Automated Process Planning for Five-Axis Point Milling of Sculptured Surfaces

Ph.DDOCTOR OF PHILOSOPH

An experimental and simulation study on parametric analysis in turning of inconel 718 and GFRP composite using coated and uncoated tools

Process simulation is one of the important aspects in any manufacturing/production context because it generates the scenarios to gain insight into process performance in reasonable time and cost. With upcoming worldwide applications of Inconel 718 and Glass Fiber Reinforced Polymer (GFRP) composites, machining has become an important issue which needs to be investigated in detail. In turning of hard materials (such as Inconel 718), cutting tool environment features high-localized temperatures (~1000ºC) and high stress (~700 MPa) due to contact between cutting tool and work piece. The tool may experience repeated impact loads during interrupted cuts and the work piece chips may chemically interact with the tool materials. Therefore, the use of coated tool is preferred for turning of Inconel 718. It is observed that performance of machining process is influenced by different machining parameters such as spindle speed, depth of cut and feed rate as in case of turning. Material removal rate (MRR) and flank wear in turning of Inconel 718 using physical vapour deposition (PVD) and chemical vapour deposition (CVD) coated on carbide insert tool are reported. A simulation model based on finite element approach is proposed using DEFORM 3D software. The simulation results are validated with experimental results. The results indicate that simulation model can be effectively used to predict the flank wear and MRR in turning of Inconel 718. For simultaneous optimization of multiple responses, a fuzzy inference system (FIS) is used to convert multiple responses into a single equivalent response so that uncertainty and fuzziness in data can be addressed in an effective manner. The single response characteristics so generated is known as Multi Performance characteristic Index (MPCI). A non-linear empirical model has been developed using regression analysis between MPCI and process parameters. The optimal process parameters are obtained by a recent population-based optimization method known as imperialistic competitive algorithm (ICA). Analysis of variance (ANOVA) is performed to identify the most influencing factors for all the performance characteristics. The optimal conditions of process parameters during turning of Inconel 718 and GFRP composites are reported. It is observed that flank wear is combatively less when machined with PVD coated tool than CVD coated tool in turning of both Inconel 718 and GFRP composite

The critical raw materials in cutting tools for machining applications: a review

A variety of cutting tool materials are used for the contact mode mechanical machining of components under extreme conditions of stress, temperature and/or corrosion, including operations such as drilling, milling turning and so on. These demanding conditions impose a seriously high strain rate (an order of magnitude higher than forming), and this limits the useful life of cutting tools, especially single-point cutting tools. Tungsten carbide is the most popularly used cutting tool material, and unfortunately its main ingredients of W and Co are at high risk in terms of material supply and are listed among critical raw materials (CRMs) for EU, for which sustainable use should be addressed. This paper highlights the evolution and the trend of use of CRMs) in cutting tools for mechanical machining through a timely review. The focus of this review and its motivation was driven by the four following themes: (i) the discussion of newly emerging hybrid machining processes offering performance enhancements and longevity in terms of tool life (laser and cryogenic incorporation); (ii) the development and synthesis of new CRM substitutes to minimise the use of tungsten; (iii) the improvement of the recycling of worn tools; and (iv) the accelerated use of modelling and simulation to design long-lasting tools in the Industry-4.0 framework, circular economy and cyber secure manufacturing. It may be noted that the scope of this paper is not to represent a completely exhaustive document concerning cutting tools for mechanical processing, but to raise awareness and pave the way for innovative thinking on the use of critical materials in mechanical processing tools with the aim of developing smart, timely control strategies and mitigation measures to suppress the use of CRMs

Ultrasonic assisted machining

A commercially available DMG MORI ULTRASONIC 65 monoBLOCK machining centre was installed in WMG in 2013 and has been primarily used to machine aerospace grade materials such as carbon fibre reinforced plastic (CFRP) and titanium alloy Ti 6Al-4V (individually and stacked) and 2000 / 6000 series aluminium alloys. Rather than discuss a single set of experimental work in detail, this paper discusses some of the issues that have been encountered when applying the technique of ultrasonic assisted machining (UAM) and some of the effects that have been observed using examples from the research conducted so far to illustrate some of the more important findings

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

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