The importance of the post-processing is increasing to remove the supporter afterward the additive manufacture process. The machining, known as the material removal process, achieve the high efficiency and rapid process as compared with the others techniques. This paper experimentally investigated the tool wear during the milling operations of the additive manufactured workpieces for the post-processing. The XRD analysis resulted that Inconel 718 powder used in the additive manufacturing has crystal structure FCC, which homologies the chemical compositions in wrought Inconel 718. The selective laser melting process had built the additive manufactured samples with two different orientations. Wrought Inconel 718 was the lowest hardness among the workpieces, whereas the severe tool wear was observed during the milling operation of wrought Inconel 718. The defects as the pores and cavities in the additive manufactured parts lead the low tool wears, even though the high hardness on the surfaces of the SLM Inconel 718. Further, the built orientation dominated the re-melted zone in the SLM parts, the contact between the tool and re-melted zone controlled the tool wears. Therefore, it should consider the built orientations to apply the machining as the post-processes.

This paper presents a non-contact, convenient, efficient cutting tool wear monitoring technique of dicing wheel based on a knife-edge diffraction interferometry. Dicing is a standard technology for fabricating components of micro-electromechanical systems, and the wear of dicing wheels may influence the components’ quality with respect to cutting-surface quality and subsurface damage. Based on the edge diffraction principle that utilizes interference of transmitted wave and a diffracted wave at the wheel end, the diffraction patterns according to dicing wheel conditions were scanned, and cross-correlation was used to extract attrition and abrasive wear from the measured diffraction patterns. Attrition and abrasive wear were related with lag and similarity coefficients of cross-correlation, respectively. This measurement technique can be used for in-process monitoring of wheel conditions or wheel radius compensation and can be included in dicing parameter optimization process.

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of Variance (ANOVA) was also used to determine factors significant to the energy consumption by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies (0.1 rad/sec) highlighted by DDS methodology.

To improve the surface quality of the copper and reduce the diamond tool wear, a prediction model is established experimentally for the relationship between surface roughness and machining parameters. Based on the processing principle of flycutting machining, the prediction model for surface roughness is set up by response surface methodology. Then, a machining experiment for the copper is conducted under different cutting parameters designed by Taguchi method and the surface roughness is tested by 4D technology dynamic laser interferometer. After that, the prediction model is obtained by analyzing the experimental data, and the accuracy of the model is verified by analysis of variance (ANOVA), R 2 value and residual analysis. Furthermore, the effect of cutting parameters upon the surface roughness is analyzed. Finally, validation tests are conducted to verify the model. Experimental results demonstrate that the prediction model is adequate at 95% confidence level. The output of prediction model helps to select cutting parameters to reduce surface roughness which ensures surface quality in ultra-precision fly cutting machining.

VESPER ( V isual E xploration of S imilarity and PER formance) is a visual analytics system for exploring similarity metrics and performance metrics derived from computer-aided design (CAD) repositories. It consists of (1) a data processing module that allows analysts to input custom similarity metrics and performance metrics, (2) a visualization module that facilitates navigation of the design spaces through coordinated, interactive visualizations, and (3) a report generation module that allows analysts to export lifecycle data of selected repository items as well as the input metrics for further external validation. In this paper, we discuss the need, design rationale, and implementation details for VESPER. We then apply VESPER to (1) sustainability-focused exploration of parts, and (2) exploration of tool wear and surface roughness in machined parts.

Over time, robots degrade because of age and wear, leading to decreased reliability and increasing potential for faults and failures; this negatively impacts robot availability. Economic factors motivate facilities and factories to improve maintenance operations to monitor robot degradation and detect faults and failures, especially to eliminate unexpected shutdowns. Since robot systems are complex, with sub-systems and components, it is challenging to determine these constituent elements’ specific influence on the overall system performance. The development of monitoring, diagnostic, and prognostic technologies (collectively known as Prognostics and Health Management (PHM)), can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. This paper presents the strategy of integrating top level and component level PHM to detect robot performance degradation (including robot tool center accuracy degradation), supported by the development of a four-layer sensing and analysis structure. The top level PHM can quickly detect robot tool center accuracy degradation through advanced sensing and test methods developed at the National Institute of Standards and Technology (NIST). The component level PHM supports deep data analysis for root cause diagnostics and prognostics. A reference data set is collected and analyzed using the integration of top level PHM and component level PHM to understand the influence of temperature, speed, and payload on robot’s accuracy degradation.

Shape Memory Alloys are smart materials that tend to remember and return to its original shape when subjected to deformation. These materials find numerous applications in robotics, automotive and biomedical industries. Micromachining of SMAs is often a considerable challenge using conventional machining processes. Micro-Electrical Discharge Machining is a combination of thermal and electrical processes, which can machine any electrically conductive material at micron scale independent of its hardness. It employs dielectric medium such as hydrocarbon oils, deionized water, and kerosene. Using liquid dielectrics has adverse effects on the machined surface causing cracking, white layer deposition, and irregular surface finish. These limitations can be minimized by using a dry dielectric medium such as air or nitrogen gas. This research involves the experimental study of micromachining of Shape Memory Alloys using dry Micro-Electrical Discharge Machining process. The study considers the effect of critical process parameters including discharge voltage and discharge current on the material removal rate and the tool wear rate. A comparison study is performed between the Micro-Electrical Discharge Machining process with using the liquid as well as air as the dielectric medium. In this study, microcavities are successfully machined on shape memory alloys using dry Micro-Electrical Discharge Machining process. The study found that the dry Micro-Electrical Discharge Machining produces a comparatively better surface finish, has lower tool wear and lesser material removal rate compared to the process using the liquid as the dielectric medium. The results of this research could extend the industrial applications of Micro Electrical Discharge Machining processes.

This paper aims to investigate the effectiveness of super-hard ceramic coatings by evaluating tool wear when drilling carbon fiber reinforced plastics (CFRP) composite. The drilling experiments of CFRP are conducted with diamond-like coating (DLC) coated, AlMgB 14 (BAM) coated, AlCrSiN coated, and uncoated tungsten carbide drills. Tool wear evolution is measured qualitatively as well as quantitatively using the scanning electron and confocal laser scanning microscopes. Both DLC and BAM coatings failed when drilling CFRP only within 10 holes while AlCrSiN coating did not fail. Failure mechanisms of each coating when drilling CFRP are discussed.

Fused silica is difficult to machine through conventional machining, mainly due to its high brittleness and strength, low fracture toughness and poor plastic deformation. This study was attempted to explore the machinability of fused silica with laser-assisted machining by heating workpiece through a pulse CO 2 laser beam. During the LAM of fused silica, the bonding and wavelike texture on the machined surface indicated the behavior change of material deformation by the local heating in front of the cutting tool. The semi-continuous chips were obtained as an evidence of material removal mechanism which was a hybrid of quasi plastic deformation and brittle fracture. Moreover, the machinability of fused silica was evaluated. The experimental results demonstrated that considerable improvement in the machinability of fused silica was achieved such as better surface roughness, smaller cutting force as well as lower tool wear.

This paper evaluates the performances of dry, minimum quantity lubrication (MQL) and MQL with nanofluid in turning the most common titanium (Ti) alloy, Ti-6Al-4V, in a solution treated and aged (STA) microstructure. In particular, the nanofluid evaluated here is vegetable oil (rapeseed) mixed with small concentrations of exfoliated graphite nanoplatelets (xGnP). The focus of this paper is on turning process because it poses a challenging condition to apply oil droplets directly onto the tribological surfaces of a cutting tool due to the continuous engagement of tool and work material. A series of turning experiments was conducted with uncoated carbide inserts while measuring the cutting forces with the dynamometer under various conditions to determine its effectiveness and optimal MQL condition in turning. The worn inserts are retrieved to measure flank and crater wear using confocal microscopy. This preliminary experimental result shows that the use of MQL and nanofluid is effective in improving the machinability of Ti alloys in turning processes.

Minimum Quantity Lubrication or MQL is an increasingly used technique for metal cutting operations and it has become an attractive alternative for machining parts at big scale production. However, fully lubricated conditions are still in use for machining special materials so that surface finish, tool wear, and temperature distribution levels remain at optimum levels. On the other hand, dry condition machining is in use as well although with some restraint due to issues with material burr, surface roughness, and tool wear. The main purpose of this work is to analyze the effects of cutting fluid flow rate, its application mechanisms, and cutting speed on surface roughness and establish the lowest possible cutting fluid flow rate that yields to minimum surface roughness (Ra). To achieve the objective, a set of experiments was performed using a Computer Numerical Control (CNC) lathe instrumented with a Kistler 9121 dynamometer and a customized cutting fluid application system to obtain coefficients of friction and cutting forces. Finally, a previously 2D finite element analysis (FEA) simulation from Akbar et al. [1] is applied and compared to experimental results to find out if the cutting force can be predicted. A first regression model that correlates cutting force and surface roughness is posed, so that FEA simulation can be implemented to predict the final surface roughness. AISI 4140 machinery steel in annealed condition is used to carry out the simulated and experimental work.

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

Increasing governmental fuel economy requirements drives automakers to increase the fuel economy of their fleets. One of the methods for improving fuel economy is lightweighting vehicles through the use of materials with high strength to weight ratios. Some of these new metals entering the automotive sector are difficult to machine and cause drastically reduced tool life and increase machining cost. It has been shown that electricity has the ability to reduce cutting force during orthogonal cutting and turning. In this research, a design of experiments study on an electrically assisted drilling operation is conducted to determine the impact and interaction between the following input parameters: applied electric current, feedrate, spindle speed, and number of holes cut. These variables used to determine impact and interaction on the following output variables: flank wear, axial cutting force, and temperature evolution. A 2D finite volume method model is used to predict drilling temperature during the process, and is used to aid in predicting axial force. It is found that electric current can reduce cutting force by 10% for 1008 steel at the cost of increased temperature, however, arcing at initial contact causes increased tool wear at higher current inputs.

BK7/K9 glass is regarded as a difficult-to-machine material due to its high hardness and high brittleness properties as well as high tool wear rate during machining. Facing to these challenges, an efficient and effective rotary ultrasonic machining (RUM) process, consisting of grinding process and ultrasonic machining process, was provided to process BK7/K9 glass. In this investigation, the effects of ultrasonic power on cutting forces, torque, and edge chipping of surface grinding in RUM of BK7/K9 glass were studied. Results showed that, by introducing ultrasonic vibration to surface grinding process, both cutting forces in feeding direction and in axial direction as well as torque values were reduced. The higher the ultrasonic power was, the lower the forces and torque values would be. Edge chipping, which was detrimental to the qualities of machined slots and would cause high machining cost, was significant reduced with the help of ultrasonic vibration.

With the increasing demands in the automotive industry for passenger safety and higher structural strength and stiffness, the automotive industry is using more advanced high strength steels. The ability to reduce tool wear and drilling forces in post forming drilling of high strength steel parts is of high importance to the automotive industry. Electrically assisted drilling is a process in which electric current is passed through the drill bit to the workpiece resulting in local softening, and allowing for a reduction in cutting forces and potential increase in tool life. In this paper, tungsten carbide (WC)-tipped drill bits are used to study the effect of varying electrical current on 1500 Usibor ® steel work pieces. The effects of current on the drilling process of high strength steel are investigated in this research by studying the maximum temperature during drilling, the dependence of chip formation, tool wear and the axial force during the drilling operation. It was found that the magnitude of current passed through the workpiece directly influences the axial force that the tool experiences, and thus the tool wear. This effect is modeled through Joule heating, leading to elevated temperature and thermal softening.

Machining is the manufacturing process, capable of producing required shape and size by material removal. In recent times industries are striving to enhance the performance of machining processes. One of the problem associated with machining is the amount of heat generation as a result of friction between tool and workpiece. Heat generated may affect the quality of machined surface and tool wear. In order to control it, cutting fluid is applied in large quantity. The problem arises with the use of cutting fluid is its effect on worker’s health and environment. The present investigation is an attempt to explore the use the solid lubricants in machining as an alternative to cutting fluid. The work involves development of minimum quantity solid lubrication set up. Turning experiments has been performed by applying solid lubricants mixed with cutting fluid in minimum quantity. The performance of minimum quantity solid lubrication has been assessed in form of obtained surface finish, power consumption and tool wear during turning. Experimental findings discovered the superiority of minimum quantity solid lubrication over conventional cutting fluid and can be considered as cost effective and sustainable lubrication method.

Continuously Variable Crown (CVC) shifting mechanisms represent a control technology with wide range of capability to influence the thickness profile and flatness (shape) of metal strip and sheet in rolling-type manufacturing processes. Further, because of the efficiency and extensive control capability to operate on thin-gauge, high-strength ferrous alloys, the 6-high mill with CVC profiles machined onto the intermediate rolls (IR) represents a popular mill configuration. This is because of the large control range for the strip thickness profile and flatness, which results from lateral shifting of the CVC intermediate rolls. However, together with this efficiency and capability comes very complex contact behaviors between the rolls and strip, including highly non-linear contact force distribution, loss of contact, asymmetric roll wear, unwanted strip wedge profiles, and the need to apply corrective roll tilting. Therefore, for most effective industry use of 6-high mills with intermediate roll CVC shifting, a rapid and accurate mathematical rolling model is needed to predict and account for these complex contact behaviors. This paper introduces an efficient roll-stack computational model capable of simulating such rolling mills under steady-state conditions. The model formulation applies the simplified mixed finite element method (SM-FEM), which is adapted to simulate asymmetric 6-high CVC mill contact behaviors. Results for a specific case study compare favorably to those obtained from a large-scale commercial finite element simulation, yet require a small fraction of the associated computational time and effort.

Cutting temperature plays an important role in micro-scale cutting process because the dimension of the micro-milling cutter is relatively small and the wear of micro-milling cutter is sensitive to temperature. Considering the sidewall of a groove is formed by main cutting edge of the tool, and the bottom of a groove is formed by tool tip and the edge on the end of the tool. Therefore, effects of tool nose corner radius and main cutting edge radius on cutting temperature in micro-milling process cannot be ignored. However, few studies have been conducted on this issue. The effects of tool nose corner radius and main cutting edge radius on cutting temperature is investigated. A three-dimensional micro-milling Inconel718 model is established by using the software DEFORM3D. And the influence of tool nose corner radius and main cutting edge radius on the size and distribution of cutting temperature are studied by numerical simulation, which is verified by experiments. The work provide reference for the control of the size and distribution of the cutting temperature during micro-milling process.

The deviations of cylinder bore dimensional accuracy have tremendous influence on engine performances including friction power loss, vibration, leak tightness between piston ring and cylinder wall, and abrasive resistance. Many researches were devoted to capturing cylinder dimensional accuracies by honing using analytical, experimental and simulation methods. These researches investigated the topography and roughness of the honed surface, the relationship between the process parameters and the dimensional accuracies. However, most researches focused on macro-scale dimensional accuracy and micro-scale surface texture respectively. To overcome the limitation, a multi-scale model for cylinder bore honing is proposed to predict the dimensional accuracy and surface texture of cylinder bore at macro-scale and micro-scale simultaneously. The model integrates the microscale factors of the honing stone abrasives distribution characteristics, abrasive wear process, previous cylinder surface topography, and macro-scale factors of cylinder geometry and honing head motion trajectory. A Force matching method is adopted to determine the feed depth of cylinder honing process. Thus the model can predict the roundness, cylindricity, roughness and Abbott-Firestone curve of the honed cylinder bore at multi-scale levels. Simulation results show that material removal distribution is closely related to cylinder bore initial shape deviations. The deviations with long wavelengths cannot be eliminated by the sequential honing.

Surface integrity of high performance components has a profound influence on the final performance. Therefore, surface integrity is a key point for realizing high performance manufacturing by which manufacture processes and parameters can be pre-selected according to a required functional performance of components, i.e., solving inverse problem of manufacturing, as long as correlations could be established respectively for between processes and surface integrity, and between surface integrity and performance. However, in practice it is still difficult in correlating processes to performance through surface integrity, due to the material and geometry constraints hindering achievability of a desired surface integrity during conventional manufacturing as well as the complex influence of multiple surface integrity parameters on a final performance. In this study, thermally sprayed WC-10Ni coatings onto stainless steel using high velocity oxy-fuel (HVOF) spraying process are investigated to identify the surface integrity predominantly determining the water-lubricated wear performance of coated steel, and then to correlate it to process parameters. The controllable surface integrity facilitates identifying responsible surface integrity parameters for a required high performance, and subsequently deriving necessary process parameters for achieving the desired responsible surface integrity. Specifically, HVOF process parameters are adjusted by changing the oxygen-to-fuel (O/F) ratio to control thermal and mechanical processing loads, i.e. temperature of heated in-flight spraying powders and impact velocity of the molten splats onto stainless steel to form the coatings. Surface features including porosity and phase structure, and surface characteristics including hardness, elastic modulus, and fracture toughness were studied with respect to the wear performance. The porosity and WC phase composition of coatings are identified responsible for the wear performance, as two essential surface integrity parameters that in turn greatly affect the surface characteristics including coating hardness, elastic modulus and fracture toughness. Consequently, the process parameter O/F is feasibly correlated to wear resistance through the responsible surface integrity parameters, as elucidating the coating formation mechanism of influence of particle velocity and temperature on the coating porosity and WC decomposition.