Influence de l'état de surface et du serrage sur les outils assemblés par frettage

Interface outil/porte-outil/ -- Les porte-outils frettés -- Le coefficient de friction statique -- Le frettage -- Modélisation des assemblages frettés -- Vibrations et amortissement -- Les paramètres de l'état de surface -- Effet du fini de surface sur les assemblages frettés -- Étude de l'interface porte-outil/outil -- Effet de l'état de surface sur l'amortissement -- Conclusion de l'étude bibliographique et identification des besoins -- Effect of surface texture and contact pressure on the static friction coefficient of shrink fit assembly -- Effect of roughness and interference on torque capacity of a shrink fitted assembly -- Effect of roughness and interference on domping of shrink fitted assemblies

Machinability assessment and tool selection for milling.

SIGLEAvailable from British Library Document Supply Centre-DSC:DX204223 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Research and development of a reconfigurable robotic end-effector for machining and part handling.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF

Towards an integrated strategy for effective machine tool probing

Spindle-mounted probing systems on machine tools have become commonplace within manufacturing industry over the last ten years. On-machine probing (OMP) is often used as an automatic way of finding workpiece offsets on machine tools to allow better repeatability and more automated procedures. In more advanced engineering OMP may be used for in-process gauging, error correction or even final pass-off. They therefore form part of the measurement process that defines the overall quality control chain. When used in production the probing system, and the machine that carries them, is subjected to harsh and variable conditions that are not experienced in a metrology room. This includes undergoing tool change routines, being affected by changing thermal conditions and contaminated by cutting debris. Unlike a traceable quality control department there is also the possibility of comparatively poor calibration techniques as this is considered a machine tool function. As a result, many companies do not use their OMP effectively, either because of a lack of understanding of capability or mistrust in the results due to a lack of understanding of uncertainties. This research has provided the building blocks for a “bottom up” strategy to analyse influencing factors for OMP to enable end users to get reliable results with the lowest uncertainty for the desired function. Crucially, the research assumes different tolerance requirements for various functions that can be performed with such a probing system in order to avoid recommending onerous pre-requisite procedures where they are not needed. The influencing factors for OMP are identified through literature review and a failure mode analysis conducted with experts from industry and academia. The magnitudes of the key factors and effects are then analysed through stated system specifications or experimentation on example machines. However, the dissertation concluded that all machine tools behave differently, and have different accuracy requirements. The main outcome of this work is, therefore, to determine which tests and checks are pre-requisite for OMP on any machine, depending upon their intended use. Ultimately, though, the work concludes that the unpredictability of thermal behaviour is likely to cause the greatest problems for on-machine probing, with a full understanding being required for reproducibility of measurement results from a machine tool

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems