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.

In process quality control on micro-injection moulding: the role of sensor location

Abstract: Micro-injection moulding is a high-throughput manufacturing process capable of producing micro-sized or meso parts containing micro-features. Quality inspection of these micro-components is usually costly and time consuming. Different process control strategies based on pressure monitoring are currently used to detect deviations in part quality. Because of the size of parts to replicate, pressure monitoring is generally performed by means of pins or ejectors inside the micro-featured cavity, leading to an indirect measure of the pressure in the mould during the injection cycle. In present study, direct monitoring of process signals during micro-injection was addressed via pressure and temperature sensors placed in two different mould locations: the runner system and the mould micro-featured cavity. Input parameters were varied following Design of Experiments methodology to analyse the variation in monitored signals as a function of replication quality. Injected micro-parts were quality controlled using confocal microscopy to correlate the recorded sensor signals to quality deviations. It has been observed that both runner system and micro-featured cavity pressure signals are linked to the replication quality level of the micro-injected part and show similar performance in terms of part quality differentiation. The process parameter which causes the greatest variations is the temperature set-point of the machine nozzle.

Enhancing Impact Strength of Fused Deposition Modeling Built Parts using Polycarbonate Material

Abstract: Objectives: In this research, enhancement of Impact Strength is done using Polycarbonate material by Optimizing the FDM (Fused Deposition Modeling) Process Parameter. Methods/Statistical Analysis: This study features four important process parameters namely layer thickness, build orientation, raster angle and raster width whose influence on Impact Strength is studied. Experiments were conducted based on Taguchi Design of Experiments methodology. The current work finds out the optimum parameter settings required to obtain maximum impact strength on Polycarbonate Material. Analysis of Variancetest (ANOVA) was performed to find the most influencing process parameter on Impact strength. A confirmatory test using the optimum process parameters was also carried out. Findings: It was found that all four parameters interact collectively with each other to obtain variation in Impact Strength values. Layer thickness influences Impact Strength the most as compared to the other considered process parameters. The results of the study show that the value of Impact strength corresponding to the optimum input parameters of layer thickness, 0.254 mm; build orientation, 30°; raster width, 0.904 mm and raster angle 60°, was found to be 68.4J/m. The findings of the confirmatory test were very close and in good agreement. Applications/Improvements: The machinists and engineers would be benefitted by selecting the optimized values for enhancing the impact strength of FDM Built parts.

Forming statistical model of independently variable parameters for timing analysis

Abstract: Forming of a statistical model for a set of independently variable parameters for analysis of a circuit design is disclosed. In one embodiment, a method includes establishing a timing model including delay and delay changes due to process parameter variations (Pi) that impact timing; selecting an element of the circuit design that dominates circuit delay in the timing model; determining a delay sensitivity of each of a set of derived process parameters (Vj) for the element that are linear combinations of the process parameter variations (Pi); and selecting only those derived process parameters with a high sensitivity for use in the statistical model. The invention simplifies the statistical model and reduces the number of calculations require for timing analysis. A method of performing a timing analysis using the simplified statistical model is also disclosed.

Multi-cylinder fuel injected IC engine running smoothness control method

Abstract: A method for controlling the running smoothness of a multi-cylinder combustion engine, in which the rotation acceleration of each individual cylinder is determined, and deviations between the individual cylinders are balanced out by altering the allotted fuel quantities for each individual cylinder. Regularly determined state variables (Z) of the engine (6) are measured, and a model (3) from these condition variables (Z) estimates a characteristic process variable (Ml) which represents a desired value of engine control. An instantaneous actual value (Mr) is determined from a measured state variable (Z) corresponding to the desired value, in which the rotary acceleration contribution of each individual cylinder is entered into the actual value (Mr). A control difference (deltaMe) for a controller (5) is formed from the desired values (Ml) and the actual value (Mr), and corrects combustion in the individual cylinders so that the control difference (deltaMe) is minimal.

Optimization of carbon nanotube based electrical discharge machining parameters using full factorial design and genetic algorithm

Abstract: This paper describes the application of the genetic algorithm (GA) coupled with full factorial design of experimental technique to optimize the parameters of the carbon nanotube (CNT) mixed with dielectric fluids in electrical discharge machining (EDM). The multiwall CNT is mixed with dielectric fluids to analyse the surface roughness and microcracks using atomic force microscope. Response surface model have been developed to predict the surface roughness of EDM parameters. Analysis of variance and F-test have been used to check the validity of response surface model and to determine the significant process parameter affecting the surface roughness. GA is used to optimize the process parameters during EDM of AISI D2 tool steel material with CNT-based machining. The developed mathematical model was further coupled with GA to find out the optimum conditions leading to the minimum surface roughness value.