Tool Condition Monitoring and Surface Topography Analysis During the Machining of CFRP Composites
Bibliographic record
Abstract
RESUME Les composites polymeres notamment FRP (fibre reinforced plastics) sont de plus en plus employes dans l’industrie manufacturiere (aeronautique et automobile notamment) pour la confection de leurs produits. L’emploi de carbone dans les FRP est en particulier prise pour ses caracteristiques mecaniques. Depuis quelques decennies, le melange de materiaux composites et metalliques au sein de structures de produits apporte des defis supplementaires lors de leur assemblage. Face a ces nouveaux materiaux, les scientifiques et industriels reemploient les techniques qui ont ete bâties et fait leurs preuves pour des materiaux metalliques relativement homogenes. Cependant, de par les proprietes intrinseques des composites FRP, l’etat de surface est fondamentalement heterogene et la caracterisation de l’etat de surface en devient complexe. Un autre point, de par ces specificites, usiner un tel materiau provoque des petites vibrations additionnelles que l’on pourrait qualifier de bruit sur des signaux vibratoires, en comparaison avec l’usinage de metaux. Cette these vise a repondre aux problemes de caracterisation de surface mais aussi au developpement d’une technique novatrice de suivi de la qualite de coupe. La technique developpee se base sur la theorie fractale et l’analyse fractale pour repondre aux besoins de la quantification du bruit sur les signaux de forces de coupe ou de vibration. L’analyse fractale permet de mettre en evidence le niveau de complexite et de rugosite d’objets. Pour bâtir et valider cette methode, plusieurs tests ont ete realises : detourage, percage axial et orbital. Malgre le niveau elementaire du developpement de cette technique, les premiers resultats sont prometteurs. Mise en place sur une cellule de fabrication, cette methode permet d’evaluer, en un temps tres court, le niveau d’usure d’outil (en vue de son possible remplacement) ainsi que la qualite de coupe generee. Pour la caracterisation de surface de composites lamines, l’etude s’est premierement basee sur des releves de profils de surface et de rugosite. Dans chacune des deux directions principales (dans le sens du pli et dans le sens de l’empilement), des problemes specifiques ont ete mis en evidence. Et pour chacune des directions, un plan correctif des methodes de caracterisation actuelles a ete propose. Le resultat de ces optimisations proposees permet de mieux evaluer l’etat de surface au vue de la qualite de coupe ainsi que de la robustesse du procede.----------ABSTRACT Polymer composites such as FRPs (fiber reinforced plastics) have been increasingly used by the manufacturing industry (aerospace and automotive in particular) in the making of their products. The carbon selection in FRPs is especially preferred for its mechanical properties. For few decades, the combination of composite and metallic materials within products’ structure has been raising up additional challenges during the assembly. To solve those issues, scientists and industrials often reused techniques that had been built and proved for relatively homogeneous metallic materials. However, due to the intrinsic properties of FRP composites, the surface condition is particularly heterogeneous and such surface condition characterization tend to be more complex. Furthermore, machining a FRP material generates additional short vibrations that could be evaluated as noise in the vibration signals in comparison with the machining of metals. This thesis is a contribution to solutions to surface characterization issues and proposes an innovative technique of machining quality monitoring. The developed technique is based on the fractal theory and fractal analysis to meet the needs of the noise assessment in the cutting forces or vibration signals. Fractal analysis allows to evaluate the complexity and roughness of objects. To build and validate this method, several tests were performed: trimming, drilling axial and orbital. Despite the relatively low level of development of this technique, first results are promising. Set up in a manufacturing cell, this method allows to evaluate, in a very short time, the tool wear (for possible tool replacement) and the machining quality. For laminated composites’ surface characterization, the study is primarily based on surface profiles and roughness observations. For both two main directions (in the ply plane direction and in the direction of the stack sequence), specific problems have been highlighted. And for each direction, a corrective plan of current characterization methods proposed. The result of those proposed enhancements to better evaluate the surface condition in view of the machining quality and process robustness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".