Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite

Carbon fiber-reinforced plastic (CFRP) is gaining wide acceptance in areas including sports, aerospace and automobile industry . Because of its superior mechanical qualities and lower weight than metals, it needs effective and efficient machining methods. In this study, the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated for CFRP composite. For machining of CFRP, CNC turning operation with coated carbide tool is used. An ANFIS model with two MISO system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameter. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. However, Design of Experiments (DOE) confirmation of this experiment fails because of multi-collinearity problem in the dataset and insufficient experimental data points to predict the tool wear and surface roughness effectively using ANFIS methodology. Therefore, the result of this experiment do not provide a proper representation, and result in a failure to conform to a correct DOE approach

Fuzzy rule based optimization in machining of glass fiber reinforced polymer (GFRP) composites

With the increasing use of Fiber Reinforced Polymer (FRP) composites outside the defense, space and aerospace industries; machining of these materials is gradually assuming a significant role. The current knowledge of machining FRP composites is in transition phase for its optimum economic utilization in various fields of applications. Therefore, material properties and theoretical mechanics have become the predominant research areas in this field. With increasing applications, economical techniques of production are indeed very important to achieve fully automated large-scale manufacturing cycles. Although FRP composites are usually molded, for obtaining close fits and tolerances and also achieving near-net shape, certain amount of machining has to be carried out. Due to their anisotropy, and non-homogeneity, FRP composites face considerable problems in machining like fibre pull-out, delamination, burning, etc. There is a remarkable difference between the machining of conventional metals and their alloys and that of composite materials. Further, each composite differs in its machining behavior since its physical and mechanical properties depend largely on the type of fibre, the fibre content, the fibre orientation and variabilities in the matrix material. Considerable amount of literature is readily available on the machinability of conventional metals/alloys and also polymers to some extent; with very limited work on FRP composites. Therefore, machining process optimization for all types FRP composites is still an emerging area of research. In this context, the present research highlights a multi-objective extended optimization methodology to be applied in machining FRP-polyester/epoxy composites with contradicting requirements of quality as well as productivity. Attempt has been made to develop a robust methodology for multi-response optimization in FRP composite machining 6 for continuous quality improvement and off-line quality control. Design of Experiment(DOE) has been be selected based on Taguchi’s orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with Material Removal Rate (MRR) of the machining process have been optimized simultaneously. A Fuzzy Inference System (FIS) integrated with Taguchi’s philosophy has been proposed for providing feasible means for meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi Performance Characteristic Index (MPCI) has been optimized finally. Detailed methodology of the proposed fuzzy based optimization approach has been illustrated in this reporting and validated by experiments

Studies on some aspects of composite machining

In this technological era, globalization has brought new challenges for the manufacturing industries, towards improving quality and productivity simultaneously, by reducing costs and increasing the performance of the machine tools. Process simulation is one of the most important aspects in any manufacturing/production context. With upcoming worldwide applications of Glass Fiber Reinforced Polymer (GFRP) composites; machining has become an important issue which needs to be investigated in detail. Process efficiency is measured in the sense of different objective functions or process output responses weather they are acceptable for a given targeted value or tolerance. Therefore, finding the best optimal parameter combination can lead towards improvement of the overall process efficiency. The performance of the process can be improved by applying optimization to the simulation model with respect to its process parameters. Single objective optimization method often creates conflict, when more than one response variables need to be optimized simultaneously. In order to minimize cost and to maximize production rate simultaneously; multi-objective optimization approach should be explored. In this thesis, multi-objective optimization methods have been reported to study some aspects of machining of composite material i.e. Glass Fiber Reinforced Polymer (GFRP) composite. The various process parameters used were cutting speed, feed rate, and depth of cut. Optimal cutting condition has been aimed to be evaluated to satisfy contradicting multi-requirements of product quality as well as productivity. This thesis has intended towards focusing two important aspects (i) when it comes to improve productivity, material removal rate has been considered and for (ii) quality of the machined composite product, various surface roughness characteristics of statistical importance have been investigated

Hybrid finite element–smoothed particle hydrodynamics modelling for optimizing cutting parameters in CFRP composites

Carbon-fibre-reinforced plastic (CFRP) is increasingly being used in various applications including aerospace, automotive, wind energy, sports, and robotics, which makes the precision modelling of its machining operations a critical research area. However, the classic finite element modelling (FEM) approach has limitations in capturing the complexity of machining, particularly with regard to the interaction between the fibre–matrix interface and the cutting edge. To overcome this limitation, a hybrid approach that integrates smoothed particle hydrodynamics (SPHs) with FEM was developed and tested in this study. The hybrid FEM-SPH approach was compared with the classic FEM approach and validated with experimental measurements that took into account the cutting tool’s round edge. The results showed that the hybrid FEM-SPH approach outperformed the classic FEM approach in predicting the thrust force and bounce back of CFRP machining due to the integrated cohesive model and the element conversion after failure in the developed approach. The accurate representation of the fibre–matrix interface in the FEM-SPH approach resulted in predicting precise chip formation in terms of direction and morphology. Nonetheless, the computing time of the FEM-SPH approach is higher than the classic FEM. The developed hybrid FEM-SPH model is promising for improving the accuracy of simulation in machining processes, combining the benefits of both techniques

Edge Trimming of CFRP- Surface Roughness Measurement and Prediction

Use of carbon fibre composites has been increasing in the aerospace industry. However, there is still a need for finishing operations by conventional machining in the manufacturing of composite parts. Composites have a very different machinability to metals and can suffer from a number of surface defects during machining. The fibres are also highly abrasive and can cause rapid tool wear which in turn leads to increased likelihood of machining defects. This project has focussed on the machined surface quality developed during machining using new surface inspection techniques and additional surface roughness parameters. It is important to be able to accurately measure the surface roughness in order to ensure the integrity of in service components and quantify surface damage from machining. The aim of this project is to develop new numerical modelling techniques for the edge trimming of carbon fibre reinforced plastic (CFRP), and develop methods for the prediction of surface roughness. Different experimental techniques have been used to analyse post-machining damage, including scanning electron microscopy (SEM), computed tomography scanning (CT) and a focus variation system for measuring surface roughness. CFRP specimens have been edge trimmed using a poly crystalline diamond (PCD) cutting tool, and compared for different machining parameters, tool wear and material fibre orientations. Cutting forces were recorded and the surface quality was inspected using the optical focus variation method. Regression models from experimental data have been combined with finite element (FE) models to create a surface roughness prediction tool which includes the effects of tool wear. Areal surface roughness Sa measurements were taken using the optical system and the advantages of the system have been compared with conventional stylus roughness measurement methods. Experimental data was used to validate 3D and 2D FE milling models using MSC Marc. New FE models were developed using adaptive re-meshing, and user subroutine to control the cutting tool movement and simulation idle time. Progressive levels of tool wear have been implemented in the 2D model by using cutting edge radius measurements from experiment. FE and experimental results show that tool wear and material fibre orientation have a significant effect on the cutting forces and surface roughness. Regression models showed that the surface roughness was most affected by tool wear, feed rate and cutting speed. A reasonable comparison has been found between FE and experiment and the FE models were capable of predicting the effects of tool wear due to cutting edge rounding. 3D models were found to better predict thrust forces than 2D FE model. The optical system was found to be useful technique for measuring surface roughness of machined fibrous composite surfaces and is more reliable than conventional roughness measurements. New strategies for roughness measurement have been recommended

Behaviour of polymeric materials in machining

The machining characteristics of a glassy thermoplastic (Polyvinyl Chloride) and a semi-crystalline thermoplastic (High Density Polyethylene) have been studied. Chip formation mechanisms, cutting forces and surface integrity were found to be dependent, on the cutting conditions and tool geometry. Results were explained by considering the different nature of the microstructure. Segmented and discontinuous chips were produced with PVC, and continuous and segmented chips were produced with HDPE. It was observed that surface damage was closely related to the nature of chip formation in these plastics. Chip formation, surface damage and tool wear mechanisms when machining Glass-Fibre-Reinforced-Plastic (GFRP) were also studied. Cutting tools used were High-Speed-Steel (HSS), cemented carbide (P type and K type) and coated carbide (titanium carbide - and triple-coated). Discontinuous chips were always produced when machining GFRP. Sliding contact is present at the tool/chip and tool/work interface. The principal aspects of surface damage include fibre breakage, resin cracking, resin decomposition and fibre/resin interface debonding. Cutting temperature is not high, but excessive heat generates when the flank wear land develops. Coated carbide tools showed the best performance and HSS tools the poorest. The main wear mechanisms are abrasive wear with HSS tools, attrition wear with cemented carbides, and discrete plastic deformation followed by attrition wear with coated carbides

The Science and Technology of 3D Printing

Three-dimensional printing, or additive manufacturing, is an emerging manufacturing process. Research and development are being performed worldwide to provide a better understanding of the science and technology of 3D printing to make high-quality parts in a cost-effective and time-efficient manner. This book includes contemporary, unique, and impactful research on 3D printing from leading organizations worldwide

FULL-FIELD DAMAGE ASSESSMENT OF NOTCHED CFRP LAMINATES

The work presented in this thesis constitutes the first dedicated application of surface full-field experimental techniques to the comprehensive damage assessment of open-hole compression (OHC) in composite laminates, under both static and fatigue loading. The relevance of the work comes from OHC being one of the two main tests used in industry to measure damage tolerance of composite material systems. The main motivation for the work is the existence of a gap in the published literature pertaining to the location of the occurrence of different damage events during the life of notched composite structures. Additionally, the effect of toughening laminates by interleaving of particles, intended to improve the damage tolerance, was studied. As such, the main goal was to demonstrate the viability of using full-field non-contact experimental techniques to study the evolution of damage in notched carbon fibre reinforced polymer laminates. The specific techniques used were thermoelastic stress analysis (TSA) and digital image correlation (DIC). It was found that a characteristic damage sequence is independent of the material system and that final failure of the laminate is controlled by the development of crush zones at the east and west sides of the hole. These crush zones result from the collapse of kink bands whose development is in turn controlled by matrix cracking early in the life of the laminate. Hence, by characterizing the sequence of damage events and their occurrence in notched coupons, the design allowables of actual composite structures can be better approximated. Pertaining to the effect of particle interleaving, statistical analysis of life data demonstrated that it could not be concluded that this kind of toughening improves the OHC fatigue life of the laminates tested. The work presented in thesis thereby demonstrates that TSA and DIC can be applied to the study of damage in composite laminates and, thus, represents a significant step towards an improved understanding of damage morphology and evolution in heterogeneous materials

Cognitive Sensor Monitoring of Machining Processes for Zero Defect Manufacturing

The topic of this thesis, focused on cognitive sensor monitoring of machining processes for zero defect manufacturing, has been addressed within the framework of the international research project EC FP7 CP-IP “IFaCOM – Intelligent Fault Correction and self Optimizing Manufacturing systems” (2011-2015; FoF NMP – 285489) and the national MIUR PON Project on “Development of eco-compatible materials and technologies for robotised drilling and assembly processes – STEP FAR” (2014-2016). The vision of the IFaCOM project is to achieve near zero defect level of manufacturing with particular emphasis on the production of high value, large variety and high performance products. This goal is achieved through the development of improved methodologies for monitoring and control of the performance of manufacturing processes with the aim to detect abnormal process conditions leading to defects on the produced parts. The overall aim of the STEP FAR project is the study of issues related to drilling and cutting techniques of advanced lightweight components, such as composite material parts, and their relative assembly, using cooperating anthropomorphic robots. The use of innovative materials and processes developed in this research will lead to a reduction in weight and environmental impact in the construction and maintenance of primary aircraft structures. At least a 5% reduction in weight of the structures is foreseen without increase of costs (a possible rise in the cost of raw materials is compensated with the reduction of process costs). In aeronautical industry the reduction of the weight of the aircraft is becoming an increasingly important aim both for environmental requirements (lower emissions) and contraction of the management costs (lower fuel consumption). Therefore new structural architectures through the use of innovative materials and technologies have been developed. One of the innovative processes analysed in this project is the drilling via machining of carbon fibre reinforced plastic (CFRP) stack-ups. In the framework of these projects, this thesis work is focused on the development of cognitive condition monitoring procedures for zero defect machining processes with reference to two different industrial manufacturing applications. The thesis is organized as follows: Chapter 2 reviews the general concept of sensor monitoring of manufacturing processes and provides a comprehensive survey of sensor technologies, advanced signal processing techniques, sensor fusion approach, and cognitive decision making strategies for process monitoring. In Chapter 3, the Strecon industrial case, as a partner of the IFaCOM project, is discussed and analysed. The STRECON end-user case is focused on improving repeatability and predictability of the surface finish produced by a Robot Automated Polishing (RAP) process. In order to establish a robust method for the detection of the polishing process end-point, i.e. the determination of the right moment for tool and abrasive paste change, STRECON sensor system selection focuses on monitoring the progress of the surface quality during the polishing process by means of variation in VQCs (Vital Quality Characteristics), i.e. roughness and gloss of the polished surface. The output data have been used to train a neural network. The employed NN learning procedure was the leave-k-out method where k cases from the training set are put aside in turn, while the other cases are used for NN training. In Chapter 4, the Alenia Aermacchi industrial case, as coordinator and partner of the STEP FAR project, is discussed and analysed. The Alenia Aermacchi user case is based on the analysis of drilling of stacks made of two overlaid carbon fibre reinforced plastic composite laminates. In this case, a neural network based cognitive paradigm based on a bootstrap procedure has been used for the identification of correlations with tool wear development and product hole quality. Finally, Chapter 5 reports the concluding remarks and future developments of this work