Stability lobes prediction in high speed milling , ISSN 2067–3604

Cherifa, Azoui, and Benmohammed Brahim. 2018. “Stability lobes prediction in high speed milling , ISSN 2067–3604”. International Journal of Modern Manufacturing Technologies Vol. X (N°1) : pp 37-42. Publisher's Version Abstract

Different techniques are used to obtain approximate solutions for delayed functional differential equations (RFDEs). All these models used the so-called stability lobe diagrams, to choose the maximum axial depth of cut for a given spindle speed associated with a free chatter in machining. In this research paper, the ZOA (Zeroth Order Approximation) and SD (Semi Discretization) methods are explained, developed and used to obtain the stability lobe diagrams for a milling cutting system with two degree of freedom, in high speed machining case.

A NEURAL NETWORK APPROACH FOR PREDICTING KINEMATIC ERRORS SOLUTIONS FOR TROCHOIDAL MACHINING

Bettine, Farida, Hacene Ameddah, and Rabah Manaa. 2018. “A NEURAL NETWORK APPROACH FOR PREDICTING KINEMATIC ERRORS SOLUTIONS FOR TROCHOIDAL MACHINING”. International Journal of Modern Manufacturing Technologies x (1). Publisher's Version Abstract

The prediction of machining accuracy of a fiveaxis machine tool is a vital process in precision manufacturing for machining a hard and free form surfaces. This work presents a novel approach for predicting kinematic errors solutions in five-axis machine for trochoidal milling strategy. 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. The data sets for the neural-network model are obtained using 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. For this, numerical study of trochoidal strategy and experimental results are presented with aims to full milling and to ensure a control of radial engagement. The experimental result shows the efficiency of the method by obtainning the toolpath and the machining possebility of this new type of strategy emerging.

A neural network approach for predicting kinematic errors solutions for trochoidal machining in the matsuura MX-330 Five-axis Machine

Bettine, Farida, Hacene Ameddah, and Rabah Manaa. 2018. “A neural network approach for predicting kinematic errors solutions for trochoidal machining in the matsuura MX-330 Five-axis Machine”. FME Transactions 46 (4) : 453-462. Publisher's Version Abstract

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.

Impeller Tool Paths Programming for Rough Machining in an Intelligent NURBS Step- Nc Format

Ameddah, Hacene. 2016. “Impeller Tool Paths Programming for Rough Machining in an Intelligent NURBS Step- Nc Format”. International Journal of Current Engineering and Technology 6 (1). Publisher's Version Abstract

The research work reported in this paper focuses on introduces a fully STEP-compliant CNC by putting forward an interpolation algorithm for Non Uniform Rational Basic spline (NURBS) curve system for rough milling tool paths with an aim to solve the problems faced by the current CNC systems. The most important components used in aerospace, ships, and automobiles are designed with free form surfaces. An impeller is one of the most important components that are difficult to machine because of its twisted blades. The research is based on the premise that a STEP-NC program can document ǲgenericǳ manufacturing information for an impeller. This way, a STEP-NC program can be made machine-independent and has an advantage over the conventional G-code based NC program that is always generated for a specific CNC machine. Rough machining is recognized as the most crucial procedure influencing machining efficiency and is critical for the finishing process. A key feature of the system is the use of STEPNC data model (ISO 14649-10: 2003; ISO 10303-238, 238: 2003), which enables more design information (e.g. geometry, workpiece information and tolerances) to be incorporated both prior to and during machining processes. The relevant algorithm for the curve was simulated in CAM software. The results have shown that the algorithm for rough milling is feasible and effective.

Impeller Tool Paths Programming for Rough Machining in an Intelligent NURBS Step- Nc Format

Laboratoire de Recherche en Productique

Prédiction des efforts de coupe pour le fraisage périphérique en utilisant la théorie prédictive d’Oxley et la loi de comportement de Johnson-Cook

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Stability lobes prediction in high speed milling , ISSN 2067–3604

A NEURAL NETWORK APPROACH FOR PREDICTING KINEMATIC ERRORS SOLUTIONS FOR TROCHOIDAL MACHINING

A neural network approach for predicting kinematic errors solutions for trochoidal machining in the matsuura MX-330 Five-axis Machine

Impeller Tool Paths Programming for Rough Machining in an Intelligent NURBS Step- Nc Format

Impeller Tool Paths Programming for Rough Machining in an Intelligent NURBS Step- Nc Format