A J Mian
Bio: A J Mian is an academic researcher from University of Manchester. The author has contributed to research in topics: Acoustic emission & Machining. The author has an hindex of 5, co-authored 6 publications receiving 246 citations.
TL;DR: In this article, a micro-milling test was conducted on Inconel 718 nickel alloy using 500μm diameter carbide end mill and the experimental design was based on an L9 Taguchi orthogonal array.
Abstract: In micro-machining the so called “size effect” is identified as critical in defining process performance. Size effects refers to the phenomenon whereby the reduction of the undeformed chip thickness to levels below the cutting edge radius, or gain size of the workpiece material begins to influence workpiece material deformation mechanisms, chip formation and flow. However, there is no clear agreement on factors that drive this size effect phenomenon. To explore the significance of cutting variables on the size effect, micro-milling tests were conducted on Inconel 718 nickel alloy using 500 μm diameter carbide end mill. The experimental design was based on an L9 Taguchi orthogonal array. Fast Fourier transform (FFT) and wavelet transform (WT) were applied to acoustic emission (AE) signals to identify frequency/energy bands and hence size effect specific process mechanism. The dominant cutting parameters for size effect characteristics were determined by analysis of variance (ANOVA). These findings show that despite most literature focussing on chip thickness as the dominant parameter on size effect, the cutting velocity is also a dominant factor. This suggests that manipulating the cutting speed is also an effective strategy in reducing burr thickness, optimising surface finish and in breaking the lower limit of micro-machining.
TL;DR: In this article, surface roughness, workpiece microstructure and burr size for micro-machined parts as well as tool wear were examined over a range of feed rates.
Abstract: In the mechanical micro-machining of multiphase materials, the cutting process is undertaken at a length scale where material heterogeneity has to be considered. This has led to increasing interest in optimising the process parameters for micro-machining of such materials. In this study the micro-machinability of two steels, a predominantly ferrite material (AISI 1005) and a near-balanced ferrite/pearlite microstructure (AISI 1045) was studied. Workpiece sample deformation properties were characterised by nano-indentation testing. Additionally, metallographic grain size evaluation was undertaken for the workpiece microstructures. Surface roughness, workpiece microstructure and burr size for micro-machined parts as well as tool wear were examined over a range of feed rates. The results suggest that for micro-machined parts, differential elastic recovery between phases leads to higher surface roughness when the surface quality of micro-machined multiphase phase material is compared to that of single phase material. On the other hand, for single phase predominantly ferritic materials, reducing burr size and tool wear are major challenges. Thus, the paper elucidates on material property effects on surface and workpiece edge quality during micro-milling.
TL;DR: In this paper, the effects of different workpiece materials on chip formation and associated mechanisms in microcutting were investigated using wavelet transformation technique to decompose acoustic emission (AE) signals generated from orthogonal micromilling of different materials.
Abstract: This work investigated the effects of different workpiece materials on chip formation and associated mechanisms in microcutting. The wavelet transformation technique was used to decompose acoustic emission (AE) signals generated from orthogonal micromilling of different workpiece materials. This allowed studying energy levels corresponding to deformation mechanisms. Resulting chip forms were characterised. The results indicated that the computed energies of decomposed frequency bands can be positivity correlated with chip morphology. The work provides significant and new knowledge on the utility and importance of AE signals in characterising chip formation in micromachining. Understanding chip formation mechanisms is important in managing the size effect in micromachining.
••01 Apr 2009
TL;DR: In this article, the effect of chip thickness, tool edge radius, and workpiece grain size on the specific cutting force, burr size, surface finish, and tool wear was investigated.
Abstract: The high demand of miniaturized components, coupled with geometric and material range limitations of traditional lithographic techniques has generated a strong interest in micromechanical machining. In micromachining the so-called size effect is a dominant factor. This is attributed to the fact that the unit or physical size of the material to be removed can be of the same order of magnitude as the tool edge radius or grain size. This paper explores the micro-machinability of multi-phase ferrite—pearlite steel that has a relatively large average grain size (10 μm). The investigation and cutting tests examined the effect of undeformed chip thickness, tool edge radius, and workpiece grain size on the specific cutting force, burr size, surface finish, and tool wear. The work clearly shows that micro tool edge radius and workpiece material grain size are valuable inputs in determining micromilling conditions that ensure the best surface finish and reduced burr size. Cutting conditions recommendations ...
••24 Oct 2011
TL;DR: In this article, the authors proposed a methodology to determine the value of minimum chip thickness by analysing acoustic emission (AE) signals generated in orthogonal machining experiments conducted in micro-milling.
Abstract: In micro-machining, determination of the minimum chip thickness is of paramount importance, as features having dimensions below this threshold cannot be produced by the process. This study proposes a methodology to determine the value of minimum chip thickness by analysing acoustic emission (AE) signals generated in orthogonal machining experiments conducted in micro-milling. Cutting trials were performed on workpiece materials ranging from non-ferrous (copper and aluminium), ferrous (single- and multiphase steel) to difficult-to-cut (titanium and nickel) alloys. The characteristics of AErms signals and chip morphology were studied for conditions when the tool was rubbing the workpiece. This provided a foundation to contrast AE signals captured at higher feed rates. This study enabled the identification of threshold conditions for the occurrence of minimum chip thickness. The values of minimum chip thickness predicted by this new approach compare reasonably well with the published literature.
TL;DR: In this article, the effects of spindle speed, feed per tooth and depth of cut on tool wear, force and surface roughness were investigated using first-order models with interaction.
Abstract: This study was carried out to understand micro-milling of aluminum material with ball nose end mill and consisted of four stages: experimental work, modelling, mono and multi objective optimization. In the first stage (experimental work), micro-milling experiments were carried out using Taguchi method. The effects of spindle speed, feed per tooth and depth of cut on tool wear, force and surface roughness were investigated. Cutting tools and workpiece surfaces were also inspected via scanning electron microscope. Adhesion and abrasion wear mechanisms during micro-milling of aluminum were observed. Workpiece surfaces had the accumulations of plastically deformed workpiece material due to the high ductility of aluminum. In the second stage (modelling), all data gathered in the experimental works were utilized to formulate first-order models with interaction. These first-order models with interaction could be used to predict responses in micro-milling of aluminum with a minor error. In the third stage (mono-objective optimization), responses were used alone in optimization study as an objective function. To minimize all responses, Taguchi’s signal to noise ratio was used. The effect of control factors on responses was determined by analysis of variance. In the fourth stage (multi objective optimization), responses were optimized simultaneously using grey relational analysis.
TL;DR: In this paper, the authors present the principal aspects related to this technology, with emphasis on the work material requirements, tool materials and geometry, cutting forces and temperature, quality of the finished product, process modelling and monitoring and machine tool requirements.
Abstract: The trend towards miniaturization has increased dramatically over the last decade, especially within the fields concerned with bioengineering, microelectronics, and aerospace. Micromilling is among the principal manufacturing processes which have allowed the development of components possessing micrometric dimensions, being used to the manufacture of both forming tools and the final product. The aim of this work is to present the principal aspects related to this technology, with emphasis on the work material requirements, tool materials and geometry, cutting forces and temperature, quality of the finished product, process modelling and monitoring and machine tool requirements. It can be noticed that size effect possesses a relevant role with regard to the selection of both work material (grain size) and tooling (edge radius). Low forces and temperature are recorded during micromilling, however, the specific cutting force may reach high values because of the ploughing effect observed as the uncut chip thickness is reduced. Finally, burr formation is the principal concern with regard to the quality of the finished part.
TL;DR: In this article, the effect of cubic boron nitride (cBN) coating on micro-machining of Ti-6Al-4V titanium alloy was investigated.
Abstract: Micro-milling process is a direct and flexible fabrication method in producing functional three dimensional micro-products. The advance of micro-milling process ultimately depends on the development of micro cutting tools since it is a tool-based process. Therefore, in this study an attempt to improve the performance of carbide micro-end mills by applying cubic boron nitride (cBN) coating was carried out. Experiments and finite element method (FEM) based simulations were used to study the effect of cBN coated tool in micro-machining of Ti-6Al-4V titanium alloy. The experiments were conducted to compare the performance of cBN coated and uncoated micro-end mills in terms of surface roughness, burr formation and tool wear. FE simulations were employed to investigate chip formation process in micro-milling to reveal the effects of cBN coated micro-end mills with increased edge radius in terms of cutting force generation, tool temperature and contact pressure, sliding velocity and hence tool wear rate. The simulation results were further utilized for estimating tool life using a sliding wear rate model and compared with experiments. This study clearly showed that the cBN coated carbide tool outperformed the uncoated carbide tool in generation of tool wear and cutting temperature.
TL;DR: In this article, a multi-objective particle swarm optimization method was used to find the optimal process parameters which minimize the surface roughness and burr formation in micro-end milling.
Abstract: Micro-end milling is one of the promising methods for rapid fabrication of features with 3D complex shapes. However, controlling the micro-end milling process to obtain the desired results is much harder compared to that of macro-end milling due to the size effect and uncontrollable factors. The problem is much pronounced when workpiece material is a difficult-to-process material such as titanium-based alloys which are widely used as material of choice for aircraft structures, turbine blades, and medical implants. In order to find the optimal process parameters which minimize the surface roughness and burr formation, experiments were conducted and models obtained with statistically based methods utilized in multi-objective particle swarm optimization to identify optimum process parameters. The results show that the average surface roughness can be minimized while burr formation is reduced concurrently.
TL;DR: Thorough literature review of various modern machining processes is presented and may become the ready information at one place and it may be very useful to the subsequent researchers to decide their direction of research.
Abstract: Thorough literature review of various modern machining processes is presented in this paper. The main focus is kept on the optimization aspects of various parameters of the modern machining processes and hence only such research works are included in this work in which the use of advanced optimization techniques were involved. The review period considered is from the year 2006 to 2012. Various modern machining processes considered in this work are electric discharge machining, abrasive jet machining, ultrasonic machining, electrochemical machining, laser beam machining, micro-machining, nano-finishing and various hybrid and modified versions of these processes. The review work on such a large scale was not attempted earlier by considering many processes at a time, and hence, this review work may become the ready information at one place and it may be very useful to the subsequent researchers to decide their direction of research.