## What is/are Process Response?

Process Response - The results revealed that the temperature was the most influential factor for all three-process responses with 71.^{[1]}This prediction could be employed to predict distortion, residual stress and other process response in a shorter time.

^{[2]}In order to analyze the process, two dependent parameters (flux and rejection) were studied as the process responses.

^{[3]}Among all cutting parameters, cutting speed has the highest effect on all process responses.

^{[4]}Multi-response surface optimization (MRSO) is a problem that is peculiar to an industrial setting, where the aim of a process engineer is to set his process at operating conditions that simultaneously optimize a set of process responses.

^{[5]}A process response analysis for the avulsion events with its sensitivity to hydrological and floodplain topographic components.

^{[6]}Task difficulty was modulated by time available to process responses.

^{[7]}The mixing ratio of S/I and HRT of the anaerobic digestion was modeled using Central Composite Design (Face Centered Design) and the process response, cumulative biogas yield was optimized.

^{[8]}52 g/min), and scanning strategies (Unidirectional, Bidirectional) were considered as the input process variables while geometrical dimensions (height, width average), standard deviation of microhardness, and the stability of additively manufactured walls were determined as process responses.

^{[9]}Study of input factors play a vital role in controlling of process responses such as surface finish, cutting temperature, energy consumption etc.

^{[10]}Second-order regression models have been developed to correlate the input parameters to the process responses.

^{[11]}The generated particle population can be used for particle-based process simulation to evaluate the process responses of various ore textures subjected to various modes of breakage.

^{[12]}Two hybrid artificial neural network (ANN) models are used to predict the process responses after training them using the experimental results.

^{[13]}However, its effect on process responses in comparison to the plain tool-shoulder end-surface lacks recognition, which has been targeted in the present work.

^{[14]}Hence, geomorphic threshold is applied to define the resilience capacity of river and significant change of process response system in case of sand mining.

^{[15]}The obtained results demonstrate the high ability of the random vector functional link model to find out the nonlinear relationship between the operating conditions and process responses.

^{[16]}To fill this gap, this paper proposes a multiagent consensus control method considering the whole-process response cost of air conditioning.

^{[17]}Response surface methodology with pulse input was utilized to investigate how the process responses vary when temperature, superficial velocity and injection volume are altered, and to define which conditions maximize productivity and minimizes xylitol retention while maintaining satisfactory levels of clarification.

^{[18]}Thus, robust design of tube hydroforming aims to vanish noise factors effects on process responses by considering the influence of process parameters variability.

^{[19]}The obtained controller tunings are applied to the Gravity Drained Tank function of LOOP-PRO software and its process responses and performance indexes are compared with manually calculated controller tunings.

^{[20]}Using the integral of signals, implicit model information contained in process response data becomes explicit, and then the least squares approach is adopted to construct a detailed low-order process model based on process response data in more general types.

^{[21]}The results indicate that all the process responses are affected by changing the inlet air flow rate, temperature, and suspension spray rate.

^{[22]}The contact depth has proved to have the most significant effect in the process responses, while the roller diameter has the least effect.

^{[23]}979) for the process responses of pressure, motor torque, and SME, and slightly lower R2 values for product responses of ER (0.

^{[24]}ANOVA was utilized to obtain the influence of input factors on the process response.

^{[25]}Once a computational model is set up, the optimization problem is treated as an inverse problem subjected to constraints that depend on the process response in terms of temperature cycles.

^{[26]}Due to that, comprehensive analysis of process responses as well as defining optimal cutting conditions is necessary.

^{[27]}Task difficulty was modulated by time available to process responses.

^{[28]}The process responses of pH solution and total organic carbon (TOC) are measured versus time for different step changes of H2O2 inlet flow rates.

^{[29]}The effect of input parameters on process responses is studied for parametric analysis.

^{[30]}This approach decomposes the variability in process responses into two components–a global trend and a residual term, which are estimated through self-learning and MTL of Gaussian process (GP), respectively.

^{[31]}Based on their acceptable values, it can be propounded that the ANFIS model can be effectively employed for prediction of process responses while treating different machining parameters as the input variables.

^{[32]}However, a chronostratigraphically constrained record of climatic fluctuations and process responses in the hinterland source area recorded in deep-water deposits is rare.

^{[33]}The effects of barrel temperature (110, 125, and 140 °C) and screw speed (150, 200, and 250 rpm) on process responses and extrudate characteristics were evaluated using a corotating twin-screw extruder.

^{[34]}Inside MPC schemes, ANNs can rapidly predict the process response to a control action.

^{[35]}Statistically, ANOVA and regression analysis were conducted to study the contribution of the individual factors to the process response and to introduce regression model to predict the resulting surface roughness in terms of different values of the factors.

^{[36]}This paper approaches the subject from beforehand prevention, in-process response, ex-post handling, and comprehensive governance aspects.

^{[37]}

## material removal rate

The process responses are characterized as material removal rate (MRR; μg/s) and change in surface roughness (ΔRa; μm).^{[1]}, drill speed, feed rate and fiber volume percentage varied at four discrete levels and surface roughness (Ra) and material removal rate (MRR) are taken as process response.

^{[2]}The three process responses considered are material removal rate (MRR), tool wear rate (TWR) and taper in drilled hole while the four selected controllable process parameters are capacitance(C), voltage(V), feed rate(f) and rotational speed of electrode (N).

^{[3]}

## Main Process Response

The damage factor of the drilled holes and the temperature of the drill tip were considered as main process responses.^{[1]}In a comprehensive research at Sarcheshmeh porphyry copper mine, geological features, what were supposed to affect the main process responses including product’s grade-recovery, and plant’s throughput, were subjected to investigate as the possible geometallurgical indexes.

^{[2]}

## Predict Process Response

Response surface methodology (RSM) based predicative modeling was developed to predict process responses and the effect of parameters was studied.^{[1]}These models require a constitutive description of the material behavior to accurately model and predict process responses such as cutting forces, temperatures, and residual stress.

^{[2]}

## Important Process Response

Two important process responses MRR and SR have been studied as a function of four different control parameters, namely pulse width, time between two pulses, wire mechanical tension and wire feed rate.^{[1]}The effectiveness of dry cutting has been experimentally investigated as compared to flooded lubrication condition giving equal weights to the two important process responses simultaneously.

^{[2]}

## process response analysi

A process response analysis for the avulsion events with its sensitivity to hydrological and floodplain topographic components.^{[1]}Given this, this work aims to apply the Xcos® for studying the implementation of the process control theory applied to chemical engineering projects, focusing on the development of control loops block diagrams, PID control tuning, and process response analysis.

^{[2]}