An analytical model of orthogonal metal cutting: implementation using LMFIT library in Python

International audienceIn this study, an analytical model for orthogonal metal cutting is presented for predicting cutting forces and temperature at tool-chip interface. In this approach, the properties of the materials are modeled by the Johnson-Cook constitutive material flow law, where the stress is a function of strain, strain rate, and temperature. The aim of the proposed work is to improve the numerical resolution of the analytical model. The determination of the optimal cutting parameters is based on the use of the Nonlinear Least-Squares Minimization and Curve-Fitting library for Python (LMFIT) whith a dual Levenberg-Marquardt optimization algorithm that has been developed and implemented in Python. The performance of the developed model has been studied by comparing its predictions with some experimental machining data for 1045 steels. A good correlation between the results of the proposed model and those resulting from literature and experiments has been demonstrated

A Proposed Scheme for Fault Discovery and Extraction Using ANFIS: Application to Train Braking System

This paper showcases the use of model oriented techniques for real time fault discovery and extraction on train track unit. An analytical system model is constructed and simulated in Mathlab to showcase the fair and unfair status of the system. The discovery and extraction phases are centered on a hybrid adaptive neuro-fuzzy inference feature extraction and segregated module. Output module interprites zero (0) as a good status of the traintrack unit and one (1) as an unpleasant status. Final results showcase the robustness and ability to discover and extract multitude of unpleasant scenarios that hinder the smooth operations of train track units due to its high selectivity and sensitivity quality

An analytical model of orthogonal metal cutting: implementation using LMFIT library in Python

In this study, an analytical model for orthogonal metal cutting is presented for predicting cutting forces and temperature at tool-chip interface. In this approach, the properties of the materials are modeled by the Johnson-Cook constitutive material flow law, where the stress is a function of strain, strain rate, and temperature. The aim of the proposed work is to improve the numerical resolution of the analytical model. The determination of the optimal cutting parameters is based on the use of the Nonlinear Least-Squares Minimization and Curve-Fitting library for Python (LMFIT) whith a dual Levenberg-Marquardt optimization algorithm that has been developed and implemented in Python. The performance of the developed model has been studied by comparing its predictions with some experimental machining data for 1045 steels. A good correlation between the results of the proposed model and those resulting from literature and experiments has been demonstrated

Anomaly detection in orthogonal metal cutting based on autoencoder method

The choice of appropriate cutting conditions is widely acknowledged as a key performance indicator for efficient machining, since it allows to mitigate tools wear. In precision machinery, tool wear can indeed lead to poor surface quality and even affect the dimensions of the final product. In such a context, the cutting and feed forces, along with other parameters are affected and are not optimal. It is therefore necessary to detect any anomaly and provide the operators in the plant with a decision support, allowing to monitor the machining conditions in order to predict the outputs values and anticipate the wear's damage on the whole cutting process. In this article, cutting speed, cutting angle, cutting width and feed depth are the input parameters. Using the extended-Oxley analytical model of orthogonal metal cutting, a sample of cutting conditions has been simulated in order to analyze the optimality of the cutting force, the feed force and the internal temperature, based on the autoencoder method. Given a threshold for decision, the results allow to identify abnormal parameters and provide significant insights for operators, allowing them to avoid error and make the best choices of the inputs for optimal cutting conditions

Rheological behaviour of tropical clays of Togo (West Africa) under loads for energy saving in clay materials processing: numerical and experimental approaches

Exigences pour le respect de l'environnement encouragent à réduire l'impact de l'activité humaine sur la nature. Génie civil répond à ces exigences par le développement de matériaux de construction écologiques. Cet article traite de la transformation de l'argile matières premières qui permettent le traitement de matériaux de construction respectueux de l'environnement: en plus de leur biodégradabilité, les alvéoles ont tiré matériaux argileux permettent des économies d'énergie en chauffage de la maison grâce à leurs propriétés d'isolation thermique. Mais leur fabrication est un procédé de forte consommation d'énergie, en particulier pendant le compactage, le séchage et la cuisson qui contribuent à l'émission de gaz de serre. Le but de cet article est d'étudier la rhéologie des pâtes d'argile afin de développer des procédés de fabrication à faible consommation d'énergie. A cet effet, les approches théoriques et expérimentaux ont été réalisés sur six variétés d'argile. Dans l'approche théorique, un élément fini (FE) modèle de simulation a été développé pour presser un matériau non rigide prédire les déformations et les contraintes se produisant dans la structure de l'argile. Des expériences ont ensuite été effectuées pour valider la modélisation par éléments finis. Dans cette approche expérimentale, les pâtes d'argile ont été transformées avec une teneur en eau en respectant les limites d'Atterberg qui déterminent la plasticité de l'argile. Le compactage des échantillons a été réalisée sous chargements variables afin de déterminer la faible charge appropriée de la consommation d'énergie. La présente étude est réalisée dans le cadre d'un projet de recherche coopérative, y compris les laboratoires suivants: Institut de Recherche sur le Transport, l'Energie et la Société IRTES-M3M Belfort / France (TCHOMBI et al, 2012, [2], civil et génie des matériaux Département-Ecole Royale Militaire de Bruxelles / Belgique, Centre de la Construction et du Logement-Unité de Recherche sur les Matériaux et les Agroressources , le CCA-URMA-Togo

Opportunities of natural polymers in the biocomposites based from Agro-Resources: Grewia venusta mucilage and Bombax costatum calyx, two tropical plants like sources of natural binders for particleboards manufacturing

Until about 1920, world was essentially based on use of materials for agro-based resources. With the coming of high performance metals, ceramics, plastics and other synthetics, the use of agro-based derived materials has lost its market share. Now, we are aware that our landfills are filling up, our resources are depleted and our planet becoming polluted. Today, it is not more to prove detrimental interest of synthetic materials (plastics or formaldehydes adhesives) on the environment and human health. Because of this, there is renewed interest of technologies which respect environment for the production of materials and products recyclable and biodegradable. In this article we present results of a prospecting study and qualitative evaluation of properties of natural binders in Grewia venusta stem bark and Bombax costatum calyx flower sepal. These two mucilaginous plants were studied as potential bio adhesive sources in the development of formaldehyde-free particleboard. Mucilage and pectin fractions of both plant organs were analysed for monosaccharides identification and quantification. The binding properties of these mucilages were investigated by testing mechanically particleboard made with the extracted mucilages. The aqueous extraction and ethanolic precipitation, followed by ionic chromatography gave of some qualitative results interesting. The mechanical tests of the panels realised following the standard requirements ANSI 208.1, have given interesting results

Semi-vitrified porous kyanite mullite ceramics: Young modulus, microstructure and pore size evolution

Microporous porcelain formulations are successfully carried out through sintering processing. During the thermal treatment of ceramic products, it was found that the addition of kyanite together with ϕ- and γ-Al2O3 allowed to enhance interconnected pores network with micrometric size from 0.1 to 9 µm in a semi-vitrified composite. Between 1200 and 1350 °C, the mullitization of kyanite hindered the extension of vitrification and the growth of acicular mullite from the transformation of metakaolin. The main pores size decreased from 4.33 to 1.54 µm for the formulation containing 32 wt% of kyanite. In this interval the specific pore area increased from 0.64 to 8.75 m2 g−1 due to the total conversion of the kyanite to fibrous and acicular mullite that reduced the voids provided by the earlier mullitization. The improvement in the mullitization without extensive vitrification and grain growth and the reduction of the pores size with the increase in the specific pore area contributed to the formation of a microporous matrix with the Young's modulus increased from 7 to > 20 GPa. The microstructure of the microporous porcelain, their specific pore area and pores size as well as the interconnection of pores was found innovative for the applications in the field of engineering filtration where high mechanical strength, strain, stiffness and pressure resistance are required

Optimization of the model of Ogden energy by the genetic algorithm method

The model of Ogden, is a density of energy used in the modeling of hyperelastic materials behavior. This model of energy presents a high number of material parameters to identify. In this paper, we expose a method of identification of these parameters:Genetic Algorithm. This method contrary to the method of Beda-Chevalier, Least Squares, directed programming object method, PSA (Pattern Search Algorithm) and LMA (Levenberg-Marquardt), allows to identify quickly good parameters which give to the Ogden model a very good prediction in uniaxial tension, biaxial tension and pure shear. This prediction is considered to be better becausewe better bring the experimental curve closer to Treloar one with the parameters optimized by the genetic algorithm