A Nonlinear Error Compensator for FDM ۳D Printed Part Dimensions Using a Hybrid Algorithm Based on GMDH Neural Network

  • سال انتشار: 1400
  • محل انتشار: مجله مکانیک کاربردی محاسباتی، دوره: 52، شماره: 3
  • کد COI اختصاصی: JR_JCAM-52-3_006
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 383
دانلود فایل این مقاله

نویسندگان

Hamid Haghshenas Gorgani

Engineering Graphics Center, Sharif University of Technology, Tehran, Iran

Hossein Korani

School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

Reihaneh Jahedan

Mechanical Science & Engineering, Grainger College of Engineering, University of Illinois at Urbana Champaign, USA

Sharif Shabani

Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran

چکیده

Following the advances in Computer-Aided Design (CAD) and Additive Manufacturing (AM), with regards to the numerous benefits of the Fused Deposition Modeling (FDM) as a popular AM process, resolving its weaknesses has become increasingly important. A serious problem of the FDM is the dimensional error or size difference between the CAD model and the actual ۳D printed part.In this study, the approach is compensating the error regardless of its source. At First, all parameters affecting the dimensional accuracy of FDM are comprehensively identified. Then, multi-input–single-output (MISO) data is prepared by designing experiments using the Taguchi method and obtaining the results from ۳D printed samples. Next, a GMDH neural network is applied, which uses a simple nonlinear regression formula in each neuron but can create very complex neuron combinations. So, it is possible to analyze small or even noisy data. Regulatory parameters of the Neural Net have been optimized to increase efficiency. The case study shows a decrease in the RSME for the Nominal CAD Model from ۰.۳۷۷ to ۰.۰۳۳, displaying the compensator's efficiency.

کلیدواژه ها

Additive Manufacturing, Fused Deposition Method, Error Compensation Model, GMDH Neural Network

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.