Thanongsak Thepsonthi

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications.

Simulation of serrated chip formation in micro-milling of titanium alloy Ti-6Al-4V using 2D elasto-viscoplastic finite element modeling

Finite element simulations have been utilized in analyses of machining process for several decades. In mechanical micromachining, finite element simulation can also be used for predicting cutting forces, minimal chip thickness, temperatures, and tool wear. The accuracy of results and the computational cost are highly dependent upon the assumptions which govern that particular chip formation problem. This study presents a comparison of two different material assumptions in finite element simulation of micro-milling titanium alloy Ti-6Al-4V. The same simulation was conducted by using the elasto-viscoplastic and the viscoplastic material assumptions. The predicted results are compared against the experimental observations. The results have shown that the material assumption has a major effect on the mechanism of chip formation and heat generation but a minor effect on the cutting force and tool wear prediction. In terms of computational cost, it was found that the simulation with the viscoplastic material assumption can reduce simulation time up to eight times that of required for a simulation with elasto-viscoplastic assumption.

Micromilling high aspect ratio features using tungsten carbide tools

This article presents experimental studies on micromilling thin walls to explore process capabilities in direct manufacturing of high aspect ratio features using tungsten carbide micro-end milling tools for two different materials: aluminium and brass. This study has been conducted in two phases. At first, the effects of micromilling parameters on the surface roughness have been investigated and most suitable machining conditions in obtaining highest surface quality have been identified. In the second phase, the effects of machining strategies have been explored in order to optimize final quality of the thin walls in terms of straightness of the machined thin walls, uniformity of wall thickness and burr presence. As a result of this experimental study, optimized machining parameters and strategies are presented. In the case of micromilling brass (CuZn36Pb3), a down-milling cutting direction with a Z-step milling strategy at a spindle speed of 35,000 r min−1, an axial depth of cut of 150 µm and a feed rate of 150 mm min−1 provided the best overall thin-wall quality. In the case of micromilling aluminium (Al6061-T4), a down-milling cutting direction with a ramp milling strategy, a spindle speed of 25,000 r min−1, an axial depth of cut of 150 µm and a feed rate of 200 mm min−1 yielded the best results.

Micro milling of titanium alloy Ti-6Al-4V: 3-D finite element modeling for prediction of chip flow and burr formation

Cutting process of titanium alloy Ti-6Al-4V is considered difficult due to chemical affinity between tool and work material, adhesion, built-up edge and burr formation, and tool wear resulting in loss of productivity. Three dimensional (3-D) chip flow together with local field variables such as temperature, elastic/plastic strain, strain-rate and velocity in the shear zones during micro milling process can be predicted using continuum-mechanics based 3-D Finite Element (FE) modelling and simulation of elastic/viscoplastic work material deformations. This paper provides much needed process insight for chip flow, built-up edge and burr formation by using modeling work with experimental validation. Scanning electron microscopic (SEM) observation of the 3-D chip morphology and burrs demonstrate ductile fractured surfaces together with localized instability and failure behaviors. FE simulations are utilized to investigate the effects of micro milling operation i.e. up and down milling and tool edge radius on 3-D chip flow, built-up edge, and 3-D burr formation. Simulated results are compared with measurements of chip morphology, shape, and dimensions together with tool edge condition of built-up edge and chip adhesion yielding to good agreements.