Optimization of roll forming process parameters—a semi-empirical approach

The roll forming process parameters play a major role in the quality of the final roll-formed product. Optimum configuration without any cost increase in the roll forming line could present accurate and flawless products. In this paper, a roll forming process experimental modelling of a symmetrical U-section profile from advanced high strength steel (AHSS) material (type DP600) is presented. The factors selected for this study are the roll forming line velocity, the inter-distance between roll stations, the roll gap, and the diameter of the rolls. An optimization procedure for the roll forming line, via statistical design of the experimental simulation runs, is also presented. The optimum values of process parameters are calculated for minimum elastic longitudinal strains and shear strains, at strip edge, for each roll station. A reduction of 20–35% in elastic longitudinal strains could occur for all roll stations, and 30–50% reduction in shear strains occurs for roll stations with a greater folding angle, as this leads to roll-formed products of a better quality. Finally, the contribution of each factor on the longitudinal and shear strains has been calculated, showing that the inter-distance between the roll stations plays a dominant role in the roll forming process.     

Optimization of an aluminum profile extrusion process based on Taguchi’s method with S/N analysis

Taguchi’s design of experiment and numerical simulation were applied in the optimization of an aluminum profile extrusion process. By means of HyperXtrude, the extrusion process was simulated and the effects of process parameters on the uniformity of metal flow and on the extrusion force were investigated with the signal to noise ratio and the analysis of variance. Through analysis, the optimum combination of process parameters for uniform flow velocity distribution was obtained, with the billet diameter of 170?mm, ram speed of 2.2?mm/s, die temperature of 465°C, billet preheated temperature of 480°C, and container temperature of 425°C. Compared with the initial process parameters, the velocity relative difference in the cross-section of extrudate was decreased from 2.81% to 1.39%. In the same way, the optimum process parameters for minimum required extrusion force were gained, with the billet diameter of 165?mm, ram speed of 0.4?mm/s, die temperature of 475°C, billet preheated temperature of 495°C, and container temperature of 445°C. A 24.7% decrease of required extrusion force with optimum process parameters was realized. Through the optimization analysis in this study, the extrusion performance has been greatly improved. Finally, the numerical results were validated by practical experiments, and the comparison showed that the optimization strategy developed in this work could provide the effective guidance for practical production.     

Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept

This paper describes the development of multi response optimization technique using utility method to predict and select the optimal setting of machining parameters in wire electro-discharge machining (WEDM) process. The experimental studies in WEDM process were conducted under varying experimental conditions of process parameters, such as pulse on time(T_on), pulse off time(T_off), peak current (IP), wire feed (WF), wire tension (WT) and servo voltage (SV) using pure titanium as work material. Experiments were planned using Taguchi’s L27 orthogonal array. Multi response optimization was performed for both cutting speed (CS) and surface roughness (SR) using utility concept to find out the optimal process parameter setting. The level of significance of the machining parameters for their effect on the CS and SR was determined by using analysis of variance (ANOVA). Finally, confirmation experiment was performed to validate the effectiveness of the proposed optimal condition.     

Multi response optimization of process parameters based on Taguchi��Fuzzy model for coal cutting by water jet technology

The process of material cutting and fracture by high velocity water jets is a complex series of phenomena which may involve compression, tension, shear, erosion, wears, cracking, wave propagation, and cavitations damage. This makes the exact analysis of the jet cutting process to be very complicated. The problem of water jet coal cutting is a multiresponse problem. There are two output variables, depth of cut and cutting width whose optimization will result in the increase in the productivity of coal cutting. In this paper, a Taguchi?CFuzzy decision method has been used to determine the effective process parameters for improving the productivity of coal mines. The Taguchi method of experimental design is a widely accepted technique used for producing high quality products at low cost. The optimization of multiple responses in complex processes is common; therefore, to reduce the degree of uncertainty during the decision making, fuzzy rule-based reasoning was integrated with the Taguchi loss function.     

Noise reduction of a high-speed printing system using optimized gears based on Taguchi’s method

To meet the increasing demand for more quiet printers, a noise reduction method is required for high-speed laser beam printing. The driving-gear noise is one of the most important components influencing the noise level in laser beam printers. In this paper, optimized gear designs based on Taguchi’s method are presented. The proposed optimized gears are applied to a high-speed laser beam printer. The design parameters for the plastic gear are selected during optimization as follows: a pressure angle of 20°, a helix angle of 20°, a module of 0.5, and a profile coefficient of 1.4/0.2/1.2 (cutter addendum × module / cutter tip radius × module / cutter dedendum × module). Through the Taguchi method, the prominence ratio and loudness in the sense of human hearing range, as well as the sound pressure level (SPL) are also reduced in the present printing system.     

Optimization of sand-casting process variables—a process window approach

The present competitive market focuses on producing high-quality products with the lowest possible cost. To help accomplish this objective, various quality-enhanced tools have been put forward in recent years and, of these, process window approach (PWA) has emerged as perhaps the most viable and efficient technique for process quality improvement. The PWA is a powerful tool for quickly optimizing and confirming the quality and robustness of a process setup for any parameters. The work in this paper focuses on implementing a proposed PWA in order to optimize the sand-casting operation variables. In this research, the authors have kept their prime focus on minimizing the defects developed in the sand-casting process by PWA. Analysis of various critical process parameters and the interaction among them is carried out with the help of Taguchi method of experimental design. To optimize the results obtained and to make the analysis more precise and cost-effective, response surface methodology (RSM) is also incorporated. The optimized parameters obtained using the Taguchi method and RSM are then tested in an industrial case study. It is validated by proposed process window approach.     

Multi-response optimisation of sintering parameters of Al–Si alloy/fly ash composite using Taguchi method and grey relational analysis

In this paper, an attempt has been made to optimise the sintering process parameters of Al–Si (12%) alloy/fly ash composite using grey relational analysis. Al–Si alloy/fly ash composite was produced using powder metallurgy technique. Al–Si alloy powder was homogenously mixed with various weight percentages of fly ash (5–15 wt.%) and compacted at a pressure ranging from 307 to 512 MPa. The green compacts were sintered at temperatures between 575 and 625°C. Experiments have been performed under different conditions of temperature, fly ash content, and compacting pressure. Taguchi’s L₉ orthogonal array was used to investigate the sintering process parameters. Optimal levels of parameters were identified using grey relational analysis, and significant parameter was determined by analysis of variance. Experimental results indicate that multi-response characteristics such as density and hardness can be improved effectively through grey relational analysis.     

Predicting surface quality of γ-TiAl produced by additive manufacturing process using response surface method

Electron beam melting (EBM) has been found to be a promising technology for producing complex shaped parts from gamma titanium aluminide alloys (γ-TiAl). The parts produced by this process are projected to have dimensions very close to the desired final shapes. However, the surface roughness of the parts produced by EBM is excessively rough. In many applications, it is necessary to improve the quality of manufactured parts using a convenient post process. This paper determines process parameters of end milling when it is used as a post process for the parts produced by EBM. Design of experiments has been used to study the effect of the selected input parameters of end milling (spindle speed, feed rate, depth of cut and coolant type) on the surface roughness of γ-TiAl parts. Response surface methodology is used to develop a predictive model for surface roughness. Effects of the selected milling process are investigated. This paper also optimizes the selected process parameters to minimize the value of the obtained surface roughness.     

Research on soft sensing modeling method of gas turbine’s difficult-to-measure parameters

During the operation of a gas turbine, there are many key parameters that are difficult to directly measure or to ensure measurement accuracy, which can only be measured by offline analysis methods. However, the data obtained by offline analysis has a large time lag, and it is difficult to realize real-time monitoring, control and optimization of gas turbines. In recent years, with the widespread application of data-driven methods, data-driven soft sensing technology has become a breakthrough method for online prediction of difficult-to-measure variables. Due to the time-varying nature of the gas turbine operation process, the predictive performance of the offline modeling method will inevitably degrade over time. Therefore, an adaptive soft-sensing multi-level modeling method based on the combination of the just in time learning and the ensemble learning is proposed in this paper. Taking compressor inlet air flow and turbine inlet temperature as examples, the research is carried out and verified by actual operating data. The results verify the effectiveness of the method.

     

Modeling of surface hardness in hot chamber die casting using Buckingham’s π approach

Hot chamber die casting (HCDC) process is designed to achieve high dimensional accuracy and surface hardness (SH) for industrial applications (like machine tool components). In the present study, outcome of Taguchi model has been used for developing a mathematical model for SH; using Buckingham’s π-theorem for HCDC process. Three input parameters namely pressure at 2^nd phase; metal pouring temperature and die opening time were selected to give output in form of SH. This study will provide main effects of these variables on SH and will shed light on the casting hardness mechanism in HCDC process. The comparison with experimental results will also serve as further validation of model.     

Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi��s quality loss function, multivariate statistical methods and artificial intelligence

This paper presents a hybrid design strategy for the determination of the optimum laser drilling parameters which simultaneously meets the requirements for seven quality characteristics (responses) of the holes produced during pulsed Nd:YAG laser drilling of a thin sheet of nickel-based superalloy Nimonic 263. The process was designed using two approaches based on the experimental data. In the first approach, the quality losses of seven correlated responses were uncorrelated into a set of components using the principal component analysis; then the grey relational analysis was applied to synthesise components into a synthetic performance measure. Since this approach considered only parameter values used in the experiment, the second approach was developed to find the global optimal parameters solution using an artificial neural network to model the relation between parameters and a synthetic performance measure, and a genetic algorithm to perform a search for the global optimum in a continual multidimensional space. The analysis of the application indicated that the proposed approaches gave a better result, in terms of the optimal parameter settings that yield the maximal synthetic performance measure, than several commonly used methods for multi-response process parameters design. The results demonstrated that the robust Nd:YAG laser drilling of Ni-based superalloy sheets was designed with respect to the requirements for seven quality characteristics of the drilled holes, by using the proposed strategy.     

Optimum selection of abrasive flow machining conditions during fine finishing of Al/15 wt% SiC-MMC using Taguchi method

Abrasive flow machining (AFM) is gaining widespread application finishing process on difficult to reach surfaces in aviation, automobile, and tooling industry. Al/SiC_p-MMC is a promising material in these industries. Here, AFM has been used to finish conventionally machined cylindrical surface of Al/15 wt% SiC_p-MMC workpiece. This paper presents the utilization of robust design-based Taguchi method for optimization of AFM parameters. The influences of AFM process parameters on surface finish and material removal have been analyzed. Taguchi experimental design concept, L₁₈ (6¹?×?3⁷) mixed orthogonal array is used to determine the S/N ratio and optimize the AFM process parameters. Analysis of variance and F test values also indicates the significant AFM parameters affecting the finishing performance. The mathematical models for R _a, R _t, ΔR _a, and ΔR _t and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. Scanning electron micrographs testifies the effectiveness of AFM process in fine finishing of Al/15 wt% SiC_p-MMC.     

Process characterization of vibrostrengthening and application to fatigue enhancement of aluminum aerospace components��part I. Experimental study of process parameters

Vibrostrengthening is a fatigue-enhancement process, originally developed by the Russian aviation industry (Rumyantsev et al. 2004). A potential alternative to shot peening, currently a standard industrial surface treatment for fatigue enhancement, vibrostrengthening offers the potential for shorter processing times and uniform treatment of the surface, especially when dealing with fragile parts and complex part geometries. Vibrostrengthening is a modification of a vibratory finishing process in which the parts or workpieces and a medium of hard granular particles are vibrated together in a processing tub causing the particles to mechanically work the surface of the workpiece. In vibrostrengthening, the workpiece is fixed inside a vibratory tub, increasing the relative velocities between the particles in the medium and the workpiece. This gives rise to more aggressive mechanical working of the workpiece surface. The resulting plastic deformation at the surface produces a sub-surface compressive residual stress, which together with a better surface finish, is conjectured to improve the fatigue strength of workpiece. This paper is an experimental study of vibrostrengthening of aluminum components for fatigue life enhancement. The effects of various process parameters on the fatigue strength of a specimen are studied to experimentally characterize the process. These experiments also demonstrate that the vibrostrengthening process produces significant fatigue enhancement on experimental samples produced by machining. Further, these experiments verify that, in fact, fatigue enhancement in the vibrostrengthening process is a result of the combined effect of inducing a compressive residual stress field within the material and improving the material??s surface finish. Fatigue tests indicate that the fatigue enhancement of this process is comparable to, if not better than, shot peening. One important reason for such a favorable comparison, given the lower levels of residual stress that result from this process, is the superior surface finish it produces. A companion paper (Sangid et al. 2010) presents a study involving process visualization to understand and explain the process mechanics; further, a computational model is produced to characterize the fatigue enhancement of the process through the compressive residual stress field and surface topography.     

Modeling and analysis of the effects of processing parameters on the performance characteristics in the high pressure die casting process of Al–SI alloys

The high pressure die casting (HPDC) process has achieved remarkable success in the manufacture of aluminum–silicon (Al–SI) alloy components for the modern metal industry. Mathematical models are proposed for the modeling and analysis of the effects of machining parameters on the performance characteristics in the HPDC process of Al–SI alloys which are developed using the response surface methodology (RSM) to explain the influences of three processing parameters (die temperature, injection pressure and cooling time) on the performance characteristics of the mean particle size (MPS) of primary silicon and material hardness (HBN) value. The experiment plan adopts the centered central composite design (CCD). The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of both the mean particle size of primary silicon and its hardness value. Two main significant factors involved in the mean particle size of primary silicon are the die temperature and the cooling time. The injection pressure and die temperature also have statistically significant effect on microstructure and hardness.     

Detection of hand tremor in patients with Parkinson’s disease using a non-invasive laser line triangulation measurement method

Detection of hand tremor for evaluating and diagnosing early stage of Parkinson’s disease (PD) remains a challenge. The purpose of this study was to correlate hand tremors analyzed by a non-invasive method with clinical manifestations among patients with PD. Four different modes of tremor detection in patients with PD were detected individually using a laser line triangulation measurement (LLTM) method and off-line analyzed.The results showed a significant correlation between age at disease onset and tremor frequency obtained from the left hand and from the non-dominant hand. Furthermore, there was a significant positive correlation between disease duration and tremor frequency obtained from the left hand and from the non-dominant hand using different detection modes.We conclude that the laser line triangulation measurement is a non-invasive, non-contact, portable, easy-to-use and low cost method that can detect tremor early in the course of patients diagnosed with PD.     

Investigating the parameters of electron beams in the presence of ion compensation of the charge using the “hole chamber” method

The parameters of electron beams produced by a hollow-anode gas-discharge electron gun based on a glow discharge and operated in an ion focusing mode at medium and low vacuum have been investigated. The “hole chamber” method is proposed. Using a device based on this method, it is possible to measure the electron beam parameters with a high efficiency under conditions of ion focusing when testing the performance of the gas-discharge electron gun in technological processes.     

Hydraulic Optimization of a Double-channel Pump’s Impeller Based on Multi-objective Genetic Algorithm

Computational fluid dynamics (CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should b...     

Optimization design of a CUSUM control chart based on taguchi’s loss function

The CUSUM charts have been widely used in statistical process control (SPC) across industries for monitoring process shifts and supporting online measurement and distributed computing. This paper proposes an algorithm for the optimimal design of a CUSUM control chart detecting process shifts in the mean value. The algorithm optimizes the sample size, sampling interval, control limit and reference parameter of the CUSUM chart through minimizing the overall mean value (ML) of a Taguchi’s loss function over the probability distribution of the random process mean shift. A new feature related to the exponential of the sample mean shift is elaborated. Comparative studies reveal that the proposed ML-CUSUM chart is considerably superior to the Shewhart ML- $\overline{X} $ chart and the conventional CUSUM chart in terms of the overall loss of ML.