Stability Issues of Welded Pipe Containing Pulsatile Flows

This paper deals with the dynamics and stability behavior of a welded pipe containing flowing fluid having a small harmonic component superposed. The equation of motion was derived to represent the motion of a welded pipe conveying a pulsatile flow using a tensioned Euler- Bernoulli beam theory. The finite element analysis was used to simulate the harmonic motion of a welded pipe conveying fluid. It was shown that welded pipes with clamped-clamped and clamped-pinned supports are subject to a multitude of parametric instabilities in all their modes. Stability maps are presented for parametric instabilities of welded pipe with clamped-clamped and clamped-pinned ends. It is found that the extent of the instability regions increases with flow velocity for clamped-clamped and clamped-pinned welded pipes. The most important consideration from a practical point of view is to avoid the onset of parametric resonance

The behaviour of different design of flexible force sensor based velostat during implementation of static load with different contact area

Great attention has been given to flexible force sensors and the materials that have been used as a sensing material by studying their properties and evaluate their behaviours with different types of loads. Velostat is one of the most promising sensing materials that has been widely studied and evaluated for different applications due to their distinct traits of flexibility, suitable cost as well as their ability to cover the wearable applications. This work makes focus on the behaviour of Velostat flexible force sensor by measuring the resistivity of the sensor while applying loads with different contact area. Two different designs of sensor have been studied, which are the single output flexible force sensor and multioutput matrix flexible force sensor, by applying loads starting from 0.98 (N) to 9.8 (N) with four compression disc having different diameters. It has been found that the two design behave in opposite manner. The resistance of the single output design decreased as the contact area increased, while the matrix design showed an increasing in the resistance as the contact area increased

TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS

Stud arc welding has become one of the most important unit operations in the mechanical industries. The need to reduce the time from product discovery to market introduction is inevitable. Reducing of standard deviation of tensile strength with desirable tensile strength joint as a performance character was use to illustrate the design procedure. The effects of (welding time, welding current, stud material, stud design, sheet material, sheet thickness, sheet cleaning and preheating) were studied. Design of Experiment (DOE) is a structured and organized method to determine relationships between factors affecting a process and output of the process itself. In order to design the best formulation it is of course possible to use a trial and error approach but this is not an effective way. Systematic optimization techniques are always preferable. Tensile strength quality is one of the key factors in achieving good stud welding process performance. 225 samples of stud welding was tested. Computer aided design of experiment for the stud welding process based on the neural network artificial intelligence by Matlab V6.5 software was also explain. The ANN was designed to create precise relation between process parameters and response. The proposed ANN was a supervised multi-layer feed forward one hidden layer with 8 input (control process parameters), 16 hidden and 2 output (response variables) neurons. The learning rule was based on the Levenberg-Marquardt learning algorithm. The work of stud welding was performed at the engineering college laboratory, Baghdad University by using the DABOTEKSTUD welding machine, for 6 mm diameter stud. The sheet materials are (K14358 and K52355) according to (USN standards, and stud materials are (54NiCrMoS6 and 4OCrMnMoS8-6) according to (DIN standards). The eight control parameters (welding time, sheet thickness, sheet coating, welding current, stud design, stud material, preheat sheet and surface condition) were studied in the mixed L16 experiments Taguchi experimental orthogonal array, to determine the optimum solution conditions. The optimum condition was reached for the stud welding process tensile strength, where the researcher develops a special fixture for this purpose. The analysis of results contains testing sample under optimum condition, chemical composition of usage materials and micro structure of optimal condition sample. According to that: Practicality: the influence parameters that affect the stud welding process are welding time, which have a major effect on stud welding process, followed by sheet material and stud material. The reduction in standard deviation was approximately (30.06 per cent) and for the range was as approximately (29.39per cent). In the other side the increase in the tensile strength mean was as approximately (30.84 per cent). The influence parameters that affect the tensile strength stud welding process are: the factor welding time has a major effect on stud welding process, followed by factor C (sheet coating) and factor F (stud material)

VERIFICATION OF LAMINATE COMPOSITEPLATE SIMULATION UNDER COMBINED LOADINGS THERMALSTRESSES

 This study deals with thermal cyclic loading phenomena of plates which were fabricated from   composite materials (woven roving fiber glass + polyester) were exposed to (75 C°) temperature   gradient thermal shock for ten times in different stage of conditioning times due to the effect of thermal   fatigue using the method of Levy solution and compared these results with both results from   experimental published work and (ANSYS Ver. 9) program. A composite laminate plate with fiber   volume fraction (υf =25.076%) is selected in this study and applying the combined loadings like   bending moment (Mo), and in-plane force (Nxx) beside the effect of thermal fatigue. It involves multi   theoretical and finite element fields; but the theoretical one contains the derived equation of stresses   distribution and evaluating the normal deflection of a middle point for dynamic analysis applying   different boundary conditions for heating and cooling. The main present numerical results for a   composite plate with (80%) fiber volume fraction claim that the relative reduction in normal deflection   and dynamic load factor are (78.593%) and (9.421%) during cooling to (-15 respectively.

RESIDUAL STRESS DISTRIBUTION FOR A SINGLE PASS WELD IN PIPE

Heat input due to the welding of mild steel pipe causes a temperature gradient in the parent metal. After welding and temperature cooling down, residual stresses appear around welding zone which reduces the weld strength. Residual stresses are a result of the temperature gradient and the dependency of material properties on the temperature, such as yield strength, elasticity modulus, and thermal expansion coefficient. In this study, a typical flat joint of a single pass weld in a thin pipe was studied analytically and numerically. Analytical approach is performed by exploring a simple method to calculate the magnitude of residual stress in terms of the weld shrinkage behavior. Numerical analysis is performed by applying non-linear transient heat transfer analysis using welding parameters, such as heat generation, free or force convection with ambient, are performed using a general purpose FE package ANSYS 8.0 in order to obtain the temperature distribution in the welded parts. A non-linear thermal-elastic-plastic stress analysis is then performed using the same package to predict the stress fields during and after welding

Welding of Low Alloy Steel DIN 15Mo3 by MIG/MAG Spot

????? ????? ????? ????? ??????? ?????? ?? ????? ?????? ?????? ?????? ??????? ?????? ?????? (MIG/MAG spot) ???????? ??? ??????? ????? ?? ???? ???????? ????? ??? ???????? ?? ?????? ??????????? ??????? ???????? ????? ???? ??????? ??????? ????? ??? (DIN15Mo3) ?????? ?????? ?????? ?????? ?????? ?? ???? ???? ?????? ???????" ??? ?????? ??? ????. ????? ??????? ??????? ?????? ??? CO2 ????" ?? ??? ??????? ?? ???? ??????? ??????? ????? ?? ?????? ????? ?? ??? ???? ?? ????? (13%) ???? (4mm) ???? (2sec). ?????? ??? ???? ??? ??????? ??? ??????? ????" ?? CO2 ????? ??? ???? (36KN) ??? ??????? ??? ??????? ????? (2mm) ???? (8sec) ?????? (220Amp.) ??? ??? ??????? ??? CO2 ????" ?? ??????? ??????? ??? ???? ??? (31KN) ??? ???? ???? ???????? (%13) ????? ???? (4mm) ????? (8sec) ????? (290Amp.) ???? (37.9KN) ??? ??? ??????? ??? CO2 ????? (30.9KN) ?? ????? ?????? (%18.5) ????? (6mm) ????? (8sec) ????? (450Amp.) ????? ??? ???? (39KN) ???? ??????? ??? CO2 ???? (37KN) ?? ????? ?????? (%5.20) .???? ??????? ????" ????? ??? ?????? ???????? ??????" ?????" ?? ????? ???? ?????? ????

The Investigation of Monitoring Systems for SMAW Processes

The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non-destructive (Hardness on HAZ) tests to investigate the quality control on the weld specimens. The experimental results obtained are then processed through the ANN model to control the welding process and predict the level of quality for different welding conditions. It has been deduced that the welding conditions (current, voltage, and travel speed) have a dominant factors that affect the weld quality and strength. Also we found that for certain welding condition, there was an optimum weld travel speed to obtain an optimum weld quality. The system supports quality control procedures and welding productivity without doing more periodic destructive mechanical test to dozens of samples

Monitoring and Quality Control of Stud Welding

This study is conducted to carry out a straightforward way appropriate for quality monitoring and stability of arc stud welding process, followed by a number of procedures to control the quality of welded samples, namely torque destructive testing and visual inspection context. Those procedures were being performed to support the monitoring system and verify its validity. Thus, continuous on-line monitoring guarantees earlier discovering stud welding defects and avoiding weld repeatability. On-line welding electronic monitoring system is for non destructive determining if a just completed weld is satisfactory or unsatisfactory, depending on welding current peak value detected by the system. Also, it has been observed significant harmonize which is mutually linking the monitored current peak values and quality control measures. So this concept is accordingly contributed in the process of supporting the fundamental objective of this research. On the other hand, two feed-forward neural networks have been developed for monitoring and control arc stud welding quality. First network predicts two output quality parameters (current peak value) and (torque testing value at failure). Second, predicts one output quality parameter (visual inspection). Networks have been trained to a set of data, which made them ready to receive new information for subsequent quality parameters prediction. Both networks showed up good response and acceptable result