Effect of membrane stress on surface roughness changes in sheet forming

Friction plays an important role in sheet metal forming (SMF) and the roughness of the surface of the sheet is a major factor that influences friction. In finite element method (FEM) models of metal forming, the roughness has usually been assumed to be constant; even though it is commonly observed that sheet drawn under tension over a tool radius results in the surface becoming shiny, indicating a major change in surface morphology. An elastic–plastic FEM model for micro-contact between a flat surface and a single roughness peak has been developed. The model was used to investigate the effect of the membrane stress in the sheet on the deformation of an artificial roughness peak. From the simulation results, the change in asperity, or deformation of the local peak, for a given nominal tool contact stress is significantly influenced by the local substrate stress. The height of the asperity decreases with increasing substrate stress and the local pressure is much higher than the nominal pressure. In addition, the local contact stress decreases with an increase in the substrate stress levels.

Effect of surface roughness of Ti, Zr, and TiZr on apatite precipitation from simulated body fluid

Some of the critical properties for a successful orthopedic or dental implant material are its biocompatibility and bioactivity. Pure titanium (Ti) and zirconium (Zr) are widely accepted as biocompatible metals, due to their non-toxicity. While the bioactivity of Ti and some Ti alloys has been extensively investigated, there is still insufficient data for Zr and titanium-zirconium (TiZr) alloys. In the present study, the bioactivity, that is, the apatite forming ability on the alkali and heat treated surfaces of Ti, Zr, and TiZr alloy in simulated body fluid (SBF), was studied. In particular, the effect of the surface roughness characteristics on the bioactivity was evaluated for the first time. The results indicate that the pretreated Ti, Zr and TiZr alloy could form apatite coating on their surfaces. It should be noted that the surface roughness also critically affected the bioactivity of these pretreated metallic samples. A surface morphology with an average roughness of approximately 0.6 microm led to the fastest apatite formation on the metal surfaces. This apatite layer on the metal surface is expected to bond to the surrounding bones directly after implantation.

Effect of surface roughness and electrostatic surface potentials on forces between dissimilar surfaces in aqueous solution

The first surface force measurements under electrochemical potential control between a metal and a ceramic surface across a liquid medium (water) are reported. Our experiments also investigate and reveal how increasing levels of surface roughness and dissimilarity between the potentials of the interacting surfaces influence the strength and range of electric double layer, van der Waals, hydration, and steric forces and how this contributes to deviations from DLVO theory at small distances within aqueous solution.

The electrochemical surface forces apparatus : the effect of surface roughness, electrostatic surface potentials, and anodic oxide growth on interaction forces, and friction between dissimilar surfaces in aqueous solutions

We present a newly designed electrochemical surface forces apparatus (EC-SFA) that allows control and measurement of surface potentials and interfacial electrochemical reactions with simultaneous measurement of normal interaction forces (with nN resolution), friction forces (with μN resolution), and distances (with Å resolution) between apposing surfaces. We describe three applications of the developed EC-SFA and discuss the wide-range of potential other applications. In particular, we describe measurements of (1) force–distance profiles between smooth and rough gold surfaces and apposing self-assembled monolayer-covered smooth mica surfaces; (2) the effective changing thickness of anodically growing oxide layers with Å-accuracy on rough and smooth surfaces; and (3) friction forces evolving at a metal–ceramic contact, all as a function of the applied electrochemical potential. Interaction forces between atomically smooth surfaces are well-described using DLVO theory and the Hogg–Healy–Fuerstenau approximation for electric double layer interactions between dissimilar surfaces, which unintuitively predicts the possibility of attractive double layer forces between dissimilar surfaces whose surface potentials have similar sign, and repulsive forces between surfaces whose surface potentials have opposite sign. Surface roughness of the gold electrodes leads to an additional exponentially repulsive force in the force–distance profiles that is qualitatively well described by an extended DLVO model that includes repulsive hydration and steric forces. Comparing the measured thickness of the anodic gold oxide layer and the charge consumed for generating this layer allowed the identification of its chemical structure as a hydrated Au(OH)3 phase formed at the gold surface at high positive potentials. The EC-SFA allows, for the first time, one to look at complex long-term transient effects of dynamic processes (e.g., relaxation times), which are also reflected in friction forces while tuning electrochemical surface potentials.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.