Optimal Combination Of Process Parameters Research Articles

Optimization of Machining Parameters in Steel Turning Operation by Taguchi Method

This work is optimization of machining parameters in steel turning operation by Taguchi method. In this study the experimental work was carried out by turning EN-31 steel alloy by using tungsten carbide inserts. There were three main purposes of this study. The first was to explain and demonstrate a systematic procedure of Taguchi parameter design and applying it to the data on turning. The second was to find out the optimal combination of process parameters based on S/N ratio and to know the significance of each parameter by performing ANOVA analysis. The third important aim was to find out the effect of lubricant temperature in steel tuning process on the response (i.e surface roughness). The cutting parameters namely feed rate, depth of cut, and lubricant temperature were varied to observe the effects on responses. The main conclusion drawn from this study is that better surface finish is obtained by applying cooled lubricant. Even with higher depth of cuts surface finish is improved if lubricant temperature is lowered.

Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

To improve the corrosion-resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion-resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. Cladding these properties can be achieved with minimum cost. The main problem faced in cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to predict various input process parameters (welding current, welding speed, gun angle, contact tip-to-work distance, andpinch) to getoptimum dilutionin stainless steel cladding of low carbon structural steel plates using Gas Metal Arc Welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. Using Artificial Neural Network (ANN) the parameters were predicted, and percentage of error was calculated between predicted and actual values. The direct and interaction effects of process parameters on clad bead geometry are presented in graphical form.

An analysis of an optical coating process capability prediction model in a Six Sigma procedure by integrating APIBPN and K-means

This study focused on plastic panel products, and treated product penetration rate as a quality characteristic to construct an optical coating process capability prediction model. This study applied Six Sigma to conduct an empirical study on an optical film processing plant.Combining the Taguchi’s parameter design method and the back propagation neural network (BPNN) prediction method, this study used the orthogonal array in experimental design, and employed the Taguchi method to analyse the data obtained from the orthogonal array experiments to study the key factors and their levels, in order to determine the optimal process parameter combinations. The influential process parameters were input into the after ‘apicalis’ in Pachycondyla apicalis optimise back propagation network (APIBPN) and K-means, the outputs of which were the prediction results of the coating film process capability. The results can provide engineers a reference in quality improvement and decision-making planning. The experimental results suggested that the Cpk was improved from 0.89 to 1.63, indicating a significant improvement in process capability value. The prediction accuracy rate is over 97.6%, which is better than that of the trial and error method, and can improve coating film process capabilities and product quality.

Application of Optimization Algorithms in Injection Molding Process Parameters Optimization

In this paper, the numerical simulation and calculation of injection molding process are taken in the Moldflow software. The BP artificial neural network combining with the orthogonal experiment design method is used to set up the injection molding process agent model, genetic algorithms are applied to realize global optimization, finally, the optimal combination of process parameters of each quality indicators is obtained.

Using Taguchi Method to Improve 3D Polishing Process Capability for Optical Fiber

This study used Six Sigma DMAIC five-phase improvement procedures for an empirical research on a multinational optical interconnect manufacturer. The Taguchi method was used for the design of experiment, and the data obtained from the orthogonal table were used to determine the key factors and levels, in order to propose the optimal combination of processing parameters and levels for fiber patch cord 3D polishing. The combination of rough polishing time of 30s, polishing pressure of 0.02pa, polishing pad hardness of 65 degrees, and the fine polishing time of 80s could produce the fittest parameters for a satisfactory level, reduce the defect rate and waste of production cost, and reach the Six Sigma standard. The results proved that the improved process capability model, as constructed by this study, can effectively improve the apex offset quality of fiber patch cords. The findings can serve as references for engineers to improve the quality and production process capability.

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.

A study on the influence of hot press forming process parameters on mechanical properties of green composites using Taguchi experimental design

Green composites made from natural fibers, and biopolymers offer a potential alternative to the petroleum-based materials, that are currently being used in many nonstructural applications. In spite of being biodegradable and eco friendly, range of applications is limited due to poor mechanical properties. Hence an attempt is made in this work to improve the mechanical properties of green composites by optimizing the hot press forming process parameters using Taguchi experimental design. Process parameters such as temperature, pressure, heating time, cooling system and recrystallization soak time were chosen for evaluation by Taguchi method. An L16 orthogonal array was chosen for the design of experiments. The optimum combination of process parameters is obtained by using the analysis of the signal-to-noise ratio. The levels of importance of process parameters on mechanical properties were determined by using analysis of variance (ANOVA). The variation of tensile, flexural and impact properties with process parameters were mathematically modeled using the regression analysis. Finally, the presented models are also verified by a set of verification tests.

Grindability Improvement of AlSiTi Conductive Ceramic

Experimental investigation was done to improve grindability aspects of conductive ceramic using cryogenic coolant. We made mathematical models of response variables, such as grinding force, surface roughness, and subsurface damage length in terms of grinding process parameters. Conductive ceramic material, also known as AlSiTi, is ceramic matrix composite. It has better thermal and electric conductivity compared to conventional ceramics. Normally, ceramic materials are ground without using coolants. But the conductive ceramics can be ground using coolants due to improved thermal properties. Grinding is an important machining process especially for ceramic materials. Hence, it is important to study the grindability aspects of AlSiTi material using cryogenic coolants and to suggest the optimum combination of process parameters that leads to high production rate along with better surface integrity. Present research work studies for the first time the effect of coolant on the advanced ceramic.

Design and verification of fabric surface softness testing system

This study developed a testing system for fabric surface softness based on the sled method. The system is compact, lightweight, portable and convenient to use. The device can be installed on the sueding machine as an on-line real-time testing system for fabric surface softness, thus improving the response efficiency of the process parameters of the sueding machine. Moreover, it can be used to construct a database of these parameters, store different fabric types, process quality requirements and reproduce processing at the same quality, in order to automatically, rapidly and accurately calibrate the parameters. This study applied the Taguchi method on the process parameters of the sueding machine to analyze the interaction between parameters, design the orthogonal array according to actual needs, and process the grinded fabric on-line. It also measured the testing system and plotted the Signal-to-Noise (S/N) ratio response table based on the S/N calculated for the parameter combinations. In the table, the larger S/N ratios represent a better quality characteristic and smaller variation. Hence, the main effect of the control factors and the optimum factor level can be obtained. After determining the optimal combination of process parameters, the process and measuring experiment of the reference group was conducted with these control factors to verify the results. It was proven that the device can obtain the maximum surface softness, and effectively and accurately test the surface softness of finished fabrics.

Application of 6-sigma design system to developing an improvement model for multi-process multi-characteristic product quality

Rapid developments in advanced manufacturing techniques and information technology have resulted in significant increases in the functionality, precision, and complexity of products. Most products have multiple quality characteristics, which may include multiple unilateral and bilateral specifications. The process capability for any quality characteristic must be satisfied if customers are to accept a product. The production process of complex products generally consists of several stages and processes. The qualities of these processes can be independent or interdependent. This paper develops an integrated assessment model for the manufacture of multi-process, multi-characteristic products based on the process capability index and process yield rate. The proposed method assess six-sigma improvement using the ‘define, measure, analyze, design, verify’ model. This study investigates the manufacturing processes of 12.7 mm ordinary-grade machine gun cartridges in order to increase their shooting precision. The proposed assessment model identifies the critical-to-quality processes that affect the precision, and then applies redesign and experimental design to obtain the optimal combination of process parameters. Finally, a mathematical programming method verifies the results, confirming an improvement in shooting precision. The resulting cartridges meet the customer demand for match quality.

The effect of moisture content in fibre laser cutting of pine wood

This paper reports a statistical analysis of the multiple-pass laser cutting of wet and dry pine wood with a Ytterbium fibre laser. As multiple factors affect the laser wood cutting process, finding the optimal combination of process parameters is necessary to achieve good quality and high process efficiency. Design of experiments (DOE) and statistical modelling were used in this study to investigate the significant process parameters and their interactions. A high brightness, 1 kW IPG single mode, continuous wave Ytterbium doped fibre laser was employed to cut wet and dry pine wood samples. The parameters investigated are laser power, traverse speed, focal plane position (f.p.p.), gas pressure, number of passes, direction of cut (normal or parallel to wood's tracheids) and the moisture content. The experimental results were compared against process responses defining the efficiency (i.e. kerf depth and energy consumption) and quality of the cut section (i.e. kerf width, heat affected zone—HAZ, edge surface roughness and perpendicularity). It has been found that the laser cutting process was mainly affected by the moisture content and the cut direction with respect to the wood's tracheids, followed by traverse speed, laser power and the number of passes. The effect of moisture content on energy consumption in the laser cutting process of both wet and dry wood is analysed. The wood cutting results with fibre laser are compared with those from a CO 2 laser.

Process optimization for Cr(VI) adsorption onto activated carbons by experimental design

A study on the adsorption of Cr(VI) onto raw and modified activated carbons was conducted and the process parameters were optimized using Response Surface Methodology (RSM). Apricot stones have been converted to microporous activated carbon with considerably high surface area (1462 m 2 g −1) by phosphoric acid activation. Produced carbons have been characterized by using different physicochemical methods. In order to determine the effects of process parameters namely temperature (20–60 °C), initial solution pH (2–6) and initial Cr(VI) concentration (30–60 mg L −1) on Cr(VI) uptake from aqueous solution, a three-level, three-factor, Box–Behnken design has been employed. The second order mathematical model was developed by regression analysis of the experimental data obtained from 17 batch runs. The optimum pH, temperature and initial concentration were found to be 2.0, 60 °C, and 60 mg L −1, respectively. Cr(VI) adsorption capacity was 262 mg g −1 at the optimum combination of process parameters. The enthalpy change of Cr(VI) adsorption was found to be 54.05 kJ mol −1. Dynamic adsorption data were applied to pseudo-first-order and pseudo-second-order rate equations. Pseudo-second-order kinetic model well expressed Cr(VI) adsorption onto activated carbon. The results of both thermodynamic and kinetic study indicated the chemical interaction between Cr(VI) species and surface functional groups on carbon surface.

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a p), radial immersion (a e), feed rate (f t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.

Removal of hydrogen sulphide from high salinity wastewater by emulsion liquid membrane

AbstractThis article presents a experimental study on the removal of hydrogen sulphide (H2S) from high salinity wastewater by emulsion liquid membrane (ELM). The ELM contains diethanolamine (DEA) as carrier, kerosene as solvent and span 80 as surfactant. The fundamental parameters (viz. surfactant concentration, carrier concentration, strip phase concentration, phase ratio, agitation speed and time) affecting the removal of H2S were investigated to select the optimum combination of process parameters. The results showed the optimal governing parameters were: inner phase NaOH aqueous solution 2.0 wt%, Surfactant (span 80) 5.0 vol%, carrier (DEA) 6.0 vol%, treat ratio 1:5, agitation speed 250 rpm, agitation time 15 min, respectively, and removal efficiency of 97.3% was achieved. © 2011 Canadian Society for Chemical Engineering

Optimization of hot extrusion process for AZ61 magnesium alloy carriers

This study applies fuzzy-base Taguchi method to investigate the optimal process parameters of the multiple performance characteristics index (MPCI) for the hot extrusion process of AZ61 magnesium alloy products. Flattening strength, fracture strength and extrusion load are taken as the input data of MPCI. Since the optimal combination of process parameters for Taguchi method varies with individual quality characteristic, the optimal combination of parameters for different quality characteristics may be contradictory to each other. In order to consider the larger-the-better quality characteristic of flattening strength and T-slot fracture strength values as well as the smaller-the-better quality characteristic of extrusion load, the fuzzy-base Taguchi method is used to analyze and to obtain the optimal combination of process parameters for the MPCI. First of all, orthogonal array is applied to arrange the experimental combination of extrusion process. The signal-to-noise (S/N) ratios of the three quality characteristics of flattening strength, T-slot fracture strength and extrusion load for the products acquired from experiments are calculated. The S/N ratio serves as the input variable of fuzzy control unit, whereas MPCI serves as a single output variable. The acquired MPCI is employed to analyze the optimal process parameters. In this study, an optimal combination of process parameters of AZ61 carrier for MPCI is obtained, and the verification experiments are conducted to prove the accuracy. Moreover, mechanical properties of AZ31 and AZ61 magnesium alloy carriers are tested for further comparison.

Optimization of Process Parameter Variability in 45 nm PMOS Device using Taguchi Method

This study reports on an investigation of the effect and optimization of process parameter variability on poly sheet resistance (RS) and leakage current (ILeak) in 45 nm PMOS device. The experimental studies were conducted under varying four process parameters, namely Halo implantation, Source/Drain Implantation, Oxide Growth Temperature and Silicide Anneal Temperature. Taguchi Method was used to determine the settings of process parameters. The level of importance of the process parameters on the poly sheet resistance and leakage current were determined by using Analysis of Variance (ANOVA). Virtual fabrication of the devices was performed by using ATHENA module. While the electrical characterization of the devices was implemented by using ATLAS module. The optimum process parameter combination was obtained by using the analysis of Signal-to-Noise (S/N) ratio. The confirmation tests indicated that it is possible to decrease the poly sheet resistance and leakage current significantly by using the Taguchi method. The results show that the RS and ILeak after optimizations approaches are 67.53 Ω sq-1 and 0.1850 m A μm-1, respectively. In this study, S/D implantation was identified as one of the process parameters that has the strongest effect on the response characteristics.

The Numerical Optimization of Laser Shot Peening Parameters Based on Response Surface Analysis

Residual stress field induced by laser shot peening (LSP) was simulated using Box-Behnken experimental design. Compressive residual stress field intensity (S) was introduced to estimate the effect of compressive residual stress field on fatigue performance. The effect of laser process parameters (such as laser shock wave peak pressure, spot diameter and peening number) on S was analyzed by response surface analysis (RSA), quadratic regression predicting model for S was established, and the rationality of that was verified. Finally, the optimal combination of laser process parameters oriented to anti-fatigue manufacture was optimized. The results indicated statistical analysis results agreed well with those of simulation, RSA for parameter optimization of LSP is feasible.

Statistical analysis of process parameters in drilling of AL/SICP metal matrix composite

This paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/SiCp metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, drill type, point angle of drill, and heat treatment. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors were the feed rate and tool type. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness. The optimal drilling performance for the surface roughness was obtained at 0.16 mm/rev feed rate, 260 rev/min spindle speed, 130° drill point angle, carbide drill type, and as-received heat treatment settings.

Optimization of a pharmaceutical freeze-dried product and its process using an experimental design approach and innovative process analyzers

The aim of the present study was to examine the possibilities/advantages of using recently introduced in-line spectroscopic process analyzers (Raman, NIR and plasma emission spectroscopy), within well-designed experiments, for the optimization of a pharmaceutical formulation and its freeze-drying process. The formulation under investigation was a mannitol (crystalline bulking agent)–sucrose (lyo- and cryoprotector) excipient system. The effects of two formulation variables (mannitol/sucrose ratio and amount of NaCl) and three process variables (freezing rate, annealing temperature and secondary drying temperature) upon several critical process and product responses (onset and duration of ice crystallization, onset and duration of mannitol crystallization, duration of primary drying, residual moisture content and amount of mannitol hemi-hydrate in end product) were examined using a design of experiments (DOE) methodology. A 2-level fractional factorial design (25−1=16 experiments+3 center points=19 experiments) was employed. All experiments were monitored in-line using Raman, NIR and plasma emission spectroscopy, which supply continuous process and product information during freeze-drying. Off-line X-ray powder diffraction analysis and Karl-Fisher titration were performed to determine the morphology and residual moisture content of the end product, respectively.In first instance, the results showed that – besides the previous described findings in De Beer et al., Anal. Chem. 81 (2009) 7639–7649 – Raman and NIR spectroscopy are able to monitor the product behavior throughout the complete annealing step during freeze-drying. The DOE approach allowed predicting the optimum combination of process and formulation parameters leading to the desired responses. Applying a mannitol/sucrose ratio of 4, without adding NaCl and processing the formulation without an annealing step, using a freezing rate of 0.9°C/min and a secondary drying temperature of 40°C resulted in efficient freeze-drying supplying end products with a residual moisture content below 2% and a mannitol hemi-hydrate content below 20%. Finally, using Monte Carlo simulations it became possible to determine how varying the factor settings around their optimum still leads to fulfilled response criteria, herewith having an idea about the probability to exceed the acceptable response limits. This multi-dimensional combination and interaction of input variables (factor ranges) leading to acceptable response criteria with an acceptable probability reflects the process design space.

Effect of injection molding process parameters on optical properties of lenses

In this paper, injection molded polymer lenses are measured and analyzed to determine the effects of process parameters on the geometric errors and optical properties of the lenses. The significant factors and optimal combination of process parameters in the injection molding process could be determined by making use of the Taguchi method for screening the control factors via the analysis of variance (ANOVA) process. Finally, the effects of storage time on contour precision and the optical properties of the lens are also investigated. The contour precision and optical properties degraded six months after production with both significant factors and optimal combination of the factors being changed.