Investigation of a quantified sound probe for stud weld quality measurement with numerical simulation data

Drawn arc stud welding with ceramic ferrules is a widely used joining process for joining sheet metal to studs, which can be threaded or sheared. During the welding process, various irregularities can occur which adversely affect the resulting mechanical properties. Arc blowing is one of the most common process defects. Arc blowing can result in an asymmetric weld bead which can increase the failure rate of the stud. An approach to stud testing is given in DIN ISO EN 14555. A sound probe carried out by an experienced welder provides qualitative information about the weld bead. The sound probe causes the stud to vibrate at its natural frequencies. If the eigenfrequencies can be calculated for each weld bead shape, the sound probe can be quantified. To this end, a new simulation approach is presented which allows the rapid calculation of the eigenfrequencies of the stud with different weld bead shapes. A data set is also generated and analyzed

Directed energy deposition-arc (DED-Arc) and numerical welding simulation as a method to determine the homogeneity

This research presents a hybrid approach to for the prediction of the homogeneity of mechanical properties in 3D metal parts manufactured using directed energy deposition-arc (DED-Arc). DED-Arc is an additive manufacturing process which can offer a cost-effective way to manufacture 3D metal parts, due to high deposition rate of up to 8 kg/h. Regression equations developed in a previous study were used to predict the mechanical properties of a wall structure using only the cooling time t8/5 calculated in a numerical welding simulation. The new approach in this research paper contains the prediction of the homogeneity of the mechanical properties, especially hardness, in 3D metal parts, which can vary due to localized changes in t8/5 cooling time provoked by specific geometrical features or general changes in dimensions. In this study a method for the calculation of the hardness distribution on additively manufactured parts was developed and shown

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas

Empirical study on DED-Arc welding quality inspection using airborne sound analysis

This study explores the potential of audible range airborne sound emissions from Gas Metal Arc Welding (GMAW) to create an automated classification system using neural networks (NN) for weld seam quality inspection. Irregularities in GMAW process (oil presence, insufficient shielding gas) may lead to porosity imperfections in weld seams. Using Directed Energy Deposition-Arc additive manufacturing, aluminum (Al) and steel wall structures were produced with varying shielding gas flows or applying oil. Acoustic emissions (AE) generated during the welding process were captured using audible to ultrasonic range microphones. Mel spectrograms were computed from the AE data to serve as input to NN during training. The proposed model achieved notable accuracies in classifying both Al weld seams (83% binary, 68% multi-class) and steel welds (82% binary, 58% multi-class). These results demonstrate that employing audible range AE and NN in GMAW monitoring offers a viable method for low-latency monitoring and valuable insights into improving welding quality

Directed energy deposition-arc (DED-Arc) and numerical welding simulation as a hybrid data source for future machine learning applications

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%

Weight Re-Mapping for Variational Quantum Algorithms

Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum machine learning applications. The promising outcomes shown by VQCs, such as improved generalization and reduced parameter training requirements, are attributed to the robust algorithmic capabilities of quantum computing. However, the current gradient-based training approaches for VQCs do not adequately accommodate the fact that trainable parameters (or weights) are typically used as angles in rotational gates. To address this, we extend the concept of weight re-mapping for VQCs, as introduced by K\"olle et al. (2023). This approach unambiguously maps the weights to an interval of length $2\pi$, mirroring data rescaling techniques in conventional machine learning that have proven to be highly beneficial in numerous scenarios. In our study, we employ seven distinct weight re-mapping functions to assess their impact on eight classification datasets, using variational classifiers as a representative example. Our results indicate that weight re-mapping can enhance the convergence speed of the VQC. We assess the efficacy of various re-mapping functions across all datasets and measure their influence on the VQC's average performance. Our findings indicate that weight re-mapping not only consistently accelerates the convergence of VQCs, regardless of the specific re-mapping function employed, but also significantly increases accuracy in certain cases

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe