Investigate Weld Quality of Gas Metal Arc Welding Process Through Online Monitoring of Input Parameters

Gas metal arc welding(GMAW)is also referred as the metal inert gas(MIG)welding which is a process of welding done by the formation of an electric arc between the consumable wire electrode and the workpiece.Through the welding process,a continuous flow of inert gas is supplied,and it avoids the weld being subjected to react with atmospheric air.The process can be automatic or semi-automatic where the main input parameters like current and the voltage can be direct and constant,respectively.Not only the current and voltage the welding quality depends on some more input parameters such as arc gap,velocity,and temperature.In this paper,we explain about a setup which is capable of real-time monitoring of input parameters mentioned above and selecting the best MIG welding parameters for the mild steel.The setup is composed of several sensors and microcontrollers for the collection and the measurement of the input parameters.The samples were categorized according to the federate and the voltage adjustment of the selected welding machine.Then the final objective was to identify the samples of the weld with different parameter changes which are monitored through the system.For the analysis,the samples were subjected to tensile and hardness tests,and microstructure tests to find the dependence of the input parameters which effect for the weld quality.Finally,the experimental results verified the effectiveness of the system for the selection of the quality weld.

Improved insensitive to input parameters trajectory clustering algorithm

The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.

Nonsingular terminal sliding mode approach applied to synchronize chaotic systems with unknown parameters and nonlinear inputs

This paper deals with the design of a novel nonsingular terminal sliding mode controller for finite-time synchro-nization of two different chaotic systems with fully unknown parameters and nonlinear inputs. We propose a novel nonsingular terminal sliding surface and prove its finite-time convergence to zero. We assume that both the master＇s and the slave＇s system parameters are unknown in advance. Proper adaptation laws are derived to tackle the unknown parameters. An adaptive sliding mode control law is designed to ensure the existence of the sliding mode in finite time. We prove that both reaching and sliding mode phases are stable in finite time. An estimation of convergence time is given. Two illustrative examples show the effectiveness and usefulness of the proposed technique. It is worthwhile noticing that the introduced nonsingular terminal sliding mode can be applied to a wide variety of nonlinear control problems.

Input torque sensitivity to uncertain parameters in biped robot

Input torque is the main power to maintain bipedal walking of robot, and can be calculated from trajectory planning and dynamic modeling on biped robot. During bipedal walking, the input torque is usually required to be adjusted due to some uncertain parameters arising from objective or subjective factors in the dynamical model to maintain the pre-planned stable trajectory. Here, a planar 5-link biped robot is used as an illustrating example to investigate the effects of uncertain parameters on the input torques. Kine-matic equations of the biped robot are firstly established by the third-order spline curves based on the trajectory planning method, and the dynamic modeling is accomplished by taking both the certain and uncertain parameters into account. Next, several evaluation indices on input torques are intro-duced to perform sensitivity analysis of the input torque with respect to the uncertain parameters. Finally, based on the Monte Carlo simulation, the values of evaluation indices on input torques are presented, from which all the robot param-eters are classified into three categories, i.e., strongly sensi-tive, sensitive and almost insensitive parameters.

Identification of multiple inputs single output errors-in-variables system using cumulant

A higher-order cumulant-based weighted least square（HOCWLS） and a higher-order cumulant-based iterative least square（HOCILS） are derived for multiple inputs single output（MISO） errors-in-variables（EIV） systems from noisy input/output data. Whether the noises of the input/output of the system are white or colored, the proposed algorithms can be insensitive to these noises and yield unbiased estimates. To realize adaptive parameter estimates, a higher-order cumulant-based recursive least square（HOCRLS） method is also studied. Convergence analysis of the HOCRLS is conducted by using the stochastic process theory and the stochastic martingale theory. It indicates that the parameter estimation error of HOCRLS consistently converges to zero under a generalized persistent excitation condition. The usefulness of the proposed algorithms is assessed through numerical simulations.

Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models

Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg·N·ha-1·yr-1 and crop residues at 3 t·ha-1·yr-1. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R2 = 0.31 - 0.55, p 0.05). Only the ECOSSE-simulated N2O fluxes showed a significant relationship (R2 = 0.33, p 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N2O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R2 = 0.34 - 0.41, p 0.05). Compared to the measured value (3624 kg·C·ha-1·yr-1), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH4 uptake we

Mathematical Modeling of the Bulk Density Property of Knitted Fabrics

The paper focuses on the prediction of the volumetric density property of knitting fabrics using mathematical modeling. Based on the graphical analysis of the obtained mathematical models, the results of the raw material properties for the futer knitted fabric on the values of alternative indicators are presented.

Sensitivity Analysis of Thermal Properties on Numerical Simulation Results of Quenching Process

A three-factor three-level orthogonal test project has been designed based on the numerical simulation of a Jominy quenching process.Then the data scattering effects of thermal conductivity and enthalpy of tested steel,as well as the heat exchange coefficients of the cooling media,on the results have been studied.The results can be used as a reference for choosing the input thermal parameter in finite element simulation of quenching,and for evaluating the simulation results.

Effects of coefficient of friction and coefficient of restitution on static packing characteristics of polydisperse spherical pebble bed

The effects of the coefficient of friction and coefficient of restitution on the static packing characteristics of a polydisperse spherical pebble bed are numerically investigated using the discrete element method.Several important static packing characteristics under different coefficients of friction and restitution are presented and discussed.The results show that the coefficients of friction and restitution impose opposite effects on the packing heights and global packing factor.Neither the coefficient of friction nor restitution affected the oscillation width of the wall,whereas their effects are primarily reflected in the oscillation amplitude of the radial local packing factor and the axial local packing factor distribution at the top of the pebble bed.In both the contact force distribution and coordination number distribution,a left-shifted phenomenon appearing as the coefficient of friction occurred,and only the magnitude of the maximum frequency is affected when the coefficient of restitution changed from 0.1 to 0.9.In all simulation cases,the effects of the coefficients of friction and restitution are similar to that of cross-impact.