Showing papers on "Process variable published in 2001"

Control system using process model

Abstract: A control system is provided for controlling a process for making paper or paper pulp. The process has a process product output at an end of the process. The controls include a process variable sensor input configured to receive a process variable related to the process. The controller is configured to provide a control signal to control the process. A process model has a model product output which is a model representation of the process product output. The model process output is a function of the sensed process variable and a product output setpoint representative of a desired process product output. The control signal is a function of the product output setpoint and the modeled product output.

A process map for consistent build conditions in the solid freeform fabrication of thin-walled structures

Abstract: In solid freeform fabrication (SFF) processes involving thermal deposition, thermal control of the process is critical for obtaining consistent build conditions and in limiting residual stress-induced warping of parts. In this research, a nondimensionalized plot (termed a process map) is developed from numerical models of laser-based material deposition of thin-walled structures. This process map quantifies the effects of changes in wall height, laser power, deposition speed and part preheating on melt pool length, which is an essential process parameter to control in order to obtain consistent build conditions. The principal application of this work is to the Laser Engineered Net Shaping (LENS) process under development at Sandia Laboratories; however, the general approach and a subset of the presented results are applicable to any SFF process involving a moving heat source. Procedures are detailed for using the process map to predict melt pool length and predictions are compared against experimentally measured melt pool lengths for stainless steel deposition in the LENS process.

A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding

Abstract: Determination of the initial process parameters for injection moulding is a highly skilled task and is based on a skilled operator's know-how and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed approach was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly, from which good quality moulded parts can be produced without relying on experienced moulding personnel.

System and method for monitoring and control of processes and machines

Abstract: The disclosed invention includes a system and method for monitoring and control of processes and machines. A computer-implemented method for monitoring and controlling a process comprises storing process attribute information of objects in a plurality of databases, receiving at least one process measurement from a measurement device, similarity searching the at least one process measurement against the process attribute information stored in the databases, assigning a similarity score to the process measurement, comparing the similarity score to a match tolerance level, computing a process action for at least one process machine, via an algorithm having a process variable, replacing the process variable in the algorithm with the process measurement where the similarity score is equal to or greater than the match tolerance level, replacing the process variable in the algorithm with a set point where the similarity score is lower than the match tolerance level, and communicating the process action to a process machine.

Processing and mechanical properties evaluation of a commingled carbon-fibre/PA-12 composite ☆

Abstract: A carbon-fibre/PA-12, hybrid yarn, woven fabric material has been processed into laminates and mechanically tested to evaluate its suitability for high performance applications. The processing technique used was compression moulding. A process parameter investigation was carried out to determine the most suitable values of pressure, temperature and time at temperature required to economically produce laminates of high quality. Laminates were subsequently produced for a detailed material-testing programme. It was found that the hybrid yarn material exhibits excellent mechanical properties, particularly tension, open-hole tension, fracture and impact properties.

Method for an alarm event generator

Abstract: An alarm event generation method and apparatus for signaling an alarm ( 436, 1400 ) for a process control system ( 100 ). The process control system ( 100 ) monitors a production run using a computer system ( 108 ). The computer system ( 108 ) collects process data corresponding to the production run from operator workstations ( 104, 106 ) and stores the process data in a process data database ( 110 ). The process data includes a plurality of process parameter counts ( 430 ) each corresponding to a process parameter type ( 428 ). The process parameter counts ( 430 ) are polled from the computer system ( 108 ). Each process parameter count ( 430 ) has a corresponding manufacturing limit ( 434 ) to which it is compared. When a polled process parameter count ( 430 ) exceeds its corresponding manufacturing limit ( 434 ), an alarm ( 436, 1400 ) is signaled. While an alarm ( 436, 1400 ) is being signaled, the computer system ( 108 ) is prevented from collecting and storing process data.

Tensile Strengths for Laser-Fabricated Parts and Similarity Parameters for Rapid Manufacturing

Abstract: This paper presents a set of process parameter selection rules to deposit a good metal part. A CO 2 laser is used to melt metal powder to achieve layer by layer deposition for fabricating three-dimensional parts. Dimensionless numbers characterizing this powder deposition process are identified using Buckingham's Π-Theorem. These dimensionless numbers are used to identify a range of values for the process parameters, such as the laser power, spot diameter, speed of the xyz stage and powder flow rate, to achieve good quality layers for different materials. The yield and ultimate strengths are examined for parts fabricated with stainless steel 304 (SS 304) powder under three different processing conditions. These stresses are correlated to the operating conditions and physical dimensions of the deposit through the dimensionless similarity parameters. Experimental data indicate that the yield strength of the part is close to the value of standard sample (250 MPa, same as wrought stainless steel SS304). It is also observed that the direction of maximum yield strength is oriented very close to the dominant direction of material solidification. The ultimate strength is found to be considerably less than the ultimate strength of wrought SS 304 (540 MPa) which may be due to the residual stresses generated in the part.

Experimental design and performance analysis of alumina coatings deposited by a detonation spray process

Abstract: The increasing demands for high-quality coatings has made it inevitable that the surface coating industry would put more effort into precisely controlling the coating process. Statistical design of experiments is an effective method for finding the optimum spray parameters to enhance thermal spray coating properties. In the present investigation, an attempt is made to produce high-quality alumina (Al2O3) coatings by optimizing the detonation spray process parameters following a (L16-24) factorial design approach. The process parameters that were varied include the fuel ratio, carrier gas flow rate, frequency of detonations and spray distance. The coating characteristics were quantified with respect to roughness, hardness and porosity. The performance of the coatings was quantitively evaluated using erosion, abrasion and sliding wear testing. Through statistical analysis of the experimental results, performed by the ANOVA method, the significance of each process parameter together with an optimal variable combination was obtained for the desired coating attributes. Confirmation experiments were conducted to verify the optimal spray parameter combination, which clearly showed the possibility of producing high-quality Al2O3 coatings.

Neurofuzzy approach to process parameter selection for friction surfacing applications

Abstract: Friction surfacing is an advanced manufacturing process, which has been successfully developed and commercialised over the past decade. The process is used for corrosion and wear resistant coatings and for reclamation of worn engineering components. At present, the selection of process parameters for new coating materials or substrate geometries experimentally requires lengthy development work. The major requirement is for the flexibility to enable rapid changes of process parameters in order to develop new applications, with variations of materials and geometries in a cost effective and reliable manner. Further improvement requires development of appropriate mathematical models of the process, which will facilitate the introduction of optimisation techniques for efficient experimental work as well as the introduction of real time feedback adaptive control. This paper considers the use of combined artificial intelligence and modelling techniques. It includes a new frame of a Neurofuzzy-model based Decision Support System — FricExpert, which is aimed at speeding up the parameter selection process and to assist in obtaining values for cost effective development. Derived models can then be readily used for optimisation techniques, discussed in our earlier work.

Method for analyzing cause for quality degradation

Abstract: PROBLEM TO BE SOLVED: To indicate immediately the process variable responsible for process anomaly or quality degradation even in the presence of multiple process variables including some correlated with each other in a method wherein quality degradation is attributed to a process variable. SOLUTION: Process data are retrieved and collected, and the main component is analyzed for the calculation of the residual variable and the distance variable. When the residual variable is out of the tolerance limit, the degrees of contribution of the respective process variables to the residual variable are calculated and compared, and a process variable with a great contribution is extracted as a candidate cause for quality degradation. When the distance variable is out of the tolerance limit, the degrees of contribution of the respective score variables to the distance variable are calculated and compared, and a score variable with a great contribution is extracted as a candidate cause for quality degradation. Furthermore, the degrees of contribution of the respective process variables to the extracted score variable are calculated and compared, and the process variable with a great contribution to the extracted score variable is extracted as the final candidate cause for quality degradation.

Method and system for controlling setpoints of manipulated variables for process optimization under constraint of process-limiting variables

Abstract: A system, method, and article of manufacture suitable for determining setpoints of the control variables to optimize the process while taking into account the process-limiting variables in applications where responses are highly non-linear. In a preferred embodiment, the method comprises determining an actual rate of change of a performance limiting process parameter; calculating a predicted rate of change for the performance limiting process parameter for a predetermined future time interval; and adjusting a setpoint for the control variables to optimize the process while taking into account the performance limiting process parameter using the actual rate of change and the predicted rate of change.

Methods and systems for reducing waste and emissions from industrial processes

Abstract: Methods and systems are disclosed for reducing wastes produced from an industrial process. One embodiment describes communicating with a communications network and acquiring process information concerning the industrial process. A process parameter, associated with a predicted waste output, is predicted according to a predetermined model and based on the process information. The process parameter is then communicated over the communications network.

Process optimisation using Taguchi methods of experimental design

Abstract: Experimental design (ED) is a powerful technique which involves the process of planning and designing an experiment so that appropriate data can be collected and then analysed by statistical methods, resulting in objective and valid conclusions. It is an alternative to the traditional inefficient and unreliable one‐factor‐at‐a‐time approach to experimentation, where an experimenter generally varies one factor or process parameter at a time keeping all other factors at a constant level. This paper presents a step‐by‐step approach to the optimisation of a production process (of retaining a metal ring in a plastic body by a hot forming method) through the utilisation of Taguchi methods of experimental design. The experiment enabled the behaviour of the system to be understood by the engineering team in a short period of time and resulted in significantly improved performance (with the opportunity to design further experiments for possible greater improvements).

Intelligent process control system for quality improvement by data mining in the process industry

Abstract: The large amount of bulky and noisy shop floor data is one of the characteristics of the process industry These data should be effectively processed to extract working knowledge needed for the enhancement of productivity and the optimization of quality The objective of the chapter is to present an intelligent process control system integrated with data mining architecture in order to improve quality The proposed system is composed of three data mining modules performed in the shop floor in real time: preprocessing, modeling, and knowledge identification To consider the relationship between multiple process variables and multiple quality variables, the Neural-Network/Partial Least Squares (NNPLS) modeling method is employed For our case study, the proposed system is configured as three control applications: feedback control, feed-forward control, and in-process control, and then applied to the shadow mask manufacturing process The experimental results show that the system identifies the main causes of quality faults and provides the optimized parameter adjustments

Designing heterogeneity into bimodal elastomeric PDMS networks

Abstract: Bimodal poly(dimethylsiloxane) (PDMS) networks prepared using a dual-step process have been studied. It is shown that these networks exhibit a significant increase in equilibrium modulus. This reinforcement can be obtained by adjusting a process parameter, the heterogeneity time, during network preparation. The increase in modulus is attributed to the clustering of the short chains that is effected through proper use of heterogeneity time.

Impact analysis of process variability on digital circuits with performance limited yield

Abstract: In this paper we present a framework for the statistical design analysis of large logic circuits. It allows one to accurately predict and analyze the impact of process variations on relevant circuit performance parameters, such as critical path delay, clock skew and signal race conditions. The proposed statistical design analysis system is based on the efficient generation of linearized models for the stage delay sensitivities to Front End Of Line (FEOL) and Back End Of Line (BEOL) process parameter variations. By using a set of intermediate RSM representations, the actual underlying FEOL/BEOL process parameters are mapped into an auxiliary set of macro-parameters, such as NMOS and PMOS I/sub DSS/ and V/sub TH/, which can be more easily observed and controlled by process and device engineers. In this way the linearized performance models can be applied to generate product speed-yield maps as functions of the above set of controllable and observable macro-parameters, which can be finally used to center the process and optimize product yield.

Method for measuring the control performance provided by an industrial process control system

Abstract: A method for determining the measure of control provided to a process by a control system. The determines process model parameters for a simple and complex model of the process and uses those parameters along with the value of the process variable and the final control element position to predict the off control data. The method also uses the process model parameters to determine the optimal tuning and then forecasts the optimal process performance from the predicted off control data and the determined optimal tuning.

Process and apparatus for preparing a composition of matter utilizing a side stream ultrasonic device

Abstract: The properties of a resinous material product are controlled in a manufacturing system by online process parameter monitoring and control. The online monitoring and control incorporates an in-situ measurement system that can monitor product in the process by use of a side stream ultrasonic device. The side stream device advantageously provides online real-time measures of the product's acoustical properties (e.g. velocity, attenuation) under conditions that are independent of process-stream conditions. The side stream device controls the product temperature, pressure, and flow rate while inside the side stream device and the velocity and attenuation are measured under these predetermined temperature, pressure, and flow rate conditions. The acoustical properties (e.g. velocity, attenuation) of the product, are used to predict the properties of the product, and provides the process control system with analysis of the acoustical properties using derived relationships between the physical properties of the product and the acoustical properties. Differences between the predicted and desired product properties are used to control process parameters. The process can be used for a variety of chemical process plants.

Process for monitoring a process, planarizing a surface, and for quantifying the results of a planarization process

Abstract: A process for quantifying the results of a planarization process provides metrics that can be applied to process parameters that affect the localized planarization results. A surface of a work piece is planarized and the amount of material removed as a function of the location on the surface of the work piece is measured and recorded. The data from the measurements are fitted to a first polynomial to construct an approximation of a low spatial frequency scan. The work piece surface is then divided into a plurality of regions, each of the regions influenced by a planarization process variable. The approximation of the low spatial frequency scan is then analyzed in each of the regions by fitting the low spatial frequency scan in that region to an nth order polynomial. The coefficient of the nth order term is used as a metric for the results in that region.

A study on development of a new algorithm for predicting the process variables in GMA welding processes

Abstract: Gas Metal Arc (GMA) welding is extensively employed in the metal industries to weld a variety of ferrous and non-ferrous metals because of its potential for increasing the productivity and quality of welding which is controlled by the process parameters The objective of this paper is to develop the algorithm that enables the determination of process variables from the optimized bead geometry for robotic GMA welding It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process variables and the bead dimensions using the least square method The method directly determines those variables which will give the desired set of bead geometry This avoids the need to iterate with a succession of guesses employed Finite Element Method(FEM) These results suggest that process variable from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time

Method to reduce lot-to-lot variation of array threshold voltage in a DRAM device

Abstract: A new method to reduce variation in an output parameter by selection of an optimal process recipe in the manufacture of an integrated circuit device is achieved. The method may be used to reduce the array voltage threshold in a DRAM circuit by compensating the source/drain ion implantation by calculating a predicted array voltage threshold. The integrated circuit device wafer is measured to obtain a present set of process parameter values. A predicted value of an output parameter is calculated by evaluating a first equation at the present set of process parameter values. The first equation is derived from a plurality of previous sets of process parameter values and the corresponding plurality of sets of output parameter values. The difference between the predicted value of the output parameter and a target value of the output parameter is the output parameter delta. A process recipe offset is calculated by evaluating a second equation at the output parameter delta. The second equation is derived from the plurality of selectable process recipes and the plurality of corresponding output parameter values. An optimal process recipe is selected from the plurality of selectable process recipes by adding the process recipe offset to the target process recipe to compensate the output parameter and to thereby reduce the variation.

Method for monitoring the measurement of process variables in a repeating batch process, involves measuring and recording expected behavior and then setting operating limits outside which a warning is generated

Abstract: The method has the following steps: measurement and recording of a representative expected behavior curve of a process variable measurement value (M) over time during at least one batch cycle; setting of a limit window (F) based on the expected behavior curve; continuous measurement of the variable over time during subsequent batch cycles to determine if the limit window is exceeded; and generation of a warning if the measurement value lies outside the expected range. The invention also relates to a corresponding measurement device with a microprocessor controlled measurement unit for implementation of the inventive method.

Method and equipment for automatic control of process variables of IC engine, provides decoupling of individual control signal or different process variables

Abstract: A controller controls more than one process variable of an IC engine (1). It can e.g. have one control input (33) determining the duration of fuel delivery which governs the cylinder pressure and a second input (34) which controls the time of maximum pressure based on the angular position of the crankshaft. Consequently this represents a coupled multi-variable system. The invention proposes that decoupling circuits (25,26) are provided so that a specific control signal only acts only on it's related process variable.

Method of estimating the properties of a polymer product

Abstract: The present invention relates to a method of estimating the properties of a polymer product by using converted process variables (cPV's) which means process variable that final or intermediate product experienced earlier in average in the reactors by stage with respect to the final product, which incorporates residence time distribution to process variables, wherein said method comprises the following steps of: computing cPV's by incorporating the residence time distribution to said process variables by means of theoretically determining the amounts of content and discharge of the product in the process, and then solving the balance equations of a hypothetical substance by taking the respective process variables as those of the hypothetical substance: and estimating the properties of the product by inputting to various property estimation models cPV's in the reactors by stage with respect to the final product after incorporating the residence time distribution to said process variables.

Advanced statistical process control: controlling sub-0.18-μm lithography and other processes

Abstract: Feed-forward, as a method to control the Lithography process for Critical Dimensions and Overlay, is well known in the semiconductors industry. However, the control provided by simple averaging feed-forward methodologies is not sufficient to support the complexity of a sub-0.18micrometers lithography process. Also, simple feed-forward techniques are not applicable for logics and ASIC production due to many different products, lithography chemistry combinations and the short memory of the averaging method. In the semiconductors industry, feed-forward control applications are generally called APC, Advanced Process Control applications. Today, there are as many APC methods as the number of engineers involved. To meet the stringent requirements of 0.18 micrometers production, we selected a method that is described in SPIE 3998-48 (March 2000) by Terrence Zavecz and Rene Blanquies from Yield Dynamics Inc. This method is called PPC, Predictive Process Control, and employs a methodology of collecting measurement results and the modeled bias attributes of expose tools, reticles and the incoming process in a signatures database. With PPC, before each lot exposure, the signatures of the lithography tool, the reticle and the incoming process are used to predict the setup of the lot process and the expected lot results. Benefits derived from such an implementation are very clear; there is no limitation of the number of products or lithography-chemistry combinations and the technique avoids the short memory of conventional APC techniques. ... and what's next? (Rob Morton, Philips assignee to International Sematech). The next part of the paper will try to answer this question. Observing that CMP and metal deposition significantly influence CD's and overlay results, and even Contact Etch can have a significant influence on Metal 5 overlay, we developed a more general PPC for lithography. Starting with the existing lithography PPC applications database, the authors extended the access of the analysis to include the external variables involved in CMP, deposition etc. We then applied yield analysis methods to identify the significant lithography-external process variables from the history of lots, subsequently adding the identified process variable to the signatures database and to the PPC calculations. With these improvements, the authors anticipate a 50% improvement of the process window. This improvement results in a significant reduction of rework and improved yield depending on process demands and equipment configuration. A statistical theory that explains the PPC is then presented. This theory can be used to simulate a general PPC application. In conclusion, the PPC concept is not lithography or semiconductors limited. In fact it is applicable for any production process that is signature biased (chemical industry, car industry, .). Requirements for the PPC are large data collection, a controllable process that is not too expensive to tune the process for every lot, and the ability to employ feedback calculations. PPC is a major change in the process management approach and therefor will first be employed where the need is high and the return on investment is very fast. The best industry to start with is the semiconductors and the most likely process area to start with is lithography.

Method and device for system and/or process monitoring

Abstract: The invention relates to a method and a device for system and/or process monitoring linked to a measuring device, which determines/monitors at least one process variable of a medium (12). The aim of the invention is to disclose a method and a device, which provide information relating to the current and future functional capacity of a measuring device or individual components of said measuring device (1). To achieve this, according to the method, the temperature values (T) of the medium (12) are directly or indirectly determined and a trend analysis, which relates to the thermal stress of the measuring device (1) or to the thermal stress of individual components of said measuring device (1), is carried out, based on the temperature values (T) of the medium (3) thus determined, or based on derivations of the determined temperature values (3) of the medium.

Method and device for controlling an electrical load

Abstract: A method and a device for controlling an electrical load. A quantity is ascertained which is a function of the temperature of the load or which characterizes the temperature of the load. The quantity is specifiable on the basis of a temperature variable and a current variable. A first filter takes into account the influence of the temperature variable on the quantity, and a second filter takes into account the influence of the current flowing through the load.

Open and closed loop flow control system and method

Abstract: A method and system are taught for controlling an operating process. The method comprises measuring an operating process parameter, determining whether the operating process parameter is within a predetermined dead band, using a closed loop controller when the operating process parameter is within the predetermined dead band, and using an open loop controller when the operating process parameter is not within the predetermined dead band. These steps are repeated until the operating process is completed. Closed loop control uses a first model in conjunction with a plurality of configurable constants that are associated with a plurality of physical parameters of the process being controlled. Open loop control uses a second model in conjunction with a plurality of configurable constants that are associated with a plurality of physical parameters of the process being controlled.