References of "International Journal of Advanced Manufacturing Technology"
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See detailLaser joining of titanium alloy to polyamide: influence of process parameters on the joint strength and quality
Alsayyad, Adham Ayman Amin UL; Lama, Prashant; Bardon, Julien et al

in International Journal of Advanced Manufacturing Technology (2020)

Laser-assisted metal–polymer joining (LAMP) is a novel assembly process for the development ofminiaturized joints in hybrid lightweight products. This work adopts a design of experiments (DoE) approach to ... [more ▼]

Laser-assisted metal–polymer joining (LAMP) is a novel assembly process for the development ofminiaturized joints in hybrid lightweight products. This work adopts a design of experiments (DoE) approach to investigate the influence of several laser welding parameters on the strength and quality of titanium alloy (Ti-6Al-4V)–polyamide (PA6.6) assembly. Significant param- eters were highlighted using the Plackett Burmann design, and process window was outlined using the Response Surface Method (RSM). A statistically reliable mathematical model was generated to describe the relation between highlighted welding param- eters and joint strength. The analysis ofvariance (ANOVA) method was implemented to identify significant parametric interac- tions. Results show the prominence offocal position and laser power, as well as significant interaction between laser power and beam speed, on the joint strength. The evolution ofweld defects (bubbles, excessive penetration, flashing, titaniumcoloring, weld pool cavities, and welding-induced deflection) along the process windowwas investigated using optical microscopy. The resulted deflection in titaniumwas quantified, and its relationship with welding parameters was mathematically modeled. Robust process window was outlined to maintain insignificant deflection in the welded joints. Results showed that the growth ofweld defects correlates with a decline in joint strength. Optimal parameters demonstrated a defect-free joint, maximizing joint strength. [less ▲]

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See detailContact-State Monitoring of Force-Guided Robotic Assembly Tasks Using Expectation Maximization-based Gaussian Mixtures Models
Jasim, Ibrahim UL; Plapper, Peter UL

in International Journal of Advanced Manufacturing Technology (2014), 73(5), 623-633

This article addresses the problem of Contact-State (CS) monitoring for peg-in-hole force-controlled robotic assembly tasks. In order to perform such a monitoring target, the wrench (Cartesian forces and ... [more ▼]

This article addresses the problem of Contact-State (CS) monitoring for peg-in-hole force-controlled robotic assembly tasks. In order to perform such a monitoring target, the wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals of the manipulated object are firstly captured for different CS's of the object (peg) with respect to the environment including the hole. The captured signals are employed in building a model (a recognizer) for each CS and in the framework of pattern classification the CS monitoring would be addressed. It will be shown that the captured signals are non-stationary, i.e. they have non-normal distribution that would result in performance degradation if using the available monitoring approaches. In this article, the concept of the Gaussian Mixtures Models (GMM) is used in building the likelihood of each signal and the Expectation Maximization (EM) algorithm is employed in finding the GMM parameters. The use of the GMM would accommodate the signals non-stationary behavior and the EM algorithm would guarantee the estimation of the optimal parameters set of the GMM for each signal and hence the modeling accuracy would be significantly enhanced. In order to see the performance of the suggested CS monitoring scheme, we installed a test stand that is composed of a KUKA Lightweight Robot (LWR) doing a typical peg-in-hole tasks. Two experiments are considered; in the first experiment we use the EM-GMM in monitoring a typical peg-in-hole robotic assembly process and in the second experiment we consider the robotic assembly of camshaft caps assembly of an automotive powertrain and use the EM-GMM in monitoring its CS's. For both experiments, the excellent monitoring performance will be shown. Furthermore, we compare the performance of the EM-GMM with that obtained when using available CS monitoring approaches. Classification Success Rate (CSR) and computational time will be considered as comparison indices and the EM-GMM will be shown to have a superior CSR performance with reduced a computational time. [less ▲]

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