Abderrazak El Ouafi

Accuracy enhancement of multi-axis CNC machines through on-line neurocompensation

This research is devoted to one of the most fundamental problems in precision engineering: machine tool accuracy. The paper presents a new approach designed to improve the accuracy of multi-axis CNC machines through software compensation of geometric, thermal and dynamic errors. Based on a multi-sensor monitoring system, the proposed compensation scheme is built to ensure error prediction. Four steps are required to develop and implement this system: (i) measurement of individual error components along each axis using a laser interferometer system, (ii) sensor integration via an artificial neural network model for on-line error estimation, (iii) synthesis of the total error into a three-dimensional error form using a simplified kinematic model and finally (iv) error compensation. Implemented on a turning center, the neurocompensation approach has improved machine accuracy by reducing the maximum error without compensation from 70 μm without compensation to less than 4 μm.

An Integrated Modeling Approach for ANN-Based Real-Time Thermal Error Compensation on a CNC Turning Center

Thermally induced errors play a critical role in controlling the level of machining accuracy. They can represent a significant proportion of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design stage, active errors compensation appears to be the most economical and realistic solution. Accurate and efficient modeling of the thermally induced errors is an indispensable part of the error compensation process. This paper presents an integrated and comprehensive modeling approach for real-time thermal error compensation. The modeling process is based on multiple temperature measurements, Taguchi’s orthogonal arrays, artificial neural networks and various statistical tools to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed approach and show that the resultant model can accurately predict the time-variant spindle thermal drift errors under various operating conditions. After compensation, the thermally induced spindle errors were reduced from 19m to less than 1 m. The proposed modeling optimization strategy can be effectively and advantageously used for real-time error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty.

An On-Line ANN-Based Approach for Quality Estimation in Resistance Spot Welding

The aim of this study is to develop an effective on-line ANN-based approach for quality estimation in resistance spot welding. The proposed approach examines the welding parameters and conditions known to have an influence on weld quality, and builds a quality estimation model step by step. The modeling procedure begins by establishing relationships between welding parameters (welding time, welding current, electrode force and sheet metal thickness), welding conditions represented by typical characteristics of the dynamic resistance curve and welding quality indices (nugget diameter, nugget penetration, and indentation depth), and the sensitivity of these elements to the variation of the process conditions. Using these results and various statistical tools, three estimation models are developed. The first one is based exclusively on welding parameters. The second model is based on characteristics of the dynamic resistance curve. The third estimation model combines welding parameters and characteristics of dynamic resistance curves. In order to carry out the models building procedure, an extensive number of welding experiments are required. For this purpose, Taguchi’s efficient method of experimental planning is adopted. The results demonstrate that the developed models can provide an accurate on-line estimate of the weld quality, under different welding conditions.

Study of induction heating process applied to internal gear using 3D model

The current paper is principally dedicated to the study of geometry and frequency effects for internal spur gears heated by induction. The overall work is realized by the simulation efforts performed on Comsol multi-physics software. The 3D model used during this study is built basing on coupling between Maxwell’s and heat transfer equations. This model is used to calculate the temperature profile in the gear in function of machine parameters. The module and the frequency are varied to determinate their effects. In fact, two gears having the same external diameter but different modules are exploited during this study and the frequency is varied from low to high level. The obtained results allow understanding the effect of module and frequency on the final temperature distribution. Finally, the optimal frequency value permitting to have the best temperature profile is found.

Dynamic Resistance Based Model for On-Line Resistance Spot Welding Quality Assessment

On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance (DR) gives a relative clear picture of the welding nugget formation and presents a significant correlation with the RSW quality indicators (QI). This paper presents a structured approach developed to design an effective DR-based model for on-line quality assessment in RSW. The proposed approach examines welding parameters and conditions known to have an influence on weld quality, and builds a quality assessment model step by step. The modeling procedure begins by examining, through a structured experimental design, the relationships between welding parameters, typical characteristics of the RD curves and multiple welding QI. Using these results and various statistical tools, different integrated quality assessment models combining an assortment of DR attributes are developed and evaluated. The results demonstrate that the proposed approach can lead to a general model able to accurately and reliably provide an appropriate assessment of the weld quality under variable welding conditions.

A Comprehensive Approach for Thermal Error Model Optimization for ANN-Based Real-Time Error Compensation in CNC Machine Tools

Thermally induced errors play a critical role in the control of machining accuracy. They can account for as much as 70% of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design phase, errors compensation appears to be the most economical solution. Accurate and efficient modeling of the thermally induced errors is an essential part of the error compensation process. This paper presents a comprehensive approach for thermal error modeling optimization. The proposed optimization method is based on multiple temperature measurements, Taguchi’s orthogonal arrays, various statistical tools and artificial neural networks to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The proposed approach can be effectively and advantageously used for real-time thermal error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed optimization method and show that the resultant model can accurately predict the time-variant thermal error components under various operating conditions.

A New Approach in Optimizing the Induction Heating Process Using Flux Concentrators: Application to 4340 Steel Spur Gear

Abstract The beneficial effects of using flux concentrators during induction heat treatment process of spur gears made of 4340 high strength steel is demonstrated using 3D finite element model. The model is developed by coupling electromagnetic field and heat transfer equations and simulated by using Comsol software. Based on an adequate formulation and taking into account material properties and process parameters, the model allows calculating temperature distribution in the gear tooth. A new approach is proposed to reduce the electromagnetic edge effect in the gear teeth which allows achieving optimum hardness profile after induction heat treatment. In the proposed method, the principal gear is positioned in sandwich between two other gears having the same geometry that act as flux concentrators. The gap between the gear and the flux concentrators was optimized by studying temperature variation between the tip and root regions of gear teeth. Using the proposed model, it was possible identifying processing conditions that allow for quasi-uniform final temperature profile in the medium and high frequency conditions during induction hardening of spur gears.

Explorative Study and Prediction of Overtempering Region of Disc Heated by Induction Process Using 2D Axisymmetric Model and Experimental Tests

Thanks to many industrial benefits that it exhibits, induction heating process is very promising for its potential application in manufacturing production. To understand the industrial context, it is necessary to investigate the process by focusing on simulation and experimental aspects. In fact, this paper presents an original approach able to predict the overtempering zone with analyzing the temperature curves resulting from simulationand the hardness profile achieved by experimental validation. The proposed approach combines experimental validation and numerical simulation applied to 4340 steel disc in order to investigate the overtempering phenomenonand develop a very simplified and practical model able to predict the hardness curve with a fairly good accuracy. The developed model is validated by experimental tests and is used to evaluate the effect of machine parameters on the overtempering.

Sensitivity Study of Temperature Profile of 4340 Spur Gear Heated by Induction Process Using 3D Model

This paper presentsa sensitivity study using a Comsol3D model simulation for spur gear heated by induction process. Based on an adequate formulation and taking into account the material properties, a multi-physics 3D model is built to calculate the final temperature distribution determinate according the machine parameters and some geometrical factors (coil width and gap between coil and gear). Since the hardness profile is affected by thermal historic during heating, the surface temperatures are deeply analyzed versus the initial current density and the heating time using medium (MF) and high frequencies (HF). Finally, the sensitivity of hardness profile with the machine parameters variation isinvestigated using various statistical tools applied to the obtained results. The obtained results exhibits the main machine parameters and theirs effects on the hardness profile.

Simulation of Helical Gear Heated by Induction Process Using 3D Model

This paper is devoted to develop a 3D model applied to helical gear heated by induction process. The global work is realised by multiphysicsimulation coupling electromagneticfields and heat transfer using Comsolsoftware. This model permits to establish the temperature distribution in function of simulation parameters. The results are very promising since they allow understanding the dissymmetry effect of heating that affect the real hardness profile. Finally, a global sensitivity study about nominal values of machine parameters is conducted to quantify the effect of power consumed by the part on the surface temperature.