitation techniques and physical exercise. The reliable assessment and robotic support of the upper limb joints provided by the presented elbow exoskeleton are important clinical goals in early rehabilitation after stroke and other neurological disorders. This allows for not only the support of activities of daily living, but also prevention of the progression neuromuscular pathology through proactive physiotherapy toward functional recovery. The prices of plastics are rising very quickly, as is their consumption, so it makes sense to optimize three dimensional (3D) printing procedures through, for example, improved artificial intelligence-based (AI-based) design or injection simulation, which reduces the use of filament, saves material, reduces waste, and reduces environmental impact. The time and cost savings will not reduce the high quality of the products and can provide a competitive advantage, especially in the case of thinly designed mass products. AI-based optimization allows for one free print after every 6.67 prints (i.e., from materials that were previously wasted).

【 授权许可】

Unknown