A neural network approach for predicting kinematic errors solutions for trochoidal machining in the matsuura MX-330 Five-axis Machine
Open accessAbstract
The prediction of machining accuracy of a Five-axis Machine tool is a vital process in precision manufacturing. This work presents a novel approach for predicting kinematic errors solutions in five axis Machine. This approach is based on Artificial Neural Network (ANN) for trochoidal milling machining strategy. We proposed a multi-layer perceptron (MLP) model to find the inverse kinematics solution for a Five-axis Machine Matsuura MX-330. The data sets for the neural-network model is obtained using Matsuura MX-330 kinematics software. The solution of each neural network is estimated using inverse kinematics equation of the Machine tool to select the best one. As a result, the Neural Network implementation improves the performance of the learning process. In this work trochoidal trajectory generation formulation has been developed and simulated using the software Matlab Inc. The main advantage of the trochoidal path is to present a continuous path radius leading the machining process to take place under favorable conditions (no impact, less marking of the part, ...). Obtaining the toolpath is to allow programming of the toolpath according to ISO 6983 (which defines the principles of the G code). For this, numerical study of trochoidal strategy and experimental result are presented with aims to full milling and to ensure a control of radial engagement.
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Neural networks are suitable for a large class of problems, including defects classification.
“Its properties are flexible and suitable for the solution of a large class of problems [22], which includes defects classification [5], [23].”
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Self-citation
- Authors
- Bettine, F., Ameddah, H., & Rabah, M.
- Affiliations
- University of Batna 1 (DZ)
- ORCID
- Ameddah, Hacene
- Venue
- FME Transactions
- Published
- 2018
- Volume
- 46
- Issue
- 4
- Pages
- 453-462
- ISSN
- 1451-2092 (ISSN-L)
- 2406-128X
- OpenAlex
- W2892157546
- Cited by
- 10
- References
- 23
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1 · 2020: 5 · 2019: 2
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- FME Transaction (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Engineering Technology and Methodologies
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Measurement and Metrology Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Advanced machining processes and optimization
- Keywords
- five-axis machine tools, trochoidal milling, inverse kinematics, multilayer perceptron, machining accuracy
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