@proceedings {6998,
	title = {Electromechanical Analysis (MEMS) of a Capacitive Pressure Sensor of a Neuromate Robot Probe},
	journal = {The First International Conference on Innovation in Biomechanics and Biomaterials (ICIBAB 2019), April 10-11,},
	year = {2019},
	address = {Oran, Algeria},
	author = {Ameddah, Hacene and Bettine, Farida and Mazouz,  Hammoudi}
}
@article {6974,
	title = {A neural network approach for predicting kinematic errors solutions for trochoidal machining in the matsuura MX-330 Five-axis Machine},
	journal = {FME Transactions},
	volume = {46},
	number = {4},
	year = {2018},
	pages = {453-462},
	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.},
	url = {https://scindeks.ceon.rs/Article.aspx?artid=1451-20921804453B\&lang=en},
	author = {Bettine, Farida and Ameddah, Hacene and Manaa, Rabah}
}
@article {6975,
	title = {A NEURAL NETWORK APPROACH FOR PREDICTING KINEMATIC ERRORS SOLUTIONS FOR TROCHOIDAL MACHINING},
	journal = {International Journal of Modern Manufacturing Technologies},
	volume = {x},
	number = {1},
	year = {2018},
	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.},
	url = {https://ijmmt.ro/vol10no12018/02_Beettine_Farid.pdf},
	author = {Bettine, Farida and Ameddah, Hacene and Manaa, Rabah}
}
