Gram Optimization using Taguchi Method of Parameter Design and Neural Network Process Model in Packaging Industry  

Oleh:

Zagloel, Teuku Yuri ; Al-Aina, Fatimah

Jenis: Article from Proceeding
Dalam koleksi: APCOMS 2009: The 2nd Asia-Pacific Conference on Manufacturing System: Reconfigurable Manufacturing System for Facing Turbulent Manufacturing Environment, November 4th-5th, 2009, Yogyakarta, Indonesia, page V.9-16.
Topik: Process Design; Optimization Technique; Process Quality Improvement; Taguchi Method; Neural Network For Prediction; Lamination Extrusion Process;
Fulltext: APCOMS G5-2.pdf (437.58KB)

Isi artikel

Costumer satisfaction is best achieved by improvement of quality product. One way to improve the quality of a product is to optimize the process output. This research paper describes the methods of manufacturing process optimization, using the basis of Taguchi parameter design and Neural Network model. Taguchi experimental design used to predict the optimum process parameters in manufacturing process, while Neural Network model forecasts the responses from the process parameters. This combination approach identifies the important factor settings to develop a setting design for the optimum operating condition that can stand from noise variables (Robust Design), without conduct an actual experiment on process. A case study illustrates this approach, collects real production data from the laminating machine in a packaging plant using gram (sheeting weight of packaging material) as quality response from the process.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali