Showing papers on "Process variable published in 2011"

Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA

Abstract: In this study, Simulated Annealing (SA) and Genetic Algorithm (GA) soft computing techniques are integrated to estimate optimal process parameters that lead to a minimum value of machining performance. Two integration systems are proposed, labeled as integrated SA-GA-type1 and integrated SA-GA-type2. The approaches proposed in this study involve six modules, which are experimental data, regression modeling, SA optimization, GA optimization, integrated SA-GA-type1 optimization, and integrated SA-GA-type2 optimization. The objectives of the proposed integrated SA-GA-type1 and integrated SA-GA-type2 are to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, to estimate the optimal process parameters values that has to be within the range of the minimum and maximum process parameter values of experimental design, and to estimate the optimal solution of process parameters with a small number of iteration compared to the optimal solution of process parameters with SA and GA optimization. The process parameters and machining performance considered in this work deal with the real experimental data in the abrasive waterjet machining (AWJ) process. The results of this study showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

Abstract: This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855–930 W and 4.50–4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800–840 W and increased to 4.75–5.37 m/min, respectively, to obtain stronger and better welds.

Studying the influence of initial powder characteristics on the properties of final parts manufactured by the selective laser melting technology

Abstract: Selective laser melting (SLM) is an additive manufacturing process that enables direct manufacturing of 3D complex shape parts and internal architecture from powder materials. The SLM technology is characterised by high temperature gradients and solidification rates that have a significant effect on the microstructures and properties of final parts. The present paper aims at understanding the influence of the initial properties of various martensitic stainless steel powders on the final microstructures and mechanical properties of parts manufactured using the same optimised SLM process parameter settings. The results obtained show that for applied optimum process parameters, the thermal effects are the same for all martensitic powders used. Besides, the final microstructures and properties are different. The results clearly show the effect of the initial complex chemical composition of the martensitic precipitation hardening powder on the microstructures of final parts, and consequently, on their properties.

Fabrication and Characterization of Melt-Extracted Co-Based Amorphous Wires

Abstract: Amorphous Co68.15Fe4.35Si12.25B15.25 wires with smooth surface and circular cross section were fabricated by melt extraction technology using a copper wheel with a knife-edge cross section angle of 60 deg. The effect of some process parameters such as wheel circumference velocity, molten alloy feed rate, and temperature on the geometry and weight, i.e., melt extracted layer thickness, of wire was examined carefully. An optimum process parameter to produce high-quality circular wires was presented. A high resolution CCD video camera recorder was used to monitor the changing of the surface shape of molten alloy contacting the wheel tip under different conditions. It was found that the mechanism of the wire formation during the optimum process condition was controlled by the momentum mechanism, while in the low wheel speed region, heat transfer turned out to be a dominant factor. Some characteristics of the circular wires such as amorphous nature and tensile strength were also studied.

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

Abstract: 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.

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning.

Abstract: Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R a value has been obtained as 1.1999 μm and the corresponding predicted surface roughness value is 1.1859 μm, which implies greater accuracy.

On-line batch process monitoring using hierarchical kernel partial least squares

Abstract: In this paper, new monitoring approach, hierarchical kernel partial least squares (HKPLS), is proposed for the batch processes. The advantages of HKPLS are: (1) HKPLS gives more nonlinear information compared to hierarchical partial least squares (HPLS) and multi-way PLS (MPLS) and (2) a new batch process monitoring using HKPLS does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The proposed method is applied to the penicillin process and continuous annealing process and is compared with MPLS and HPLS monitoring results. Applications of the proposed approach indicate that HKPLS effectively capture the nonlinearities in the process variables and show superior fault detectability.

System and method for visual presentation of information in a process control system

Abstract: Various graphical displays used for visualization of control techniques in a process control system can be provided to an operator. For example, the graphical display could include an image (224) associated with a portion of a process and icons (602, 608, 614, 638) that are associated with corresponding process variables. At least some of the icons (638) include a symbol that represents a change of one value of the associated process variable relative to another value of the process variable. Another graphical display could include a pigeonhole display (1200) that includes visual elements (802, 810, 814) associated with corresponding process variables. Selection of a visual element could present the operator with a peephole display (1000) that includes information associated with process variables associated with the selected visual element. Each visual element (900) displays layers (902, 904, 906) of information that are associated with its process variable.

Five-axis side milling machining process parameter design method

Abstract: The invention discloses a five-axis side milling machining process parameter design method, belongs to the technology of numerical control (NC) machining, and solves the problem that real machining conditions cannot be reflected in cutting force calculation in the conventional process parameter design method. The method comprises the following steps of: tool path planning, cutting force calculation and process parameter optimization; in the tool path planning step, an NC code is generated by using computer-aided manufacturing (CAM) software; in the cutting force calculation step, first, a continuous tool path is generated from the NC code; then, a cutting thickness is obtained; and finally, the cutting force is calculated according to the cutting thickness; and in the process parameter optimization step, whether the calculated cutting force is not greater than a design threshold is judged; if the calculated cutting force is not greater than the design threshold, the NC code, the cutting depth and a feed rate are taken as input parameters; and otherwise, an NC code is regenerated. In the method, the real machining conditions are reflected by utilizing a tool enveloping surface analytical expression and the obtained transient cutting thickness is more accurate, so that the accuracy of the calculation of the cutting thickness and the cutting force is improved, and reliable assurance is provided for precisely and efficiently machining a spatial curved surface.

Process Parameters Optimization of an Aluminium Alloy with Pulsed Gas Tungsten Arc Welding (GTAW) Using Gas Mixtures

Abstract: This paper demonstrates the enhancement of mechanical properties and effective optimization of pulsed GTAW process parameters on aluminium alloy 6061 using sinusoidal AC wave with argon plus helium gas mixtures. Modified Taguchi Method (MTM) was employed to formulate experimental layout and to study effect of process parameter optimization on mechanical properties of the weld joints. Microstructural characterization of weld joint was carried out to understand the structural property correlation with process parameters.

Experimental study on flow forming process of AISI 321 steel tube using the Taguchi method

Abstract: Flow forming is an effective process for the manufacturing of thin-walled seamless tubes. It has been found that a number of parameters affect the quality and dimensional precision of flow-formed tubes. In this study, the required flow forming tools are manufactured. The out-of-roundness of an annealed and flow-formed AISI 321 steel tubular pre-form is investigated for various levels of effective process parameters experimentally. Taguchi’s method is employed to design of experiments (DOE). The parameters considered are the feed rate, the depth of cut and the roller attack angle. The effects and contributions weight and interaction effects of these parameters on the out of roundness as response function are analysed. It is found that the depth of cut is the most important process parameter affecting out of roundness. The out-of-roundness decreases with increase in the depth of cut and it increases with increase in the feed rate and roller attack angle.

Modeling of Weld Bead Geometry and Shape Relationships in Submerged Arc Welding using Developed Fluxes

Abstract: To automate a welding process, which is the present trend in fabrication industry, it is essential that mathematical models have to be developed to relate the process variables to the weld bead parameters. Submerged arc welding (SAW) is characterized by its high reliability, deep penetration, smooth finish and high productivity especially for welding of pipes and boiler joints. In the present work mathematical models have been developed for SAW using developed fluxes. Response surface methodology has been used to predict critical dimensions of the weld bead geometry and shape relationships. The models developed have been checked for their adequacy and significance by using the F-test and the t-test, respectively. Main and interaction effects of the process variables on bead geometry and shape factors are presented in graphical form and using which not only the prediction of important weld bead dimensions and shape relationships but also controlling the weld bead quality by selecting appropriate process parameter values are possible.

Data mining-based plate shape control key process parameter optimization system

Abstract: The invention relates to a data mining-based plate shape control key process parameter optimization system, and belongs to the technical field of automatic control of cold continuous rolling plate shape. The data mining method is adopted for acquiring key process parameter optimization settings which can meet the good cold continuous rolling plate shape. The system is characterized by comprising the following functional modules: an actual data acquisition and storage module, a process data pre-processing module, a process data storage module, a process data correlation analysis module, a process data clustering analysis module, a process data association rule analysis module, an optimization result generating module and an optimization result application module. The system has the advantage that the good plate shape can be obtained by applying the plate shape control system. The method avoids endless theoretical research on plate shape control and fully utilizes actual plate shape control process data containing successful operation experience of field operators, and the plate shape process parameter settings which can obtain the good plate shape are acquired by data mining, so the plate shape qualification rate and the finished product rate of cold continuous rolled strip steel are improved.

Industrial process transmitter with high static pressure isolation diaphragm coupling

Abstract: A process transmitter for measuring a process variable comprises a sensor module and a static pressure coupling. The sensor module comprises a sensor for measuring a process variable of an industrial process and for generating a sensor signal. The sensor includes a hydraulic fluid inlet within the module. The static pressure coupling is connected to the sensor module and comprises a isolator fitting, a process fluid coupling and an isolation diaphragm. The isolator fitting is inserted into the sensor module and connected to the hydraulic fluid inlet. The process fluid coupling is joined to the isolator fitting. The isolation diaphragm is positioned between the isolator fitting and the process fluid coupling outside of the sensor module.

Optimization of micro metal injection molding with multiple performance characteristics using grey relational grade

Abstract: Micro metal injection molding (μMIM) which is a variant of MIM is a promising process towards near net-shape of metallic micro components of complex geometry. In this paper, μMIM is applied to produce 316L stainless steel micro components. Due to highly stringent characteristic of μMIM properties, the study has been emphasized on optimization of process parameter. Here, Taguchi method associated with Grey Relational Analysis (GRA) will be implemented as it represents novel approach towards investigation of multiple performance characteristics. Basic idea of GRA is to find a grey relational grade (GRG) which can be used for the optimization conversion from multi objectives case which are density and strength to a single objective case. After considering the form ‘the larger the better’, results show that the injection time(D) is the most significant followed by injection pressure(A), holding time(E), mold temperature(C) and injection temperature(B). Analysis of variance (ANOVA) is also employed to strengthen the significant of each parameter involved in this study.

Optimization of GMAW Protocols and Parameters for Improving Weld Strength Quality Applying the Taguchi Method

Abstract: The strength value most desired in any welding process is an excellent Ultimate Tensile Strength (UTS) of the weld, compared with the parent metal. Process parameters applied during the welding process ought to be subjected to continuous scrutiny and assessment because of the ever increasing demand for structural and industrial materials with weld joints possessing higher strength values. This study is intended to investigate the inadequacies of existing GMAW welding process parameters utilized by the investigated industrial firm in its signature welding protocol, by suggesting alternative, uniquely crafted, and improved process parameters to replace its existing signature welding protocol, thereby improving the weld results by attaining higher UTS. These suggested process parameters were thereafter subjected to reported literature, following which optimization was achieved by employing the Taguchi Method. From the analysis conducted by applying the Taguchi Method, an optimum process parameter of A3 B3 C1 D1, which consists of welding current of 240A, welding time of 2.0 mins, welding speed of 0.0062 m/s, and welding voltage of 33V, was suggested. These optimum parameters were found to have an improvement of 2.32dB of the S/N ratio, and 1.11 times over the UTS of the existing process parameters. This study elucidates a step by step approach for applying the Taguchi Method. The study also shows that the use of the Taguchi Method has successfully improved on the existing process parameters, giving the industrial firm a more efficient signature welding protocol.

Performance evaluation of coating materials and process parameters optimization for surface quality during turning of AISI 304 austenitic stainless steel

Abstract: Austenitic stainless steels are a widely used group of stainless steels. Problems have been reported by users during machining due to its low thermal conductivity, high work hardening, high strength, and high ductility. These made it difficult to machine the materials. The aim of the present study is to investigate the influence of Pressure Vapour Deposition (PVD) and Chemical Vapour Deposition (CVD) coated cemented carbide inserts on the surface quality of the work piece when turning on AISI 304 austenitic stainless steel work pieces, on computer numerical controlled (CNC) lathe. Taguchi’s Design of Experiments approach (DOE) is used to analyze the effect of process parameters on surface roughness to obtain their optimal setting. The analysis of variance (ANOVA) is employed to analyze the influence of process parameters during turning. The results have shown that the improvement in average surface finish is obtained when machining with PVD coated insert (1.13 im).The nose radius is the most significant process parameter (62.88% contribution) when turning with PVD insert. The cutting speed is the most significant factor (37.84% contribution) when turning with CVD insert. Optimal ranges of surface roughness values are also predicted.

Quality and productivity improvement using Six Sigma and Taguchi methods

Abstract: This research study proposes an innovative analysis for controlling the defects in aluminium die casting business. Six Sigma DMAIC (define, measure, analyse, improve, control) methodology is used to analyse the problem. Process validation is done with Taguchi DOE and ANOVA analysis at 95% confidence showed that the metal temperature is the vital process parameter causing defects. Confirmation experiment at optimum process parameter level showed defect level reduced from 17.22% to 4.8%. Since aluminium die casting is one of the most widely used process for producing automobile components, this study provides a methodology for improving the die casting business excellence.

Investigation of cutting parameters of surface roughness for a non-ferrous material using artificial neural network in CNC turning

Abstract: Surface roughness, an indicator of surface quality is one of the most specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and nonlinear. In this work, machining process was carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness was measured using Surface Roughness Tester. To predict the surface roughness, an artificial neural network (ANN) model was designed through back propagation network using Matlab 7 software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN was used confidently. The results obtained, conclude that ANN is reliable and accurate for solving the cutting parameter optimization. Key words: CNC turning process, non-ferrous material, surface roughness, artificial neural network (ANN), optimization.

Evaluating and controlling process variability in micro-injection moulding

Abstract: Microsystem technologies require relatively strict quality requirements. This is because their functionalities are usually dependent on stringent requirements of dimensions, masses or tolerances. When mass-producing micro-components, e.g. replication of disposable microfluidic diagnostics devices, the consistency of the produced components could be significantly affected by process variability. The variability could be associated with a specific process parameter or could be a result of process noise. This paper presents a methodology to assess and minimise process variability in micro-injection moulding, an example of well-established mass-production techniques for micro-components. A design-of-experiments approach was implemented, where five process parameters were investigated for possible effects on the process variability of two components. The variability was represented by the standard deviation of the replicated part mass. It was found that melt temperature was a significant source of variability in part mass for one of the components, whilst the other was affected by unsystematic variability. Optimisations tools such as response surfaces and desirability functions were implemented to minimise mass variability by more than 40%.

Variation-sensitive monitor circuits for estimation of Die-to-Die process variation

Abstract: We propose a set of variation-sensitive ring oscillators (RO) to estimate Die-to-Die process parameter variation. ROs are designed to have different sensitivity to each parameter variation. A method suitable to estimate variation from different ROs is proposed. We have fabricated test chip and successfully estimated process parameter variation. Variation results are correlated with that in Process Control Module data.

Parametric Optimization in Electrochemical Machining of EN-31 Steel Based on Grey Relation Approach

Abstract: Electrochemical machining is one of the widely used non-traditional machining processes to machine complicated shapes for electrically conducting but difficult-to-machine materials such as superalloys, Ti-alloys, alloy steel, tool steel, stainless steel, etc. Use of optimal ECM process parameters can significantly reduce the ECM operating, tooling, and maintenance cost and will produce components of higher accuracy. This paper investigates the effect and parametric optimization of process parameters for Electrochemical machining of EN-31 steel using grey relation analysis. The process parameters considered are electrolyte concentration, feed rate and applied voltage and are optimized with considerations of multiple performance characteristics including material removal rate, overcut and cylindricity error. Analysis of variance is performed to get contribution of each parameter on the performance characteristics and it was observed that feed rate is the significant process parameter that affects the ECM robustness. The experimental results for the optimal setting show that there is considerable improvement in the process. The application of this technique converts the multi response variable to a single response Grey relational grade and, therefore, simplifies the optimization procedure.

Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

Abstract: Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly from CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles espectively, these processes are based on additive principle for part fabrication. The biggest advantage of RP processes is that an entire 3-D (three-dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the mplexity of the part geometry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Out of all commercially available RP processes, fused deposition modelling (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present study focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in mechanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing parameters such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically validated models, microphotographs and non-traditional optimization methods. For improving dimensional accuracy of the part, Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control the part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing constraints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predictive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parameters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indicate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI. Assessment of process parameters on mechanical properties viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion within or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously.To address this issue, all the studied strengths are combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consideration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods such as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle.

Optimization of a product performance using mixture experiments including process variables

Abstract: This article presents a case study of a chemical compound used in the delay mechanism to start a rocket engine. The compound consists in a three-component mixture. Besides the components proportions, two process variables are considered. The aim of the study is to investigate the mix components proportions and the levels of process variables that set the expected delay time as close as possible to the target value and, at the same time, minimize the width of prediction interval for the response. A linear regression model with normal responses was fitted. Through the model developed, the optimal components proportions and the levels of the process variables were determined. For the model selection, the use of the backward method with an information criterion proved to be efficient in the case under study.