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.

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.

Statistical-based optimization of process parameters of fused deposition modelling for improved quality

Abstract: Fused Deposition Modelling (FDM) is a rapid prototyping system that produces physical models directly from the computer aided design (CAD) drawings. These models can be used to evaluate the assembly and the functionality of the design, also producing a manufacturing tools, and end-use parts. Parts built with production-grade thermoplastics that match the traditional machined parts, and according to the realworld conditions. FDM can produce instantly functional parts that used mainly in medical and automotive applications, with the use of reverse engineering techniques such as engineering scanning or digitizing systems. Knowledge of the quality characteristics of FDM fabricated parts is crucial. Quality significantly depends on process variable parameters. Optimizing the process parameters of FDM can make the system more precise and repeatable and such advancement can lead to use of FDM in rapid manufacturing applications rather than only producing prototypes. The part building is influenced by variant processing conditions. Thus, FDM process variable parameters are required to be collectively optimized rather than individually. In order to understand this issue, this study presents results of the experimental work on the effect of the main FDM process variable parameters of layer thickness (A), air gap (B), raster width (C), contour width (D), and raster orientation (E) on the quality characteristics of surface roughness (Ra), dimensional accuracy (DA), and tensile strength (TS). Previous studies have investigated the quality characteristics but limited knowledge is available on FDM newly improved materials. Thus, the new ABS- M30i biomedical material was used in this experimental work to build parts. To conduct this study, a full factorial experiment was used to obtain the test runs. A number of analytical methods such as regression analysis, Analysis of Variance (ANOVA), and Pareto analysis were used to determine the influence of the variable FDM process parameter settings. Results show that these process parameters have significant effect on the quality of finished products. For example, it has been found that the surface roughness and tensile strength of processed parts are greatly influenced by the air gap parameter as it affects the part’s beads structure, because it overlapping the material beads and consequently strengthen the beads bonding, and reduce the voids between the beads. Scanning Electron Microscope (SEM) work has been undertaken to characterise the experimental results. The results will be important for FDM produced parts in different functional applications as rapid manufacturing becomes increasingly accepted.

Process parameter effects on dimensional accuracy of a hot embossing process for polymer-based micro-fluidic device manufacturing

Abstract: The embossing of polymeric materials is widely used for manufacturing of micro-parts. The thermoplastic amorphous polymers are always selected to get micro-components. The paper consists to characterise the thermal mechanical properties of three amorphous polymers: polystyrene (PS), poly(methyl methacrylate) (PMMA) and polycarbonate (PC). A set of experiments such as differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) have been carried out with PS, PMMA and PC. The thermoplastic polymers’ behaviours have been characterised above their glass transition temperature. Then, flexible micro-fluidic devices have been manufactured with the polymers by hot embossing process. The surface topography of mould die cavity insert and the micro-fluidic devices have been observed and analysed by a 3D optical microscopy viewing and measurement system in order to compare the replication accuracy for polymeric replicas. The surface roughness of the micro-fluidic devices has been measured to characterise the effect of the compression load and temperature in the hot embossing process. The paper is mainly concentrated on the effect of the process parameters with different amorphous thermoplastic polymers to achieve the process optimization. The results concerning the micro- and nano-scale cavities filling provide information on the reliability about the facilities to replicate complex surface topographies. In this paper, the flow behaviour of polymer during forming process thus process parameters would be investigated. The mould instrumented with micro-structured patterns will be presented. The relationship between embossing conditions and parts quality will be established.

Monitoring laser machining process to be carried out on workpiece, comprises detecting actual measuring values by sensor, which monitors the laser machining process, and determining actual characteristic values from actual measuring values

Abstract: The method comprises detecting two actual measuring values by a sensor, which monitors a laser machining process, determining two actual characteristic values from the two actual measuring values using neural networks, where the actual characteristic values mutually represent an actual fingerprint in a characteristic value range, providing a predetermined point set in the characteristic value range, and classifying the laser machining process by detecting the position of the actual fingerprint relative to the predetermined point set in the characteristic value range. The method comprises detecting two actual measuring values by a sensor, which monitors a laser machining process, determining two actual characteristic values from the two actual measuring values using neural networks, where the actual characteristic values mutually represent an actual fingerprint in a characteristic value range, providing a predetermined point set in the characteristic value range, classifying the laser machining process by detecting the position of the actual fingerprint relative to the predetermined point set in the characteristic value range, regulating a process parameter of an associated actuator, so that the actuator is activated when the actual fingerprint exits the predetermined point set of the characteristic value range. The change of the associated process parameter corresponds to gradients in the characteristic value range, which extends itself from the fingerprint in the direction of the point set in the characteristic value range. The determination of the actual characteristic value from the actual measuring value comprises a process for data reduction or dimension reduction as a main component analysis, multidimensional scaling, support vector machine or support vector classification, or industrial standard organization-manufacturing automation protocol process. The predetermined point set is fixed within the characteristic value range using a learning process. The gradient field of the characteristic value range is determined in different areas at the points in the characteristic value range in dependent of the process parameter, where the points in the characteristic value range are representative for the respective area with respect to the gradients. The gradients of the characteristic value range are determined by variation of the process parameter at a predetermined point of the characteristic value range in dependent of the process parameter. The sensor comprises a photodiode with filter for the determined wavelengths, body- and airborne receiver, and a camera unit with a corresponding surface illumination. The actuator comprises a controller of the laser capacity, a speed controller of a machining head relative to a workpiece, a controller of the focal position of the machining laser beam, a controller of the distance of the machining head to the workpiece, and a controller of lateral offsets. Independent claims are included for: (1) a device for monitoring a laser machining process to be carried out on a workpiece; and (2) a laser machining head for processing a workpiece.

Development of a hybrid particle swarm optimization algorithm for multi-pass roller grinding process optimization

Abstract: In the field of metal rolling, the quality of steel roller’s surface is significant for the final rolling products, e.g., metal sheets or foils. The surface roughness of steel rollers must fall into a stringent range to guarantee the proper rolling force between the sheet and the roller. To achieve the surface roughness requirement, multiple grinding passes have to be implemented. The current process parameter design for multi-pass roller grinding mainly relies on the knowledge of the experienced engineers. This always requires time tedious “trial and error” and is insufficient to work out cases: (1) multi-pass with complex interaction for one pass with its neighboring passes; (2) large number of process parameters setup; (3) multiple process objectives and constrains. In this paper, a process planning method for multi-objective optimization is proposed with a hybrid particle swarm optimization while incorporating the response surface model of the surface roughness evolution. The hybrid particle swarm optimization regards the entire grinding process parameters (from rough grinding, semi-finish grinding, finish grinding to spark-out grinding) as a whole, and realizes the parameter optimization by considering multiple objectives and constrains. The establishment of the response surface model of surface roughness evolution is capable to incorporate the inter-correlation of neighboring passes into the multi-pass parameter optimization. Finally, the experimental verification was implemented to verify the effectiveness of the proposed method. The error between predicted roughness and experimental roughness is less than 16.53%, and the grinding efficiency is improved by 17.00% compared with the empirical optimal process parameters.