Dimensional Analysis of Thermal Fields Surrounding Friction Stir Welding Process

A dimensionless correlation is developed based on Buckingham’s Pi-Theorem to estimate the temperature fields generated by the movement of a tool during the Friction Stir Welding of an aluminum alloy (6061-T6). Symmetrical thermocouple measurements are taken during a statistically designed experiment using different factor levels (RPM, Traverse, etc). Analytical comparison (using multivariate ANOVA) validates the predicted dimensionless correlation including the often-reported difference between the advancing versus retreating side of the Friction Stir Tool.

Process Performance of Silicon Thin-Film Transfer Using Laser Micro-Transfer Printing

Micro-transfer printing is rapidly emerging as an effective pathway for heterogeneous materials integration. The process transfers pre-fabricated micro- and nano-scale structures, referred to as “ink,” from growth donor substrates to functional receiving substrates. As a non-contact pattern transfer method, Laser Micro-Transfer Printing (LMTP) has been introduced to enhance the capabilities of transfer printing technology to be independent of the receiving substrate material, geometry, and preparation. Using micro fabricated square silicon as inks and polydimethylsiloxane (PDMS) as the stamp material. The previous work on the LMTP process focused on experimentally characterizing and modeling the effects of transferred inks’ sizes and thicknesses, and laser beam powers on the laser-driven delamination process mechanism. In this paper, several studies are conducted to understand the effects of other process parameters such as stamp post dimensions (size and height), PDMS formulation for the stamp, ink-stamp alignment, and the shape of the transferred silicon inks on the LMTP performance and mechanism. The studies are supported by both experimental data for the laser pulse duration required to initiate the delamination, and thermo-mechanical FEA model predictions of the energy stored at the interface’s edges to release the ink (Energy Release Rate (ERR)), stress levels at the delamination crack tip (Stress Intensity Factors (SIFs)), and interfacial temperature. This study, along with previous studies, should help LMTP users to understand the effects of the process parameters on the process performance so as to select optimal operation conditions.

Tribological Investigation of Copper (Cu) and Copper Oxide (CuO) Nanoparticles Based Nanolubricants for Machine Tool Slideways

The present study intends to evaluate the tribological characteristics of Copper (Cu) and Copper oxide (CuO) based nanolubricant for its use in machine tool slideways. Different sizes of copper and copper oxide particles were chosen and physical characterisation were carried out using scanning electron microscope (SEM) and transmission electron microscope (TEM). The nanolubricants were prepared by adding various proportions (0.1%, 0.25%, 0.4% wt) of the particles in Polyalphaolefin (PAO) base oil with lecithin and oleic acid surfactants. Friction and stick-slip characteristics of the nanolubricants were assessed using pin-on-block reciprocating friction monitor simulating the actual loading conditions prevailing in machine tool slideways. Studies were also conducted under elevated temperatures to ascertain the performance of particles at higher temperatures. Extreme pressure properties of the lubricants were studied using Four Ball Tester. The results of the experiments were compared with ISO VG 32 oil, a conventional mineral lubricant meant for machine tool slideways and it was found that the tribological properties nanolubricants using both the nanoparticles were considerably better. The coefficient of friction found to be decreased by 2.5% and 17.5% for copper particles with 0.1% weight composition in ambient temperature and elevated temperature respectively. Whereas for copper oxide particles with 0.1% weight composition a reduction of 14.25% and 10% were obtained.

Nanoscale Thermal Transport in Plasmonic Nanofocusing Structure With Strong Nonlocality

Nanoscale optical energy concentration and focusing is crucial for many high-throughput nanomanufacturing applications, such as material processing, imaging and lithography. The use of surface plasmons has resulted in the rapid development of nanofocusing devices and techniques at spatial confinements as good as a few nanometers associated with strong nonlocal plasmonic response. However, operations of these plasmonic nanofocusing structures usually require extremely high optical energy density at nanoscale, which leads to intense structure heating and causes unreliable device functions and short device lifetimes. In many plasmonic applications, optical heating has become a very important issue, which has not been investigated intensively yet. In these structures, the ballistic transport and interface scattering of the energy carriers both become significant because the characteristic lengths of the devices are comparable to or smaller than the mean free paths of the carriers. A comprehensive model is desired to understand the heat generation and transport inside the plasmonic nanofocusing structures. This work studied the electromagnetic and optothermal responses of plasmonic nanofocusing nanostructures. At the nanometer length scale, the local optical response and diffusive thermal model are no longer sufficient to describe the device optothermal response because of the strong interactions between energy carriers and the ballistic nature of carriers. Here, we used the hydrodynamic Drude model to consider the nonlocality of plasmonic response and calculate the heat generation inside the metallic nanostructures. Starting from Boltzmann transport equation, we derived the energy transport equations for both electron and phonon systems under the relaxation-time approximations. The obtained multi-carrier ballistic-diffusive model was used to study the non-equilibrium heat transports inside the structures. We assume that the ballistic electrons originate from boundaries and the electron-photon couplings inside the structure, experiencing out-scattering only in the material. The optically-generated “hot” electrons are considered as ballistic and are treated separately from the “ordinary” electrons which are in local thermal equilibrium and have significantly lower energies. Meanwhile, the electron-phonon couplings are considered under the non-equilibrium conditions between the electron and phonon systems. Using our model, we further investigated the transient optothermal responses of a one-dimensional (1D) plasmonic nanofocusing structure. In comparison to the diffusive transport description, our multi-carrier ballistic-diffusive model can more accurately describe the optothermal responses of the plasmonic nanofocusing structures which are crucial for predicting the performance and the lifetime of the plasmonic nanofocusing devices.

Design Method for Lattice-Skin Structure Fabricated by Additive Manufacturing

Parts with complex geometry structure can be produced by AM without significant increase of fabrication time and cost. One application of AM technology is to fabricate customized lattice-skin structure which can enhance performance of products with less material and less weight. However, most of traditional design methods only focus on design at macro-level with solid structure. Thus, a design method which can generate customized lattice-skin structure for performance improvement and functionality integration is urgently needed. In this paper, a novel design method for lattice-skin structure is proposed. In this design method, FSs and FVs are firstly generated according to FRs. Then, initial design space is created by filling FVs and FSs with selected lattice topology and skin, respectively. In parallel to the second step, initial parameters of lattice-skin structure are calculated based on FRs. Finally, TO method is used to optimize parameter distribution of lattice structure with the help of mapping function between TO’s result and lattice parameters. The design method proposed in this paper is proven to be efficient with case study and provides an important foundation for wide adoption of AM technologies in industry.

Axial Force Reduction in Friction Stir Welding of AA6061-T6 at Right Angle

Invented in 1991, friction stir welding (FSW) is a new solid state joining technique. This process has many advantages over fusion welding techniques including absence of filler material, shielding gas, fumes and intensive light, solid state joining, better microstructure, better strength and fatigue life, and etc. The difficulty with FSW is in the high forces involved especially in axial direction which requires use of robust fixturing and very stiff FSW machines. Reduction of FSW force would simplify implementation of the process on less stiff CNC machines and industrial robots. In this paper axial welding force reduction is investigated by use of tool design and welding parameters in FSW of 3.07 mm thick AA6061-T6 sheets at right angle. Attempt is made to reduce the required axial force while having acceptable ultimate tensile strength (UTS). It is found that shoulder working diameter and shoulder angle are the most important parameters in the axial force determination yet pin angle has minor effect. According to the developed artificial neural network (ANN) model, proper selection of shoulder diameter and angle can lead to approximately 40% force reduction with acceptable UTS. Regions of tool design and welding parameters are found which result in reduced axial force along with acceptable UTS.

Towards the Development of an Ontology-Based Product Requirement Model

Determination of appropriate product design criteria depends on the requirement specifications as well as their interrelations with other life cycle process information. Although the representation of the design requirements for creating a desirable product/part is a necessity, most of the time it has been carried out by the designer based on his/her own experiences. Such requirement handling processes need deep understanding of the product design, materials, manufacturing, working environments, finance and regulations, and are normally cost ineffective and error prone. Furthermore, very little attentions have been paid to the development of a structured requirement modeling for future intelligent applications. After a systematic study on the categorization of product design related information and requirements, this paper proposes an ontological framework for representing information and knowledge about the engineering product requirements. To demonstrate the use of the proposed requirement model and its role in the requirement management process, a case study related to an automotive brake rotor has been discussed in detail.

Localized Decision-Making for Materials Transportation Systems Subject to Stochastic Uncertainty

Programming and installation of Materials Transportation Systems (MTS) in a manufacturing setting represent a significant portion of the investment in these systems. The costs are often so high that this alone presents a barrier to adoption amongst midsize manufacturers. Furthermore, the resulting systems are just as costly to reconfigure, limiting the flexibility of the resulting manufacturing facility to product changes. This research examines a localized decision-making scheme, in which individual manufacturing components are enhanced with an amount of intelligence and autonomy to enable the system to automatically self-program. Through an example, we study this paradigm versus fixed programming alternatives as the stochastic variability of the manufacturing setting increases. Localized Decision-Making enables a plug-n-play autonomy that can readily adapt to changes and deal with uncertainties in manufacturing.

Effect of Machining Parameters on Surface Roughness in µ-EDM of Conductive SiC

Micro-electrical discharge machining (μ-EDM) is a non-traditional manufacturing technique that has been widely used in the production of precision engineering components throughout the world in recent years. The most important performance measure in μ-EDM is the surface roughness. The Silicon Carbide is a reaction bonded advanced ceramic that is the fourth hardest material after Diamond, boron nitride and boron carbide. Due to low fracture toughness, machining of Silicon Carbide is accomplished with EDM. In this study, the experimental studies were conducted under varying gap voltage, capacitance and threshold. The numbers of experiments were reduced by L9 array of Taguchi’s theory of DOE. Signal-to-noise (S/N) ratio was employed to determine the most influencing levels of parameters that affect the surface roughness in the μ-EDM of conductive silicon carbide. To validate the study, confirmation experiment has been carried out at optimum set of parameters and predicted results have been found to be in good agreement with experimental findings. A fuzzy logic model for predicting surface roughness during μEDM was also developed on MATLAB software and the goodness of fit of predicted values with experimental values was tested using chi-square test.