Designing and manufacturing replacement cancellous bone structures by lattice structures and Additive Manufacturing (AM) techniques is an effective method to create lightweight orthopedic implants while ensuring that they are mechanically compatible and their osseointegration ability with the host bone. In this article, we suggest a new design based on three lattice structures from triply periodic minimal surfaces (TPMS) with a different volume porosity to replace cancellous bone based on predicting the mechanical stiffness. To predict the mechanical stiffness, the relationship between the effective modulus of elasticity and different porosity ratios of the lattice structures was determined by using three methods: i) finite element modeling (FEM) simulation, ii) Gibson and Ashby method and iii) a uniaxial compression test after manufacturing the lattice structures by using Fused Filament Fabrication (FFF) Technology. To demonstrate the efficiency of our approach, the comparison of both numerical and experimental results showed that the effect of structure difference and porosity ratio of lattice structures on the mechanical stiffness values effectively match the cancellous bone in terms of elastic modulus and porosity ratio.

The turbine blades are subjected to high operating temperatures and high centrifugal tensile stress due to rotational speeds. The maximum temperature at the inlet of the turbine is currently limited by the resistance of the materials used for the blades. The present paper is focused on the thermo-mechanical behavior of the blade in composite materials with reinforced mast under two different types of loading. The material studied in this work is a composite material, the selected matrix is a technical ceramic which is alumina (aluminum oxide Al2O3) and the reinforcement is carried out by short fibers of high modulus carbon to optimize a percentage of 40% carbon and 60% of ceramics. The simulation was performed numerically by Ansys (Workbench 16.0) software. The comparative analysis was conducted to determine displacements, strains and Von Mises stress of composite material and then compared to other materials such as Titanium Alloy, Stainless Steel Alloy, and Aluminum 2024 Alloy. The results were compared in order to select the material with the best performance in terms of rigidity under thermomechanical stresses. While comparing these materials, it is found that composite material is better suited for high temperature applications. On evaluating the graphs drawn for, strains and displacements, the blade in composite materials reinforced with mast is considered as optimum.

Designing and manufacturing lattice structures with Topology Optimization (TO) and Additive Manufacturing (AM) techniques is a novel method to create light-weight components with promising potential and high design flexibility. This paper proposes a new design of lightweight-graded lattice structures to replace the internal solid volume of the turbine blade to increase its endurance of high thermal stresses effects. The microstructure design of unit cells in a 3D framework is conducted by using the lattice structure topology optimization (LSTO) technique. The role of the LSTO is to find an optimal density distribution of lattice structures in the design space under specific stress constraints and fill the inner solid part of the blade with graded lattice structures. The derived implicit surfaces modelling is used from a triply periodic minimal surfaces (TPMS) to optimize the mechanical performances of lattice structures. Numerical results show the validity of the proposed method. The effectiveness and robustness of the constructed models are analysed by using finite element analysis. The simulation results show that the graded lattice structures in the improved designs have better efficiency in terms of lightweight (33.41–40.32%), stress (25.52–48.55%) and deformation (7.35–19.58%) compared to the initial design.

In this work we present a new biomimetic disc prosthesis imitating the fibroreinforced osmotic, and viscoelastic properties of the biological intervertebral disc (BID). For this, we proposed to study the second-generation biomimetic prosthesis "the M6-C prosthesis" which contains two metal plates, a core and a fiber fabric. First, a 3D model was established, the finite element analysis (FEA) under the ANSYS©2015 was conducted. Secondly, a biomimetic material, the silicone rubber, was compared with the polyethylene to find the material that mimics the behavior of a biological disk. Finally, the analysis of the results found the polymer has the same mechanical properties as the nucleus pulposus, in particular the viscoelastic behaviour compared with that of polyethylene

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.

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.