Modelling and characterization of ultrasonic consolidation process of Aluminium alloys

Ultrasonic consolidation process is a rapid manufacturing process used to join thin layers of metal at low temperatures and low energy consumption. In this work, finite element method has been used to simulate the ultrasonic consolidation of Aluminium alloys 6061 (AA-6061) and 3003 (AA-3003). A thermomechanical material model has been developed in the framework of continuum cyclic plasticity theory which takes into account both volume (acoustic softening) and surface (thermal softening due to friction) effects. A friction model based on experimental studies has been developed, which takes into account the dependence of coefficient of friction upon contact pressure, amount of slip, temperature and number of cycles. Using the developed material and friction model ultrasonic consolidation (UC) process has been simulated for various combinations of process parameters involved. Experimental observations are explained on the basis of the results obtained in the present study. The current research provides the opportunity to explain the differences of the behaviour of AA-6061 and AA-3003 during the ultrasonic consolidation process. Finally, trends of the experimentally measured fracture energies of the bonded specimen are compared to the predicted friction work at the weld interface resulted from the simulation at similar process condition. Similarity of the trends indicates the validity of the developed model in its predictive capability of the process. © 2008 Materials Research Society.

Investigation of Machining Parameters for Multiple Response Optimization in Microelectrodischarge Milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R a ), maximum peak-to-valley roughness height (R y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R a , R y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R a , R y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min−1, respectively for 4.79 μm s−1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.

Process-variation resilient and voltage-scalable dct architecture for robust low-power computing

In this paper, we present a novel discrete cosine transform (DCT) architecture that allows aggressive voltage scaling for low-power dissipation, even under process parameter variations with minimal overhead as opposed to existing techniques. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors appear only from the long paths that are designed to be less contributive to output quality. The proposed architecture allows a graceful degradation in the peak SNR (PSNR) under aggressive voltage scaling as well as extreme process variations. Results show that even under large process variations (±3σ around mean threshold voltage) and aggressive supply voltage scaling (at 0.88 V, while the nominal voltage is 1.2 V for a 90-nm technology), there is a gradual degradation of image quality with considerable power savings (71% at PSNR of 23.4 dB) for the proposed architecture, when compared to existing implementations in a 90-nm process technology. © 2006 IEEE.

Process variation tolerant low power DCT architecture

2-D Discrete Cosine Transform (DCT) is widely used as the core of digital image and video compression. In this paper, we present a novel DCT architecture that allows aggressive voltage scaling by exploiting the fact that not all intermediate computations are equally important in a DCT system to obtain "good" image quality with Peak Signal to Noise Ratio(PSNR) > 30 dB. This observation has led us to propose a DCT architecture where the signal paths that are less contributive to PSNR improvement are designed to be longer than the paths that are more contributive to PSNR improvement. It should also be noted that robustness with respect to parameter variations and low power operation typically impose contradictory requirements in terms of architecture design. However, the proposed architecture lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors would only appear from the long paths that are less contributive towards PSNR improvement, providing large improvement in power dissipation with small PSNR degradation. Results show that even under large process variation and supply voltage scaling (0.8V), there is a gradual degradation of image quality with considerable power savings (62.8%) for the proposed architecture when compared to existing implementations in 70 nm process technology.

Investigation of the process energy demand in polymer extrusion: a brief review and an experimental study

Extrusion is one of the fundamental production methods in the polymer processing industry and is used in the production of a large number of commodities in a diverse industrial sector. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reducing operating costs. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the largest energy consuming device in an extruder while barrel/die heaters are responsible for the second largest energy demand. This study is focused on investigating the total energy demand of an extrusion plant under various processing conditions while identifying ways to optimise the energy efficiency. Initially, a review was carried out on the monitoring and modelling of the energy consumption in polymer extrusion. Also, the power factor, energy demand and losses of a typical extrusion plant were discussed in detail. The mass throughput, total energy consumption and power factor of an extruder were experimentally observed over different processing conditions and the total extruder energy demand was modelled empirically and also using a commercially available extrusion simulation software. The experimental results show that extruder energy demand is heavily coupled between the machine, material and process parameters. The total power predicted by the simulation software exhibits a lagging offset compared with the experimental measurements. Empirical models are in good agreement with the experimental measurements and hence these can be used in studying process energy behaviour in detail and to identify ways to optimise the process energy efficiency.

3D FEM simulation of helical milling hole process for titanium alloy Ti-6Al-4V

As an emerging hole-machining methodology, helical milling process has become increasingly popular in aeromaterials manufacturing research, especially in areas of aircraft structural parts, dies, and molds manufacturing. Helical milling process is highly demanding due to its complex tool geometry and the progressive material failure on the workpiece. This paper outlines the development of a 3D finite element model for helical milling hole of titanium alloy Ti-6Al-4V using commercial FE code ABAQUS/Explicit. The proposed model simulates the helical milling hole process by taking into account the damage initiation and evolution in the workpiece material. A contact model at the interface between end-mill bit and workpiece has been established and the process parameters specified. Furthermore, a simulation procedure is proposed to simulate different cutting processes with the same failure parameters. With this finite element model, a series of FEAs for machined titanium alloy have been carried out and results compared with laboratory experimental data. The effects of machining parameters on helical milling have been elucidated, and the capability and advantage of FE simulation on helical milling process have been well presented.

An optimized process design for oxymethylene ether production from woody-biomass-derived syngas

The conversion of biomass for the production of liquid fuels can help reduce the greenhouse gas (GHG) emissions that are predominantly generated by the combustion of fossil fuels. Oxymethylene ethers (OMEs) are a series of liquid fuel additives that can be obtained from syngas, which is produced from the gasification of biomass. The blending of OMEs in conventional diesel fuel can reduce soot formation during combustion in a diesel engine. In this research, a process for the production of OMEs from woody biomass has been simulated. The process consists of several unit operations including biomass gasifi- cation, syngas cleanup, methanol production, and conversion of methanol to OMEs. The methodology involved the development of process models, the identification of the key process parameters affecting OME production based on the process model, and the development of an optimal process design for high OME yields. It was found that up to 9.02 tonnes day1 of OME3, OME4, and OME5 (which are suitable as diesel additives) can be produced from 277.3 tonnes day1 of wet woody biomass. Furthermore, an optimal combination of the parameters, which was generated from the developed model, can greatly enhance OME production and thermodynamic efficiency. This model can further be used in a techno- economic assessment of the whole biomass conversion chain to produce OMEs. The results of this study can be helpful for petroleum-based fuel producers and policy makers in determining the most attractive pathways of converting bio-resources into liquid fuels.

Process Optimization for Mass Production of Marine Yeast Candida sp. S 27 and its Nutritional Characterization

The main source of protein for human and animal consumption is from the agricultural sector, where the production is vulnerable to diseases, fluctuations in climatic conditions and deteriorating hydrological conditions due to water pollution. Therefore Single Cell Protein (SCP) production has evolved as an excellent alternative. Among all sources of microbial protein, yeast has attained global acceptability and has been preferred for SCP production. The screening and evaluation of nutritional and other culture variables of microorganisms are very important in the development of a bioprocess for SCP production. The application of statistical experimental design in bioprocess development can result in improved product yields, reduced process variability, closer confirmation of the output response to target requirements and reduced development time and overall cost.The present work was undertaken to develop a bioprocess technology for the mass production of a marine yeast, Candida sp.S27. Yeasts isolated from the offshore waters of the South west coast of India and maintained in the Microbiology Laboratory were subjected to various tests for the selection of a potent strain for biomass production. The selected marine yeast was identified based on ITS sequencing. Biochemical/nutritional characterization of Candida sp.S27 was carried out. Using Response Surface Methodology (RSM) the process parameters (pH, temperature and salinity) were optimized. For mass production of yeast biomass, a chemically defined medium (Barnett and Ingram, 1955) and a crude medium (Molasses-Yeast extract) were optimized using RSM. Scale up of biomass production was done in a Bench top Fermenter using these two optimized media. Comparative efficacy of the defined and crude media were estimated besides nutritional evaluation of the biomass developed using these two optimized media.

Optimization of Two Stage Process for the Growth of In2S3 , CuInSe2 and CuIn (Sel-xSx)2Thin Films for Solar Cell Application

Two stage processes consisting of precursor preparation by thermal evaporation followed by chalcogenisation in the required atmosphere is found to be a feasible technique for the PV materials such as n-Beta In2S3, p-CulnSe2, p-CulnS2 and p-CuIn(Sel_xSx)2. The growth parameters such as chalcogenisation temperature and duration of chalcogenisation etc have been optimised in the present study.Single phase Beta-In2S3 thin films can be obtained by sulfurising the indium films above 300°C for 45 minutes. Low sulfurisation temperatures required prolonged annealing after the sulfurisation to obtain single phase Beta-1n2S3, which resulted in high material loss. The maximum band gap of 2.58 eV was obtained for the nearly stoichiometric Beta-In2S3 film which was sulfurised at 350°C. This wider band gap, n type Beta-In2S3 can be used as an alternative to toxic CdS as window layer in photovoltaics .The systematic study on the structural optical and electrical properties of CuInSe2 films by varying the process parameters such as the duration of selenization and the selenization temperature led to the conclusion that for the growth of single-phase CuInSe2, the optimum selenization temperature is 350°C and duration is 3 hours. The presence of some binary phases in films for shorter selenization period and lower selenization temperature may be due to the incomplete reaction and indium loss. Optical band gap energy of 1.05 eV obtained for the films under the optimum condition.In order to obtain a closer match to the solar spectrum it is desirable to increase the band gap of the CulnSe2 by a few meV . Further research works were carried out to produce graded band gap CuIn(Se,S)2 absorber films by incorporation of sulfur into CuInSe2. It was observed that when the CulnSe2 prepared by two stage process were post annealed in sulfur atmosphere, the sulfur may be occupying the interstitial positions or forming a CuInS2 phase along with CuInSe2 phase. The sulfur treatment during the selenization process OfCu11 ln9 precursors resulted in Culn (Se,S)2 thin films. A band gap of 1.38 eV was obtained for the CuIn(Se,S)2.The optimised thin films n-beta 1n2S3, p-CulnSe2 and p-Culn(Sel-xSx)2 can be used for fabrication of polycrystalline solar cells.

Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

An adaptive chart for monitoring the process mean and variance

Traditionally, an (X) over bar chart is used to control the process mean and an R chart is used to control the process variance. However, these charts are not sensitive to small changes in the process parameters. The adaptive ($) over bar and R charts might be considered if the aim is to detect small disturbances. Due to the statistical character of the joint (X) over bar and R charts with fixed or adaptive parameters, they are not reliable in identifing the nature of the disturbance, whether it is one that shifts the process mean, increases the process variance, or leads to a combination of both effects. In practice, the speed with which the control charts detect process changes may be more important than their ability in identifying the nature of the change. Under these circumstances, it seems to be advantageous to consider a single chart, based on only one statistic, to simultaneously monitor the process mean and variance. In this paper, we propose the adaptive non-central chi-square statistic chart. This new chart is more effective than the adaptive (X) over bar and R charts in detecting disturbances that shift the process mean, increase the process variance, or lead to a combination of both effects. Copyright (c) 2006 John Wiley & Sons, Ltd.

Monitoring process mean and variability with one non-central chi-square chart

Traditionally, an (X) over bar -chart is used to control the process mean and an R-chart to control the process variance. However, these charts are not sensitive to small changes in process parameters. A good alternative to these charts is the exponentially weighted moving average (EWMA) control chart for controlling the process mean and variability, which is very effective in detecting small process disturbances. In this paper, we propose a single chart that is based on the non-central chi-square statistic, which is more effective than the joint (X) over bar and R charts in detecting assignable cause(s) that change the process mean and/or increase variability. It is also shown that the EWMA control chart based on a non-central chi-square statistic is more effective in detecting both increases and decreases in mean and/or variability.

The Variable Sample Size (X)over-bar Chart with Estimated Parameters

The VSS X chart, dedicated to the detection of small to moderate mean shifts in the process, has been investigated by several researchers under the assumption of known process parameters. In practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS chart when the process parameters are estimated, we compare them in the case where the process parameters are assumed known and we propose specific optimal control chart parameters taking the number of Phase I samples into account.

Preliminary results concerning the VSS X- chart with unknow in-control parameters

The VSS X- chart is known to perform better than the traditional X- control chart in detecting small to moderate mean shifts in the process. Many researchers have used this chart in order to detect a process mean shift under the assumption of known parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS X- control chart when the process parameters are estimated and we compare them in the case where the process parameters are assumed known. We draw the conclusion that these performances are quite different when the shift and the number of samples used during the phase I are small. ©2010 IEEE.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.