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.

Thermal modeling for white layer predictions in finish hard turning

Abstract: Part thermal damage is a process limitation in finish hard turning and understanding process parameter effects, especially, tool wear, on cutting temperatures is fundamental for process modeling and optimization. This study develops an analytical model for cutting temperature predictions, in particular, at the machined-surfaces, in finish hard turning by either a new or worn tool. A mechanistic model is employed to estimate the chip formation forces. Wear-land forces are modeled using an approach that assumes linear growth of plastic zone on the wear-land and quadratic decay of stresses in elastic contact. Machining forces and geometric characteristics, i.e. shear plane, chip–tool contact, and flank wear-land, approximate the heat intensity and dimensions of the shear plane, rake face, as well as wear-land heat sources. The three heat sources are further discretized into small segments, each treated as an individual rectangular heat source and subsequently used to calculate temperatures using modified moving or stationary heat-source approaches. Temperature rises due to all heat-source segments are superimposed, with proper coordinate transformation, to obtain the final temperature distributions due to the overall heat sources. All heat sources are simultaneously considered to determine heat partition coefficients, both at the rake face and wear-land, and evaluate the final temperature rises due to the combined heat-source effects. Simulation results show that, in new tool cutting, maximum machined-surface temperatures are adversely affected by increasing feed rate and cutting speed, but favorably by increasing depth of cut. In worn tool cutting, flank wear has decisive effects on machined-surface temperatures; the maximum temperature increases 2–3 times from 0 to 0.2 mm wear-land width. White layers (phase-transformed structures) formed at the machined-surfaces have been used to experimentally validate the analytical model by investigating tool nose radius effects on the white layer depth. The experimental results show good agreement with the model predictions. The established model forms a framework for analytical predictions of machined-surface temperatures in finish hard turning that are critical to part surface integrity and can be used to specify a tool life criterion.

System and method for correlated process pessimism removal for static timing analysis

Abstract: A method of removing pessimism in static timing analysis is described. Delays are expressed as a function of discrete parameter settings allowing for both local and global variation to be taken in to account. Based on a specified target slack, each failing timing test is examined to determine a consistent set of parameter settings which produces the worst possible slack. The analysis is performed on a path basis. By considering only parameters which are in common to a particular data/clock path-pair, the number of process combinations that need to be explored is reduced when compared to analyzing all combinations of the global parameter settings. Further, if parameters are separable and linear, worst-case variable assignments for a particular clock/data path pair can be computed in linear time by independently assigning each parameter value. In addition, if available, the incremental delay change with respect to each physically realizable process variable may be used to project the worst-case variable assignment on a per-path basis without the need for performing explicit corner enumeration.

Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness

Abstract: In this study, significant process parameters (layer thickness, build orientation, infill density and number of contours) are optimized for enhancing the magnitude/dimensional preciseness of fused deposition modeling (FDM) devise units. Hybrid statistical tools such as response surface methodology–genetic algorithm (RSM–GA), artificial neural network (ANN) and artificial neural network-genetic algorithm (ANN-GA) in MAT LAB 16.0 are utilized for training and optimization. An attempt has been made to build up a mathematical model in order to set up an indirect correlation between various FDM process parameters and magnitude preciseness. Sequentially to verify the different developed models and the optimum process parameters setting validation tests were also performed. The results showed that various hybrid statistical tools such as RSM-GA, ANN and ANN-GA are very adequate tools for FDM process parameter optimization. The minimum percentage variation in length = 0.06409%, width = 0.03961% and thickness = 0.85689% can be obtained by using ANN-GA.

Additive Manufacturing of IN100 Superalloy Through Scanning Laser Epitaxy for Turbine Engine Hot-Section Component Repair: Process Development, Modeling, Microstructural Characterization, and Process Control

Abstract: This article describes additive manufacturing (AM) of IN100, a high gamma-prime nickel-based superalloy, through scanning laser epitaxy (SLE), aimed at the creation of thick deposits onto like-chemistry substrates for enabling repair of turbine engine hot-section components. SLE is a metal powder bed-based laser AM technology developed for nickel-base superalloys with equiaxed, directionally solidified, and single-crystal microstructural morphologies. Here, we combine process modeling, statistical design-of-experiments (DoE), and microstructural characterization to demonstrate fully metallurgically bonded, crack-free and dense deposits exceeding 1000 μm of SLE-processed IN100 powder onto IN100 cast substrates produced in a single pass. A combined thermal-fluid flow-solidification model of the SLE process compliments DoE-based process development. A customized quantitative metallography technique analyzes digital cross-sectional micrographs and extracts various microstructural parameters, enabling process model validation and process parameter optimization. Microindentation measurements show an increase in the hardness by 10 pct in the deposit region compared to the cast substrate due to microstructural refinement. The results illustrate one of the very few successes reported for the crack-free deposition of IN100, a notoriously “non-weldable” hot-section alloy, thus establishing the potential of SLE as an AM method suitable for hot-section component repair and for future new-make components in high gamma-prime containing crack-prone nickel-based superalloys.