Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Parametric optimization for cutting forces and material removal rate in the turning of AISI 5140

The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR

Evaluation of a top hole full return drilling system applying a concentric dual drill string and an integrated pump

Master's thesis in Offshore Technology: Marin and Subsea technologyThis thesis evaluates the possibility for a full mud return, top hole drilling system, applying a concentric dual drill string and an integrated pump. Top holes are usually drilled without mud return, leaving the cuttings on the sea floor. Sea water with barite and other additives are employed as drilling fluid and is released to the sea when used. By employing a dual drill string and a down hole pump to lift the return to top side facilities, full return is enabled. This facilitates the use of high performance mud, which have several advantages, including primary well control before the BOP is set, improved hole stability, elimination of a pilot hole to check for shallow gas influx and extended top hole sections. Possible solutions to obtain a complete and functioning new system have been analyzed. Based on existing technology and its current limitations, two alternative systems are developed on a conceptual level. The first system includes one integrated return pump, the second employs multiple integrated return pumps. The design base case is set to 1000 meter water depth and 500 meter deep well, of which 100 meter is drilled with a 36” drill bit, and 400 meter is drilled with a 26” drill bit. This base case covers most of the top holes drilled on the Norwegian sector. System pressure estimates are presented, and a mud level regulation solution is developed and analyzed. The mud level regulation system allows the mud level in the well to be controlled to keep the well balanced and stabilized, and to prevent mud discharges to sea floor. The level regulation solution is theoretically proved, and enables reliable regulation of the mud level in the well based on existing technology. Predictions of the system behavior are made, and the limitations of the systems are presented. The developed systems drilling capacities are analyzed and found not capable of fulfilling the base case requirements, due to the limitations of the selected dual drill pipe. The low flow rate of the pipe limits the ROP, due to high cutting generation with large drill bit diameters. The hydraulic horsepowers at the drill bit nozzles are also too low, due to the lowered available pressure drop, low flow rate, and large drill bit. However, the available pressure drop at the drill bit nozzles are estimated to over 80 bar. It is recommended to employ a larger dual drill pipe, with increased pressure capacity. Then the drilling capacity of the system would be comparable to other full return top hole drilling systems. The systems impact on cost and drilling parameters are discussed and found to be comparable with other innovative solutions for full return top hole drilling. There are uncertainties of both developed systems. The uncertainties regarding the system employing only one return pump concerns the design limitations of the chosen return pump type, a progressive cavity pump. The uncertainties regarding the multiple return pump system, concerns the system behavior with several return pumps distributed throughout the drill string. A full return top hole drilling system employing a concentric dual drill string and an integrated pump is found feasible. But due to existing technology limitations, a mud motor is chosen to power the return pump, this demands a drill pipe with a higher capacity than what exists today, to obtain comparable drilling capacity to other top hole drilling systems. The development of an electric conducting dual drill pipe would expand the possibilities much further, and improve the overall drilling capacity of the system

Design and analysis of the internally cooled smart cutting tools with the applications to adaptive machining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Adaptive machining with internally cooled smart cutting tools is a smart solution for industrial applications, which have stringent manufacturing requirements such as contamination free machining (CFM), high material removal rate, low tool wear and better surface integrity. The absence of cutting fluid in CFM causes the cutting tool and the workpiece subject to great thermal loads owing to higher friction and adhesion, and as a result may increase the levels of tool wear drastically. The increase in cutting temperature may influence the chip morphology which in return producing metal chips in unfavourable ribbon or snarl forms. CFM is difficult to be realized as contaminants can be in various forms in the machining operation and to avoid them totally requires a very tight controlled condition. However, the ecological, economical and technological demands compel the manufacturing practitioners to implement environmentally clean machining process (ECMP). Machining with innovative cooling techniques such as heat pipe, single-phase microduct, cryogenic or minimum quantity lubrication (MQL) has been intensely researched in recent years in order to reduce the cutting temperature in ECMP, thus enabling the part quality, the tool life and the material removal rate achieved in ECMP at least equate or surpass those obtained in conventional machining. On the other hand, the reduction of cutting temperature by using these techniques is often superfluous and is adverse to the produced surface roughness as the work material tends to inherent brittle and hard property at low temperature. Open cooling system means the machining requires a constant cooling supply and it does not provide a solution for process condition feedback as well.This Ph.D. project aims to investigate the design and analysis of internally cooled cutting tools and their implementation and application perspectives for smart adaptive machining in particular. Circulating the water based cooling fluid in a closed loop circuit contributes to sustainable manufacturing. The advantage of reducing cutting temperature from localized heat at the tool tip of an internally cooled cutting tool is enhanced with the smart features of the tool, which is trained by real experimental data, to cognitively vary the coolant flow rate, cutting feed rate or/and cutting speed to control the critical machining temperature as well as optimum machining conditions. Environmental friendly internal micro-cooling can avoid contamination of generated swarf which can also reduce the cutting temperature and thus reduce tool wear, increase machining accuracy and optimize machining economics. Design of the smart cutting tool with internal micro-cooling not only takes into account of the environmental aspects but also justifies with its ability to reduce the machining cost. Reduction of production cost can be achieved with the lower consumption of cooling fluid and improved machining resources/ energy efficiency. The models of structural, heat transfer, computational fluid dynamics (CFD) and tool life provide useful insight of the performance of the internally cooled smart cutting tool. Experimental validation using the smart cutting tool to machine titanium, steel and aluminium, indicates that the application of internally cooled smart cutting tools in adaptive machining can improve machining performance such as cutting temperature, cutting forces and surface quality generated. The useful tool life span is also extended significantly with internally cooled smart cutting tools in comparison to the tool life in conventional machining. The internally cooled smart cutting tool has important implications in the application to ECMP particularly by overcoming the stigma of high uncontrollable cutting temperature with the absence of cooling fluid.Brunel Universit

Smart machining system platform for CNC milling with the integration of a power sensor and cutting model

Novel techniques and strategies are investigated for dynamically measuring the process capability of machine tools and using this information for Smart Machine System (SMS) research. Several aspects of the system are explored including system integration, data acquisition, force and power model calibration, feedrate scheduling and tool condition monitoring. A key aspect of a SMS is its ability to provide synchronization between process measurements and model estimates. It permits real time feedback regarding the current machine tool process. This information can be used to accurately determine and keep track of model coefficients for the actual tooling and materials in use, providing both a continued improvement in model accuracy as well as a way to monitor the health of the machine and the machining process. A cutting power model is applied based on a linear tangential force model with edge effect. The robustness of the model is verified through experiments with a wide variety of cutting conditions. Results show good agreement between measured and estimated power. A test platform has been implemented for performing research on Smart Machine Systems. It uses a commercially available OAC from MDSI, geometric modeling software from Predator along with a number of modules developed at UNH. Test cases illustrate how models and sensors can be combined to select machining conditions that will produce a good part on the first try. On-line calibration allows the SMS to fine tune model coefficients, which can then be used to improve production efficiency as the machine learns its own capabilities. With force measurements, the force model can be calibrated and resultant force predictions can be performed. A feedrate selection planner has been created to choose the fastest possible feedrates subject to constraints which are related to part quality, tool health and machine tool capabilities. Monitoring tangential model coefficients is shown to be more useful than monitoring power ratio for tool condition monitoring. As the model coefficients are independent of the cutting geometry, their changes are more promising, in that KTC will increase with edge chipping and breakage, while KTE will increase as the flank wearland expands

Monitoring of hybrid manufacturing using acoustic emission sensor

The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the entire hybrid manufacturing process. During the laser metal deposition, the acoustic emission sensor will monitor the defect formation. The acoustic emission sensor will monitor the depth of cut during milling machining. There are three tasks in this study. The first task addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. The second task, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis (K-means clustering) in conjunction with a principal component analysis (PCA). In the third task, a study was conducted to investigate the ability of AE to detect and identify defects during laser metal deposition using a Logistic Regression Model (LR) and an Artificial Neural Network (ANN) --Abstract, page iv

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

An investigation on micro cutting mechanics: Modelling, simulations and experimental case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Micro cutting is becoming increasingly important since miniature and micro components/products have become more and more demanded in precision engineering applications and consumer goods in a daily life. Meanwhile, it has not been thoroughly investigated yet. Scientific understanding of the fundamentals in micro cutting mechanics and physics is vital for micro manufacturing of micro or miniature components and products. Consequently, the scientific investigation on micro cutting mechanics is critically needed, particularly on its key fundamental aspects on which a systematic approach and key enabling technologies are developed for micro manufacturing. Therefore, three key fundamental aspects of micro cutting mechanics have been identified for this PhD project and a comprehensive systematic research has been performed through both theoretical and experiment-based investigations. The three aspects of micro cutting mechanics mainly include dynamic stiffness investigation, innovative micro cutting force modelling, and the study on micro cutting heat, temperature and their partitioned distribution. All experiment-based investigations are undertaken on a diamond turning machine test rig supported with a fast tool servo (FTS) using different reconfigured experimental setups. The finite element (FE)-based analysis is conducted to further support the in-depth analysis on the micro cutting phenomena especially the modelling and simulation of micro cutting force and temperature. Accordingly, both micro cutting force modelling and micro cutting temperature are investigated using modelling and simulation supported by well-designed experimental cutting trials and validations.The investigation on dynamic stiffness in the micro cutting system is focused on its effects on the micro cutting process and its control strategies. The burrs formation and machining accuracy are explored in relation with control of the dynamic stiffness. Furthermore, the control algorithm for dynamic stiffness is developed accordingly in order to minimise burrs formation and stabilize the micro cutting accuracy.The micro cutting force modelling is performed based on specific cutting force, i.e. modelling the cutting force at the unit cutting length or area as coined as the amplitude aspect of the proposed cutting force modelling. The cutting force against a dynamically varied cutting time interval is proposed as the spatial aspect of the cutting force formulation. The amplitude aspect can provide the insight into the micro cutting phenomena particularly in relation with the chip formation and size-effects. The spatial aspect, using a on the wavelet transform (WT) technique and standard deviation analysis can render the dynamic behaviour of the micro cutting force, particularly representing the dynamic effects of the cutting process and its correlation with tool wear.The micro cutting temperature is investigated to formulate the scientific understanding of cutting temperature, heat and their partitioned distribution particularly at the tool-workpiece-chip interface zone in ultraprecision and micro cutting using a diamond cutting tool. The contribution to knowledge at this aspect is to represent the partitioned cutting heat in the micro cutting process and their different behaviours compared to the conventional metal cutting. The scientific approach to modelling micro cutting application (MMCA), i.e. based on modelling-simulation combined with experimental validation, is further evaluated and validated to illustrate the overall benefits of this research investigation through micro cutting of single crystal silicon (for ultraprecision machining of large-sized infrared devices). This approach is established in light of combining all the three aspects of the above investigation on micro cutting mechanics. The research results show the approach can lead to industrial scale advantages for ultraprecision and micro cutting but driven by the scientific understanding of micro manufacturing technology. The systematic investigation on dynamic stiffness control, micro cutting force modelling, micro cutting heat and temperature and their integrated approach can contribute well to the future micro cutting applications

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency