Showing papers on "Process variable published in 2009"

Multi-response optimization of EDM with Al-Cu-Si-TiC P/M composite electrode

Abstract: The present study investigates the relationship of process parameters in electro-discharge of CK45 steel with novel tool electrode material such as Al–Cu–Si–TiC composite produced using powder metallurgy (P/M) technique. The central composite second-order rotatable design had been utilized to plan the experiments, and response surface methodology (RSM) was employed for developing experimental models. Analysis on machining characteristics of electrical discharge machining (EDM) die sinking was made based on the developed models. In this study, titanium carbide percent (TiC%), peak current, dielectric flushing pressure, and pulse on-time are considered as input process parameters. The process performances such as material removal rate (MRR) and tool wear rate (TWR) were evaluated. Analysis of variance test had also been carried out to check the adequacy of the developed regression models. Al–Cu–Si–TiC P/M electrodes are found to be more sensitive to peak current and pulse on-time than conventional electrodes. The observed optimal process parameter settings based on composite desirability are TiC percent of 18%, peak current of 6 A, flushing pressure of 1.2 MPa, and pulse on-time of 182 μs for achieving maximum MRR and minimum TWR; finally, the results were experimentally verified. A good agreement is observed between the results based on the RSM model and the actual experimental observations. The error between experimental and predicted values at the optimal combination of parameter settings for MRR and TWR lie within 7.2% and 4.74%, respectively.

Process parameter optimization for MIMO plastic injection molding via soft computing

Abstract: Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the process parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding process. The proposed approach integrates Taguchi's parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the process parameters. The research results indicate that the proposed approach can effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs.

Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm

Abstract: Selective laser sintering (SLS) is an attractive rapid prototyping (RP) technology capable of manufacturing parts from a variety of materials. However, the wider application of SLS has been limited, due to their accuracy. This paper presents an optimal method to determine the best processing parameter for SLS by minimizing the shrinkage. According to the nonlinear and multitudinous processing parameter feature of SLS, the theory and the algorithms of the neural network are applied for studying SLS process parameters. The process is modeled and described by neural network based on experiment. Moreover, the optimum process parameters, such as layer thickness, hatch spacing, laser power, scanning speed, work surroundings temperature, interval time, and scanning mode are obtained by adopting the genetic algorithm based on the neural network model. The optimum process parameters will be benefit for RP users in creating RP parts with a higher level of accuracy.

Optimization of quality characteristics parameters in a pulsed metal inert gas welding process using grey-based Taguchi method

Abstract: Optimization of a manufacturing process has to take into accounts all of the factors that influence the product quality and productivity. Optimization of welding process parameters is considerably complex because welding is a multi-variable process, which is influenced by a lot of process uncertainties. In this paper, a grey-based Taguchi method has been adopted to optimize the pulsed metal inert gas welding process parameters. Many quality characteristic parameters are combined into one integrated quality parameter by using grey relational grade or rank. The welding process parameters considered in this analysis are pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate, and table feed rate. The quality parameters considered are the tensile strength, bead geometry, transverse shrinkage, angular distortion, and deposition efficiency. Analysis of variance has been performed to find out the impact of individual process parameter on the quality parameters. If the tensile strength as the most important quality parameter is assigned a higher weight, then the pulse voltage was found to be the most influential process parameter. Experiments with the optimized parameter settings, which have been obtained from the analysis, are given to validate the results.

Adaptive and optimal control of a non-linear process using intelligent controllers

Abstract: A time-optimal control for set point changes and an adaptive control for process parameter variations using neural network for a non-linear conical tank level process are proposed in this work. Time-optimal level control was formulated using dynamic programming algorithm and basic properties of the solutions were analysed. It was found that the control is of bang-bang type and there is only one switching. In this method, a mathematical step-by-step procedure is used to obtain the optimal valve position path with one switching and is trained by neural network, based on the back-propagation algorithm. The dynamic programming procedure allows the set point to be reached as fast as possible without overshoot. An adaptive system is also designed and proved to be useful in adjusting the trained parameter of the dynamic programming based neural network for the process parameter variations. A prototype of conical tank level system has been built and implementation of dynamic programming based neural network control algorithm for set point changes and implementation of adaptive control for process parameter variations are performed. Finally, the performance is compared with conventional control. The results prove the effectiveness of the proposed optimal and adaptive control schemes.

Multiperformance Optimization in Turning of Free-Machining Steel Using Taguchi Method and Utility Concept

Abstract: This article presents the application of Taguchi method and the utility concept for optimizing the machining parameters in turning of free-machining steel using a cemented carbide tool. A set of optimal process parameters, such as feed rate, cutting speed, and depth of cut on two multiple performance characteristics, namely, surface roughness and metal removal rate (MRR) is developed. The experiments were planned as per L9 orthogonal array. The optimal level of the process parameters was determined through the analysis of means (ANOM). The relative importance among the process parameters was identified through the analysis of variance (ANOVA). The ANOVA results indicated that the most significant process parameter is cutting speed followed by depth of cut that affect the optimization of multiple performance characteristics. The confirmation tests with optimal levels of machining parameters were carried out to illustrate the effectiveness of Taguchi optimization method. The optimization results revealed that a combination of higher levels of cutting speed and depth of cut along with feed rate in the medium level is essential in order to simultaneously minimize the surface roughness and to maximize the MRR.

Optimization of high-pressure die-casting process parameters using artificial neural network

Abstract: High-pressure die casting is a versatile process for producing engineered metal parts. There are many attributes involved which contribute to the complexity of the process. It is essential for the engineers to optimize the process parameters and improve the surface quality. However, the process parameters are interdependent and in conflict in a complicated way, and optimization of the combination of processes is time-consuming. In this work, an evaluation system for the surface defect of casting has been established to quantify surface defects, and artificial neural network was introduced to generalize the correlation between surface defects and die-casting parameters, such as mold temperature, pouring temperature, and injection velocity. It was found that the trained network has great forecast ability. Furthermore, the trained neural network was employed as an objective function to optimize the processes. The optimal parameters were employed, and the castings with acceptable surface quality were achieved.

Online control of the injection molding process based on process variables

Abstract: The conventional control of the injection molding process is based on machine variables, which cannot sufficiently characterize the course of the process. Hence, a system that controls the injection molding process based on process variables has been developed at the Institute of Plastics Processing at RWTH Aachen University during the last years. It controls the quality determining process variable cavity pressure directly and realizes a desired course of cavity pressure in the injection and holding pressure phases. The cavity pressure course in the holding pressure phase is controlled online on the basis of pvT behavior of the processed plastic material. Thus, an optimal course of the process in the pvT diagram can be guaranteed and the quality constancy of the molded parts can be clearly increased. Using the pvT-based process control, the effect of varying mold and melt temperatures on the molded part weight can be decreased by about 90% compared with the conventional process control. © 2009 Wiley Periodicals, Inc. Adv Polym Techn 28:65–76, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/adv.20153

Laser cutting system for cutting a workpiece with a laser beam at a variable cutting speed

Abstract: The laser cutting system for cutting a workpiece with a laser beam along a cutting line at a variable cutting speed includes a movable machining head for placing the laser beam on the respective workpiece, a user interface for specifying the respective cutting line and for specifying a minimum path accuracy of the laser beam, and a control device for controlling a movement of the machining head along the cutting line relative to the respective workpiece and for controlling a plurality of process variables of a cutting process. A second subset of the process variables comprises exclusively one or more process variables which have no influence on the power of the laser beam available for cutting. At least one process variable of the second subset can be controlled by way of the control device as a function of at least one variable control parameter.

An integrated parameter optimization system for MISO plastic injection molding

Abstract: This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L25 orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.

Industrial process device utilizing piezoelectric transducer

Abstract: A process device for coupling to an industrial process for use in monitoring or controlling the process includes a device housing configured to physically couple to the industrial process. A process variable sensor is configured to measure a process variable and measurement circuitry coupled to the process variable sensor provides an output related to the sensed process variable. A piezoelectric transducer provides an electrical output related to pressure pulsations in the industrial process. Electrical circuitry in the housing includes an input configured to receive the electrical output from the piezoelectric sensor.

An evaluation of process-parameter and part-geometry effects on the quality of filling in micro-injection moulding

Abstract: This paper addresses the use of micro-injection moulding for the fabrication of polymeric parts with microfeatures. Five separate parts with different micro-feature designs are moulded of Polymethylmethacrylate. The design-of-experiments approach is applied to correlate the quality of the parts to the processing parameters. Five processing parameters are investigated using a screening half-factorial experimentation plan to determine their possible effect on the filling quality of the moulded parts. The part mass is used as an output parameter to reflect the filling of the parts. The experiments showed that the holding pressure is the most significant processing parameter for all the different shapes. In addition, the experiments showed that the geometry of the parts plays a role in determining the significant processing parameters. For a more complex part, injection speed and mould temperature became statistically significant. A desirability function approach was successfully used to improve the filling quality of each part.

Non-Periodic Control Communications in Wireless and Other Process Control Systems

Abstract: Disclosed is a controller having a processor and a control module adapted for periodic execution by the processor and configured to be responsive to a process variable to generate a control signal for a process. An iteration of the periodic execution of the control module involves implementation of a routine configured to generate a representation of a process response to the control signal. The routine is further configured to maintain the representation over multiple iterations of the periodic execution of the control module and until an update of the process variable is available. In some cases, the update of the process variable is made available via wireless transmission of the process signal. In those and other cases, the controller may be included within a process control system having a field device to transmit the process signal indicative of the process variable non-periodically based on whether the process variable has changed by more than a predetermined threshold. In some embodiments, the field device also transmits the process signal if a refresh time has been exceeded since a last transmission.

Process, voltage, and temperature sensor

Abstract: An integrated circuit includes a process sensor, a temperature sensor, and a voltage sensor. The process sensor is configured to sense a process parameter indicative of a semiconductor process by which the integrated circuit is formed and, based upon the sensed process parameter, to provide a characterization of the semiconductor process to the output of the process sensor. The temperature sensor is configured to provide an indication of a temperature of the integrated circuit to an output of the temperature sensor and the voltage sensor is configured to provide an indication of a power supply voltage level of the integrated circuit to an output of the voltage sensor. The output of the process sensor is coupled to at least one of the temperature sensor and the voltage sensor to compensate at least one of the indication of the temperature and the indication of the power supply voltage level.

On-line quality evaluation and real-time intelligent control method for tobacco process parameter

Abstract: The invention discloses a method for online quality evaluation and real-time intelligent control of a parameter during a process for processing tobacco. The method comprises the following steps: a parameter value between an operating parameter of raw material processing equipment and inner quality of a processed product is preset through a data management system; online acquisition is carried out in real time through a data management acquisition system; a parameter acquired by real-time online acquisition is compared with the preset parameter value through an intelligent control system of a data management layer; a valve, a draught fan and an air door power element on the equipment and parameter regulation of tobacco strip remoistening and rotating speed of a conditioning cylinder can be controlled through regulating the operating parameter of the PLC control system and a setting value of a PID open-close loop automation control parameter, thereby realizing accurate and dynamic control of raw material quality variation tendency during a tobacco online processing process and ensuring that the inner quality of the product can be controlled in an excellent and stable range in real time. The method is applicable to control of inner index in a tobacco primary processing line, a tobacco beating and redrying line, a CO2 swelling tobacco line and a rolling, connecting and packaging processing process.

Industrial process power scavenging device and method of deriving process device power from an industrial process

Abstract: A process device (202) includes a fluid disruption generation element (210) to generate a fluid disruption within process fluid flowing through a pipe associated with an industrial process and a process variable sensor coupled to the disruption generation element (210) to measure a process parameter. The process device (202) further includes a power generation element (212) adapted to generate an electrical output signal in response to the fluid disruption and a power storage component (226) coupled to the power generation element (212). The power storage component (226) is adapted to accumulate a charge based on the electrical output signal.

Taguchi Method Based Process Space for Optimal Surface Topography by Finish Hard Milling

Abstract: Hard milling has a potential to replace finish grinding in manufacturing dies and molds. Surface finish is one key surface integrity parameter to justify the use of hard milling. In this study a Taguchi design-of-experiment based finish milling hardened AISI H13 tool steel (50 ±1 HRc) with physical vapor deposition (PVD) (Ti, Al) N―TiN-coated end mill was conducted to investigate the optimal surface topography and roughness. A kinematic model of the cutting tool loci was developed to investigate the formation mechanism of the surface texture and correlate the simulated surface textures with the measured ones. The milled 3D surface topography and anisotropic roughness in the feed and step-over directions were thoroughly characterized and analyzed. The milled surface roughness R a of less than 0.1 μm in the feed direction and 0.15 μm in the step-over direction has shown that hard milling is capable of replacing grinding as a finish or semifinish process. Furthermore, the process parameter spaces for the desired surface properties were indentified via the surface contour maps.

ANN model for the prediction of density in Selective Laser Sintering

Abstract: The effects of process parameters on density of the part prepared by Selective Laser Sintering (SLS) were modelled, using an Artificial Neural Network (ANN) with a feed forward topology and a back propagation algorithm. The inputs of the ANN are the process parameters, including layer thickness, hatch spacing, laser power, scanning speed, temperature of working environment, interval time and scanning mode. The output of the ANN is the density. The experimental investigation results show that the ANN model may be used to analyse the relationship between the process parameters and the density of the SLS part quantitatively. [Received 12 September 2007; Revised 14 March 2009; Accepted 24 March 2009]

Modeling of process parameter selection with mathematical logic for process planning

Abstract: Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.

Optimization of high speed turning parameters of superalloy Inconel 718 material using Taguchi technique

Abstract: Superalloy, Inconel 718 is widely used in sophisticated applications due to its unique properties desired for the engineering applications. Due to its peculiar characteristics machining of Superalloy Inconel 718 is difficult and costly. The present work is an attempt to make use of Taguchi optimization technique to optimize cutting parameters during high speed turning of Inconel 718 using tungsten carbide cutting tool. Taguchi method is a powerful design of experiments (DOE) tool for engineering optimization of a process. It is an important tool to identify the critical parameters and predict optimal settings for each process parameter. Analysis of variance (ANOVA) is used to study the effect of process parameters and establish correlation among the cutting speed, feed and depth of cut with respect to the major machinability factor, cutting forces such as cutting force and feed force. Validations of the modeled equations are proved to be well within the agreement with the experimental data.

Modelling, analysis and optimisation of weld bead parameters of nickel based overlay deposited by plasma transferred arc surfacing

Abstract: Purpose: Plasma Transferred Arc surfacing is increasingly used in applications where enhancement of wear, corrosion and heat resistance of materials surface is required. The shape of weld bead geometry affected by the PTA Welding process parameters is an indication of the quality of the weld. In the paper the modelling, analysis and optimization of weld bead parameters of nickel based overlay deposited by plasma transferred arc surfacing are made. Design/methodology/approach: The experiments were conducted based on a five factor, five level central composite rotatable design and a mathematical model was developed using multiple regression technique. The direct and interaction effects of input process parameters of PTA Hardfacing on weld bead geometry are discussed. Finally, Microsoft Excel Solver has been used to optimize the process parameter with a view to economize the powder and achieve the desirable bead dimensions. Findings: Penetration, dilution and total area are increased when the welding current is increased but reinforcement marginally increases and then decreases. Penetration, weld width, dilution and total area decrease when travel speed is increased. Reinforcement increases slightly and then decreases. Practical implications: The developed mathematical models can be used to predict the dimensions of the weld bead and dilution. Originality/value: This paper highlights the development of a mathematical model correlating various process parameters to weld bead geometry in PTA hardfacing of Colmonoy 5, a Nickel based alloy over Stainless steel 316 L plates.

Pressure-based diagnostic system for process transmitter

Abstract: A process transmitter for measuring a process variable in an industrial process comprises a gauge pressure sensor, an excitation source and transmitter circuitry. The gauge pressure sensor measures a pressure difference between a process fluid and a reference volume, and generates a pressure sensor signal representing the pressure difference. The excitation source generates a pressure pulse within the reference volume to influence generation of the pressure sensor signal. The transmitter circuitry is connected to the gauge pressure sensor to provide an output related to a change in the pressure sensor signal due to the pressure pulse.

Study of tube hydroforming in a trapezoid-sectional die

Abstract: Trapezoid-sectional parts are used in many areas. In the present paper, the hydroforming process of the tube in a trapezoid-sectional die is investigated experimentally. Effects of loading paths on the hydro-formability of trapezoid-sectional parts are studied and the design principle of loading paths for trapezoid-sectional parts is put forward. Through numerical simulations, the effects of die angles and friction coefficients on the hydroforming process and the final parts are explored. The variation regularity of thickness on the unsupported wall along the cross-sections is analyzed and the simulation results show good agreement with the analytical conclusions. The present study provides a knowledge base for hydroforming die and process parameter design and helps to further understand the corner-filling process.

Method for monitoring workpiece machining process in machining tool, involves outputting alarm and changing machining process speed, if actual process variable-measured values are not depending on statistic variation of process variable

Abstract: The method involves iteratively determining process variable-measured values as a function of control variable, so as to receive a set of process variable-characteristics. An expected value assigned to each value of the control variable is determined from a measured value-amount at the process variable-measured values. A determination is made whether the process variable-measured values depend on a statistic variation of the process variable. An alarm is outputted, and processing speed of the machining process is changed, if the measured values are not depending on the statistic variation. An independent claim is also included for a controller of a machine tool.

Cutting tool mode parameter uncertain curve five-shaft numerical control process parameter optimizing method

Abstract: The invention provides a method for optimizing uncertain curve-surface five-axis numerical control process parameter of the cutter modal parameters, belonging to the technical field of computer numerical control machining. The method for optimizing the machining process parameter comprises the steps as follows: firstly, the uncertain area of the cutter system modal parameter is obtained; a five-axis milling machining dynamic model is established; the input parameter of the model comprises a cutter system modal parameter area, a cutting force coefficient, a cutter geometry and a cutter path; the five-axis milling machining vibration stabilization curve is calculated; and by taking the five-axis milling machining vibration stabilization curve as a constraint, the process parameter optimization model is established and worked out by a sequence nonlinear planning method so as to obtain the optimized process parameter. As the invention considers the uncertainty of the cutter system modal parameter and is closer to the real machining condition, thus improving the exactness of the vibration prediction during the machining.