Version 1
: Received: 19 December 2016 / Approved: 20 December 2016 / Online: 20 December 2016 (11:02:09 CET)
Version 2
: Received: 24 February 2017 / Approved: 26 February 2017 / Online: 26 February 2017 (10:18:59 CET)
How to cite:
Urbikain, G.; López De Lacalle, L.N.; Alonso, M.A.; Arsuaga, M.; Alvarez, A. A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. Preprints2016, 2016120106. https://doi.org/10.20944/preprints201612.0106.v1
Urbikain, G.; López De Lacalle, L.N.; Alonso, M.A.; Arsuaga, M.; Alvarez, A. A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. Preprints 2016, 2016120106. https://doi.org/10.20944/preprints201612.0106.v1
Urbikain, G.; López De Lacalle, L.N.; Alonso, M.A.; Arsuaga, M.; Alvarez, A. A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. Preprints2016, 2016120106. https://doi.org/10.20944/preprints201612.0106.v1
APA Style
Urbikain, G., López De Lacalle, L.N., Alonso, M.A., Arsuaga, M., & Alvarez, A. (2016). A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages. Preprints. https://doi.org/10.20944/preprints201612.0106.v1
Chicago/Turabian Style
Urbikain, G., Mikel Arsuaga and Alvaro Alvarez. 2016 "A Reliable Turning Process by the Early Use of a Deep Simulation Model at Several Manufacturing Stages" Preprints. https://doi.org/10.20944/preprints201612.0106.v1
Abstract
The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.
Keywords
simulation software; manufacturing systems; process integration; machining optimization; Industry 4.0; knowledge-based manufacturing
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.