Antoine Tahan

Genetic algorithms and finite element coupling for mechanical optimization

Optimization of mechanical components is an important aspect of the engineering process; a well designed system will lead to money saving during the production phase and better machine life. On the other hand, optimization actions will increase the engineering investment. Consequently, and since computer time is inexpensive, an efficient design strategy will tend to transfer the effort from the staff to the computers. This paper presents an efficient design tool made to carry out this task: a new optimization model based on genetic algorithms is developed to work with commercial finite element software. The objective is to automate optimization of static criteria (stresses, weight, strength, etc.) with finite element models. In the proposed model, the process acts on two geometric aspects of the shape to be optimized: it controls the position of the vertices defining the edges of the volume and, in order to minimize stresses concentrations, it can add and define fillet between surfaces. The model is validated from some benchmark tests. An industrial application is presented: the genetic algorithms-finite element model is employed to design the fillets at the crown-blade junctions of a hydroelectric turbine. The results show that the model converges to a very efficient solution without any engineer intervention.

A Novel Approach for the Inspection of Flexible Parts Without the Use of Special Fixtures

In a free state, flexible parts may have different shapes compared to their computer-aided design (CAD) model. Such parts may likewise undergo large deformations depending on their space orientation. These conditions severely restrict the feasibility of inspecting flexible parts without restricting the deformations of the part and therefore require dedicated and expensive tools such as a conformation jig or a fixture to maintain the integrity of the part. To address these challenges, this paper proposes a new inspection method, the iterative displacement inspection (IDI) algorithm, that evaluates profile variations without the need for specialized fixtures. This study examines 32 models of simulated manufactured parts to show that the IDI algorithm can iteratively deform the meshed CAD model until it resembles the scanned manufactured part, which enables their comparison. The method deforms the mesh in such a manner so as to ensure its smoothness. This way, neither surface defects nor the measurement noise of the scanned parts are concealed during the matching process. As a result, the case studies illustrate that the methods error essentially only represents the scanned parts measurement noise. The inspection results, therefore, solely reflect the effect of variations from the manufacturing process itself and not the deformation of the part.

Review and Study of Genotypic Diversity Measures for Real-Coded Representations

The exploration/exploitation balance is a major concern in the control of evolutionary algorithms (EAs) performance. Exploration is associated with the distribution of individuals on a landscape, and can be estimated by a genotypic diversity measure (GDM). In contrast, exploitation is related to individual responses, which can be described with a phenotypic diversity measure. Many diversity measures have been proposed in the literature without a comprehensive study of their differences. This paper looks at surveys of GDMs published over the years for real-coded representations, and compares them based on a new benchmark, one that allows a better description of their behavior. The results demonstrate that none of the available GDMs is able to reflect the true diversity of all search processes. Nonetheless, the normalized pairwise diversity measurement proves to be the best genotypic diversity measurement for standard EAs, as it shows nondominated behavior with respect to the desired GDM requirements.

Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO

Amplitude demodulation is a key for diagnosing bearing faults. The quality of the demodulation determines the efficiency of the spectrum analysis in detecting the defect. A signal analysis technique based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD), and Teager Kaiser energy operator (TKEO) is presented. The proposed method consists in enhancing the signal by using MED, decomposing the signal in intrinsic mode functions (IMFs) and selects only the IMF which presents the highest correlation coefficient with the original signal. In this study the first IMF1 was automatically selected, since it represents the contribution of high frequencies which are first excited at the early stages of degradation. After that, TKEO is used to track the modulation energy. The spectrum is applied to the instantaneous amplitude. Therefore, the character of the bearing faults can be recognized according to the envelope spectrum. The simulation and experimental results show that an envelope spectrum analysis based on MED-EMD and TKEO provides a reliable signal analysis tool. The experimental application has been developed on acoustic emission and vibration signals recorded for bearing fault detection.

A virtual fixture using a FE-based transformation model embedded into a constrained optimization for the dimensional inspection of nonrigid parts

Virtually mounting nonrigid parts onto their fixture is proposed by researchers to remove the need for the use of complex physical inspection fixtures during the measurement process. Current approaches necessitate the pre-processing of the free-state nonrigid parts point cloud into a suitable finite element?(FE) mesh and are limited by the use of the boundary conditions setting methods available in FE software. In addition to these limits, these approaches do not take into account the forces used to restrain the part during the inspection, as commonly mandated for aerospace panels. To address these shortcomings, this paper presents a virtual fixture method that predicts the fixed shape of the part without the aforementioned drawbacks of current approaches. This is achieved by embedding information retrieved from a FE analysis of the nominal CAD model into a boundary displacement constrained optimization. To evaluate the proposed method, two case studies on physical parts are performed using the proposed virtual fixture method to evaluate the profile and assembly force specifications of each part. The virtual fixture method allows for the inspection of nonrigid parts.It does not necessitate the pre-processing of the point cloud into a FE mesh.It takes into account the parts specification limiting the restraining forces.It infers the parts structural behavior from the FE model of the nominal CAD.Two case studies on physical parts are performed.

Empirical Mode Decomposition of Acoustic Emission for Early Detection of Bearing Defects

Empirical Mode Decomposition (EMD) is one of the techniques that proved its efficiency for an early detection of defects in many mechanical applications like bearings and gears. The EMD methodology decomposes the original times series data into intrinsic mode functions (IMF), by using the Hilbert-Huang transform. In this study, EMD is applied to acoustic emission signals. The acoustic emission signal is heterodynined around a central high frequency in order to obtain an audible signal. Scalar statistical parameters such as Kurtosis and THIKAT are then used in this study. These statistical descriptors are calculated for each IMF. The technique is validated by experiments on a test bench with a very small crack (40 μm) on the outer race of a ball bearing. It is found that the application of time descriptors to different IMF decomposition levels gives good results for early detection of defects in comparison with the original time signal.

Review of phenotypic diversity formulations for diagnostic tool

Practitioners often rely on search results to learn about the performance of a particular optimizer as applied to a real-world problem. However, even the best fitness measure is often not precise enough to reveal the behavior of the optimizers added features or the nature of the interactions among its parameters. This makes customization of an efficient search method a rather difficult task. The aim of this paper is to propose a diagnostic tool to help determine the impact of parameter setting by monitoring the exploration/exploitation balance (EEB) of the search process, as this constitutes a key characteristic of any population-based optimizer. It is common practice to evaluate the EEB through a diversity measure. For any diagnostic tool developed to perform this function, it will be critical to be able to certify its reliability. To achieve this, the performance of the selected measure needs to be assessed, and the EEB framework must be able to accommodate any landscape structure. We show that to devise a diagnostic tool, the EEB must be viewed from an orthogonal perspective, which means that two diversity measures need to be involved: one for the exploration axis, and one for the exploitation axis. Exploration is best described by a genotypic diversity measure (GDM), while exploitation is better represented by a phenotypic convergence measure (PCM). Our paper includes a complete review of PCM formulations, and compares nearly all the published PCMs over a validation framework involving six test cases that offer controlled fitness distribution. This simple framework makes it possible to portray the underlying behavior of phenotypic formulations based on three established requirements: monotonicity in fitness varieties, twinning, and monotonicity in distance. We prove that these requirements are sufficient to identify phenotypic formulation weaknesses, and, from this conclusion, we propose a new PCM, which, once validated, is shown to comply with all the above-mentioned requirements. We then compare these phenotypic formulations over three specially designed fitness landscapes, and, finally, the new phenotypic formulation is combined with a genotypic formulation to form the foundation of the EEB diagnostic tool. The value of such a tool is substantiated through a comparison of the behaviors of various genetic operators and parameters.

CAD/Tolerancing integration: Mechanical assembly with form defects

Abstract Geometric deviations affect the assemblability and functional compliance of products, since small part variations accumulate through large-scale assemblies and lead to malfunctions. The Digital Muck Up (DMU) upgrade requires a tolerance consideration in CAD models. The improvement of tolerancing leads to industrial success. Therefore, improving the CAD model to be closer to the realistic model is a necessity to verify and validate the mechanical system assemblability. In previous work, an approach to consider the dimensional, positional and orientation tolerances in CAD models was developed. In this paper, the above approach is improved to take into account form defects in CAD models. To model the component with form defects, the toleranced face is modeled by gird vertices. According to form tolerance values, a White Gaussian Noise (WGN) of gird vertices is computed. The realistic face is obtained by an interpolation based on the tessellation using Thin Plate Surface (TPS) modeling. The realistic assembly configurations were performed by updating the mating constraints. In fact, in realistic modeling, a new method to redefine constraints, while respecting the Objective Function of the Assembly (OFA), is established. In the case of a planar joint, a sub-algorithm based on Oriented Bounding Box (OBB) and the matrix transformation is developed. Relative part displacements are simulated with or without guaranteeing contact. Tolerance impacts on the realistic assembly motion are quantified. The realistic cylindrical joint is performed using an optimization method: the minimum cylinder inside a realistic hole and the maximum cylinder outside a realistic pin. Finally, in the case of a revolute joint, a sub-algorithm to redefine the mating constraints between two realistic parts is performed. This paper proposes a new approach to incorporate tolerances on CAD models in the case of planar and cylindrical faces by determining configurations with positional, orientation and form defects. This approach provides an assembly result closer to the real assembly of the mechanical system. Integrating tolerances in CAD allows the simulation and visualization of the mechanical assemblies’ behavior in their real configurations.

Performances of observability indices for industrial robot calibration

This work presents a comparison of the five observability indices used for robot calibration. The comparison is realized in order to determine the most appropriate observability index, which allows for the best parameter identification of a calibrated robot, and therefore leading to the best improvement of the robot accuracy. In this study, the accuracy analysis is based on the robot end-effector errors, which are expressed in term of Euclidean errors. The parameter identification process is based on minimizing the residual of the position errors. The actual values of these positions are usually measured by an external measurement device and have measurement noise. The position residuals are calculated in all the calibration configurations, which are selected by using observability indices. An optimal set of configurations is the one reducing the impact of the measurement noise on the parameter identification efficacy. Our study is carried out for the calibration of four robots: two degrees of freedom (DOF) and 6-DOF serial robots, and 2-DOF and 3-DOF planar parallel robots. The comparison of the observability indices was achieved through a Monte Carlo simulation, using 100 different cases for each of the four robots considered. The position measurement noise was assumed to be within a range of ± 200 μm. Investigations led to conclude that there is a specific index that may be considered the best observability index for robot calibration. Finally, an experimental study has been applied to a LR Mate 200ic FANUC robot and confirms the simulated results.

Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy Complexity

Vibration analysis is the most used technique for defect monitoring failures of industrial gearboxes. Detection and diagnosis of gear defects are thus crucial to avoid catastrophic failures. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv Complexity (LZC) for detection of gears defects. These methods are based on statistical measurements exploring the regularity of vibratory signals. Applied to gear signals, the parameter selection of ApEn, SampEn, and LZC calculation is first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators and a comparative study with traditional time domain indicators is presented. The results demonstrate that ApEn, SampEn, and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.