Modeling micro-end-milling operations. Part II: tool run-out

The effect of run-out is clearly noticed in micro-end-milling operations, while the same run-out creates negligible change at the cutting force profile of conventional end-milling operations. In this paper, the cutting force characteristics of micro-end-milling operations with tool run-out are investigated. An analytical cutting force model is developed for micro-end-milling operations with tool run-out. The proposed model has a compact set of expressions to be able to estimate the cutting force characteristics very quickly compared to the numerical approaches. The cutting forces of micro-end-milling operations simulated by the proposed model had good agreement with the experimental data.     

Modeling micro-end-milling operations. Part III: influence of tool wear

The characteristics of the cutting forces were studied at different usage levels and the analytical model of the micro-end-milling operations was modified to represent the tool wear. A new expression was derived from the model to estimate the remaining tool life from experimental data. The parameters of the model are estimated by using genetic algorithms. The difference between the simulated and experimental cutting force profiles for new and worn tools was less than 8%. The remaining tool life was estimated with typically 10% error from the experimental data. Maximum error was 20%. The introduced analytical model and genetic algorithm-based parameter estimation approach is very convenient for on-line tool wear monitoring without extensive experimental study.     

Modeling micro-end-milling operations. Part I: analytical cutting force model

A new analytical cutting force model is proposed for micro-end-milling operations. The model calculates the chip thickness by considering the trajectory of the tool tip while the tool rotates and moves ahead continuously. The proposed approach allows the calculation of the cutting forces to be done accurately in typical micro-end-milling operations with very aggressively selected feed per tooth to tool radius (f_t/r) ratio. The difference of the simulated cutting forces between the proposed and conventional models can be experienced when f_t/r is larger than 0.1. The estimated cutting force profile of the proposed model had good agreement with the experimental data.     

Experimental study on machinability improvement of hardened tool steel using two dimensional vibration-assisted micro-end-milling

High strength, low thermal conductivity and high work hardening tendency of tool steel are the main factors that make its machinability difficult. In this paper, two dimensional vibration-assisted micro-end-milling (2-D VAMEM) is applied to machine the hardened tool steel (HRC 55 and HRC 58) in order to improve its machinability. The experiments are carried out to study the effects of vibration parameters on the surface roughness and the tool wear. It is found that 2-D VAMEM can improve the surface roughness and reduce the tool wear compared to traditional micro-end-milling, and larger amplitude and higher frequency are useful for the surface roughness improvement and the tool wear reduction. Therefore, 2-D VAMEM is an effective method to milling of hardened tool steel and can be applied in the manufacture of moulds and dies with improved machining efficiency, surface quality and tool life.     

Tool wear estimation in micro-machining.: Part II: neural-network-based periodic inspector for non-metals

The relationship between the cutting force characteristics and tool usage (wear) in a micro-end-milling operation was studied for two different metals. Neural-network-based usage estimation methods are proposed that use force-variation- and segmental-averaging-based encoding techniques.     

A study on initial contact detection for precision micro-mold and surface generation of vertical side walls in micromachining

The surface quality and the dimensional accuracy are important criteria for micro-mold production, specially for micro-fluidic devices. Important cutting parameters that affect the quality of vertical side walls created by the peripheral cutting edge in micro-end-milling operations were identified. Surface roughness and form error were used to define the quality of side walls on stainless steel and aluminum workpieces. An acoustic emission sensor was used to detect initial contact between a tool and a workpiece for higher dimensional accuracy where the referencing is a critical element for precision micromachining feature creation.     

Tool wear estimation in micro-machining.: Part I: tool usage–cutting force relationship

The relationship between the cutting force characteristics and tool usage (wear) in a micro-end-milling operation was studied for two different metals. Neural-network-based usage estimation methods are proposed that use force-variation- and segmental-averaging-based encoding techniques.     

Development of a tool wear-monitoring system for hard turning

This paper describes an in-depth study on the development of a system for monitoring tool wear in hard turning. Hard turning is used in the manufacturing industry as an economic alternative to grinding, but the reliability of hard turning processes is often unpredictable. One of the main factors affecting the reliability of hard turning is tool wear. Conventional wear-monitoring systems for turning operations cannot be used for monitoring tools used in hard turning because a conglomeration of phenomena, such as chip formation, tool wear and surface finish during hard turning, exhibits unique behavior not found in regular turning operations. In this study, various aspects associated with hard turning were investigated with the aim of designing an accurate tool wear-monitoring system for hard turning. The findings of the investigation showed that the best method to monitor tool wear during hard turning would be by means of force-based monitoring with an Artificial Intelligence (AI) model. The novel formulation of the proposed AI model enables it to provide an accurate solution for monitoring crater and flank wear during hard turning. The suggested wear-monitoring system is simple and flexible enough for online implementation, which will allow more reliable hard turning in industry.     

A new model for the prediction of cutting forces in micro-end-milling operations

In this work an analytical forces model for real micro-end-milling is developed by regarding the main factors which have influence on the process. The run-out or eccentric deviation of the tool path is taken into account as well as the tool deflection. A linear equation system has to be solved to obtain the forces, so it requires a low computational cost. Size effect is also considered since the chip thickness is comparable to the edge radius and therefore there is chip removal only when it is higher than a certain value. This phenomenon causes variation in the entry and exit angles of the tool in the workpiece. These factors have already been studied in conventional milling. However, since they have not been yet considered for micromilling, the validity of mechanistic models is limited. The model has been developed for two different types of side micromilling: up milling and down milling. Experimental results carried out on Steel and Aluminum show a good correlation with the model. The forces model proposed in this work can be used in a process monitoring in real time as well as in adaptive control of the process.     

Micro-end-milling of single-crystal silicon

Ductile-regime machining of silicon using micro-end-mill is almost impossible because of the brittle properties of silicon, crystal orientation effects, edge radius of the cutter and the hardness of tool materials. Micro-end-milling can potentially be used to create desired three dimensional (3D) free form surface features using the ductile machining technology for single-crystal silicon. There is still a lack of fundamental understanding of micro-end-milling of single-crystal silicon using diamond-coated tool, specifically basic understanding of material removal mechanism, cutting forces and machined surface integrity in micro-scale machining of silicon. In this paper, further research to understand the chip formation mechanism was conducted. An analysis was performed to discover how the chips are removed during the milling process. Brittle and ductile cutting regimes corresponding to machined surfaces and chips are discussed. Experiments have shown that single-crystal silicon can be ductile machined using micro-end-milling process. Forces generated when micro-end-milling single-crystal silicon are used to determine the performance of the milling process. Experimental results show that the dependence of the cutting force on the uncut chip thickness can be well described by a polynomial function order n. As cutting regime becomes more brittle, the cutting force has more complex function.     

A decision fusion algorithm for tool wear condition monitoring in drilling

Tool wear monitoring of cutting tools is important for the automation of modern manufacturing systems. In this paper, several innovative monitoring methods for on-line tool wear condition monitoring in drilling operations are presented. Drilling is one of the most widely used manufacturing operations and monitoring techniques using measurements of force signals (thrust and torque) and power signals (spindle and servo) are developed in this paper. Two methods using Hidden Markov models, as well as several other methods that directly use force and power data are used to establish the health of a drilling tool in order to avoid catastrophic failure of the drill. In order to increase the reliability of these methods, a decision fusion center algorithm (DFCA) is proposed which combines the outputs of the individual methods to make a global decision about the wear status of the drill. Experimental results demonstrate the effectiveness of the proposed monitoring methods and the DFCA.     

A decision support system for optimising the selection of parameters when planning milling operations

The paper focuses on the problem of choosing the manufacturing route in metal removal processes, a highly important element in computer aided process planning (CAPP) systems. At present, decisions concerning the cutting parameters used in metal removal operations are based on experience.The object of this work has been to design a system to help select the parameters in the cutting process in the case of milling. The algorithm is based on optimising the cost of the operation. The result is the selection of parameters that take into account all the existing restrictive factors (material, geometry, roughness, machine and tool).A wide range of parts have been evaluated, using group technology in order to choose representative cases. We compared the results of our system with the values proposed in the reference manuals. The experiment has served to determine the different relationships between the factors that have an influence on milling operations and the features which allow us to take into account the wide range of geometrical possibilities existing for all the pieces we studied.Applying our method helps to make the right decisions on the optimal parameters in mill operations as applied to the machining processes, and, in our experiments, leads to a reduction of about 35% in processing times while ensuring optimal finishing conditions.     

COATS: an Expert Module for Optimal Tool Selection

After an introduction in which the usefulness of expert systems in the field of process planning is discussed, the paper describes an expert module for fully automatized tool selection in turning operations: the module is a part of a process planning package developed by the authors. The structure of the module includes a knowledge base where production rules IF-THEN can be easily created, edited and deleted by means of an interactive program and a dictionary without being a software specialist, in order to fit the module to different industrial environments. Each rule must be written together with a “weight” evaluated upon the basis of each tool parameter and its influence on cutting process: a reference guide is given for this purpose. After consulting the rules the module proposes the best toolholder-insert couples found in properly created tool files. The “score” of each tool selected is computed by rules weight. Tests carried out have given good results some of which are here described. Then an approach to self learning is described, which allows the module to improve its knowledge base upon the basis of experience, properly evaluated by a monitoring and diagnostic program: automatic creation of new rules is so possible. The advantages of this module are flexibility and possibility to be directly connected with workshop environment.     

Seam-Tracking for Friction Stir Welded Lap Joints

This article presents a method for automatic seam-tracking in friction stir welding (FSW) of lap joints. In this method, tracking is accomplished by weaving the FSW tool back-and-forth perpendicular to the direction of travel during welding and monitoring force and torque signals. Research demonstrates the ability of this method to automatically track weld seam positions. Additionally, tensile and S-bend test result comparisons demonstrate that weaving most likely does not reduce weld quality. Finally, benefits of this weave-based method to FSW of lap joints are discussed and methods for incorporating it into existing friction stir welding control algorithms (such as axial load control) are examined.     

Automatic gap detection in friction stir butt welding operations

Friction stir welding (FSW) is a new solid-state welding technology that has been used successfully in many joining applications. A common problem that arises when welding two sheets is the presence of a gap between the sheets. Gaps may be due to improper fixturing, imprecision in the processes used to manufacture the sheets, etc. When the FSW tool encounters a gap, material can possibly escape from the processing zone and the welded part's effective cross-sectional area around the gap will decrease. Both of these effects can possibly cause an unsuitable weld. This paper develops a monitoring algorithm to detect gaps in friction stir butt welding operations in real time (i.e., during the operation). Experimental studies are conducted to determine how the process parameters (e.g., tool rotation rate and tool traverse speed) and the gap width affect the welding process; particularly, the plunge force (i.e., the force acting vertically down on the part). The proposed monitoring algorithm examines the filtered plunge force in the frequency domain to determine the presence of a gap. Several experimental studies are conducted for 2024 aluminum with a variety of process parameters and the monitoring algorithm is shown to be able to reliably detect the presence of gaps in friction stir butt welding operations for tool traverse speeds below 4.233 mm/s and gap sizes above 0.3048 mm.     

Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.     

An integrated ultrasonic sensor for monitoring gradual wear on-line during turning operations

The condition of the tool and the cutting process are essential inputs to any productivity improvements through process optimization in conventional and unmanned machining. Tool replacement and tool wear compensation strategies, which are based on prior experience and/or tool history are, in general, under performing. Currently, the methods of tool condition monitoring are either time consuming, as in the case of off-line direct measurements of the tool, or are modestly successful, as in the case of the on-line indirect measurements, such as forces or acoustic emissions. This in part is due to the lack of suitable sensors and/or exact dynamic model, which relate the indirect measurements to the actual tool condition.This paper describes a promising ultrasonic method for on-line direct measurement of gradual wear in turning operations. An integrated (transmit and receive) single ultrasonic transducer operating at a frequency of 10 MHz is placed in contact with the tool. The change in the amount of the reflected energy from the nose and the flanks of the tool can be related to the level of gradual wear and the mechanical integrity of the tool. The experimental results show that under laboratory conditions, a correlation exists between the ultrasonic measurement and gradual wear and that it is tool dependent.     

Study of milling cutting force pulsation applied to the detection of tool breakage

A study of the cutting force pulsation due to tool breakage is presented. Monitoring algorithms extracting the cutting force signal changes caused by tool breakage and further processing the extracted cutting force signal to recognize tool breakage are proposed. Theoretical studies and experimental results performed in milling operations have proven the feasibility of the algorithms proposed.     

A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations

The industrial demands for automated machining systems to increase process productivity and quality in milling of aerospace critical safety components requires advanced investigations of the monitoring techniques. This is focussed on the detection and prediction of the occurrence of process malfunctions at both of tool (e.g. wear/chipping of cutting edges) and workpiece surface integrity (e.g. material drags, laps, pluckings) levels. Acoustic emission (AE) has been employed predominantly for tool condition monitoring of continuous machining operations (e.g. turning, drilling), but relatively little attention has been paid to monitor interrupted processes such as milling and especially to detect the occurrence of possible surface anomalies.This paper reports for the first time on the possibility of using AE sensory measures for monitoring both tool and workpiece surface integrity to enable milling of “damage-free” surfaces. The research focussed on identifying advanced monitoring techniques to enable the calculation of comprehensive AE sensory measures that can be applied independently and/or in conjunction with other sensory signals (e.g. force) to respond to the following technical requirements: (i) to identify time domain patterns that are independent from the tool path; (ii) ability to “calibrate” AE sensory measures against the gradual increase of tool wear/force signals; (iii) capability to detect workpiece surface defects (anomalies) as result of high energy transfer to the machined surfaces when abusive milling is applied.Although some drawbacks exist due to the amount of data manipulation, the results show good evidence that the proposed AE sensory measures have a great potential to be used in flexible and easily implementable solutions for monitoring tool and/or workpiece surface anomalies in milling operations.     

Broaching tool design through force modelling and process simulation

Tool design is the most critical part of broaching operations as the key process parameters are permanently defined by the cutter geometry. The main objective of this study is to develop methods for increased productivity of broaching processes through improved tool design based on force simulations. A thermo-mechanical force model is applied to broaching operations with complex tool geometries. Intermediate teeth definition and generation algorithms which are essential parts of broaching process simulations are developed. Simulation results are experimentally verified, and improved tool designs are demonstrated.