Process variable

About: Process variable is a research topic. Over the lifetime, 3983 publications have been published within this topic receiving 43130 citations. The topic is also known as: process parameter.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes.

Abstract: Understanding process parameter interactions and their effects on mammalian cell cultivations is an essential requirement for robust process scale-up. Furthermore, knowledge of the relationship between the process parameters and the product critical quality attributes (CQAs) is necessary to satisfy quality by design guidelines. So far, mainly the effect of single parameters on CQAs was investigated. Here, we present a comprehensive study to investigate the interactions of scale-up relevant parameters as pH, pO2 and pCO2 on CHO cell physiology, process performance and CQAs, which was based on design of experiments and extended product quality analytics. The study used a novel control strategy in which process parameters were decoupled from each other, and thus allowed their individual control at defined set points. Besides having identified the impact of single parameters on process performance and product quality, further significant interaction effects of process parameters on specific cell growth, specific productivity and amino acid metabolism could be derived using this method. Concerning single parameter effects, several monoclonal antibody (mAb) charge variants were affected by process pCO2 and pH. N-glycosylation analysis showed positive correlations between mAb sialylation and high pH values as well as a relationship between high mannose variants and process pH. This study additionally revealed several interaction effects as process pH and pCO2 interactions on mAb charge variants and N-glycosylation pattern. Hence, through our process control strategy and multivariate investigation, novel significant process parameter interactions and single effects were identified which have to be taken into account especially for process scale-up.

Statistical Characterization of Ultrasonic Additive Manufacturing Ti/Al Composites

Abstract: Ultrasonic additive manufacturing (UAM) is an emerging solid-state fabrication process that can be used for layered creation of solid metal structures. In UAM, ultrasonic energy is used to induce plastic deformation and nascent surface formation at the interface between layers of metal foil, thus creating bonding between the layers. UAM is an inherently stochastic process with a number of unknown facets that can affect the bond quality. In order to take advantage of the unique benefits of UAM, it is necessary to understand the relationship between manufacturing parameters (machine settings) and bond quality by quantifying the mechanical strength of UAM builds. This research identifies the optimum combination of processing parameters, including normal force, oscillation amplitude, weld speed, and number of bilayers for the manufacture of commercially pure, grade 1 titanium+1100-O aluminum composites. A multifactorial experiment was designed to study the effect of the above factors on the outcome measures ultimate shear strength and ultimate transverse tensile strength. Generalized linear models were used to study the statistical significance of each factor. For a given factor, the operating levels were selected to cover the full range of machine capabilities. Transverse shear and transverse tensile experiments were conducted to quantify the bond strength of the builds. Optimum levels of each parameter were established based on statistical contrast trend analyses. The results from these analyses indicate that high mechanical strength can be achieved with a process window bounded by a 1500 N normal force, 30 μm oscillation amplitude, about 42 mm/s weld speed, and two bilayers. The effects of each process parameter on bond strength are discussed and explained.

Design layout preparing method

Abstract: There is disclosed a method of producing a design layout by optimizing at least one of design rule, process proximity correction parameter and process parameter, including calculating a processed pattern shape based on a design layout and a process parameter, extracting a dangerous spot having an evaluation value with respect to the processed pattern shape, which does not satisfy a predetermined tolerance, generating a repair guideline of the design layout based on a pattern included in the dangerous spot, and repairing that portion of the design layout which corresponds to the dangerous spot based on the repair guideline.

Semiconductor run-to-run control system with state and model parameter estimation

Abstract: A method for a run-to-run (R2R) control system includes processing materials using a process input and producing a process output, storing the process input in a database, the storing including using a timestamp, and storing at least one measurement of the process output in the database aligned with each process input using the timestamp. The method further includes iterating over the data in the database to estimate one or more coefficients for a model, and, if one or more measurements is missing, replacing the missing measurements based on a prediction from said model. The model is updated with said coefficient estimates. The method additionally includes iterating over the data from the database to estimate a process state, and, if one or more of the measurements is missing from the database, replacing the missing measurements based on prediction from the model. The model is updated with said process state estimate. A controller may receive the updated model and utilize the model to produce the next process input. The updated model may also be utilized to generate an estimate for a measurable process variable, wherein the estimate can be compared to an actual measurement to determine if the estimate is within confidence limits. If the estimate is not within confidence limits, a fault is indicated.

Optimization of PMEDM process parameter for maximizing material removal rate by Taguchi’s method

Abstract: The electrical discharge machining process is hampered by a low material removal rate (MRR), high tool wear, and low quality of machined surface, which limit its applicability. The use of powder mixed electrical discharge machining helps overcome this drawback and increases the efficiency of the machining process. This study focused on the machining of SKD61, SKD11, and SKT4 die steels using titanium powder. Taguchi methods and analysis of variance were employed to identify the main parameters that affect the MRR. The other process parameters considered were the electrode material, workpiece material, electrode polarity, pulse-on time, pulse-off time, electric current, and titanium powder concentration. The results indicated that electric current, electrode material, and powder concentration were the most significant parameters that influenced the MRR. A powder concentration of 20 g/l increased the MRR by 42.1 %, as compared with no-powder machining. Further, the optimal value of the MRR was determined to be 45.734 mm3/min.