Measurement Potential of the Barkhausen Effect for Obtaining Additional Information on the Component Condition in Manufacturing

The measurement of Barkhausen noise is one of the non-destructive testing methods which allows the use within the production line and within the cycle time at a high production volume. The aim of the present study was to answer the question, whether it is possible to extract the informations that the Barkhausen noise includes, concerning work-piece conditions, from the signal characteristic and more important assigning these findings. Therefore, soft machined and heat treated shaft components made of the ferromagnetic material Cf53 (1.1213) were analyzed to find characteristics in the Signal that allow to separate clearly an increase in temperature of the tested area from a change in the microstructure. For this purpose the shafts were analyzed at higher temperatures (up to 80 °C) and after an additional annealing process (to change the microstructure specifically). Both investigated situations (higher temperature and modified microstructure) showed different characteristic in the Barkhausen signal, thus an assigning is possible. Metallographic investigation and hardness measurements has been carried out to support the results.

Fault Diagnostics of Roller Bearings Using Dimension Theory

Rolling bearings accomplishes a smoother force transmission between relative components of high production volume systems. An impending fault may cause system malfunction and its maturation lead to a catastrophic failure of the system that increases the possibility of unscheduled maintenance or an expensive shutdown. These critical states demand a robust failure diagnosis scheme for bearings. The present paper demonstrates a novel way to develop a dynamic model for the rotor-bearing system using dimensional analysis (DA) considering significant geometric, operating, and thermal parameters of the system. The vibration responses of faulty spherical roller bearings are investigated under various operating conditions for validation of the developed model. Multivariable regression analysis is performed to expose the potential of the approach in the detection of the bearing failure. Results obtained unveil the simple and reliable nature of the dynamic modeling using DA.

Screen-Printed Electrodes for the Voltammetric Sensing of Benzotriazoles in Water

Benzotriazoles (BZTs) are high production volume industrial chemicals that are used in various applications such as corrosion inhibitors, antifreeze agents, and UV radiation stabilizers. Given their potential ecotoxicological implications for different ecosystems and in human health, as well as their poor biodegradability, they are of increasing concern. In this study, a new voltammetric method using commercial screen-printed electrodes (SPEs) has been developed for the sensing of BZTs in water samples to help in their environmental monitoring. To this end, different types of SPEs based on carbon nanoallotropes and copper were tested under several experimental conditions to determine the two BZTs most frequently detected in the environment: 1H-benzotriazole (BZT) and 5-methyl-1H-benzotriazole (Me-BZT, tolyltriazole) as model compounds for BZTs. Carbon nanofibers electrodes exhibited the best performance, allowing detection limits as low as 0.4 mg L−1 for both BZTs, with repeatability and reproducibility of ca. 5%. The applicability of the method was tested through the determination of BZT in spiked drinking water samples, suggesting its suitability for the sensing of samples heavily polluted with BZTs.

Historical Perspective of Bisphenol A and Phthalates in the Environment and Their Health Effects

Plastics are widely used in modern life, and their unbound chemicals Bisphenol A and Phthalates, which are important chemical-building blocks, can leach out into the surrounding environment. They are also ubiquitous contaminants in the human body, wildlife, and the environment. BPA and PAEs have recently attracted the special attention of the scientific community, regulatory agencies, and the general public because of their high-production volume, widespread use of plastics, and endocrine-disrupting effects.

Urolithiasis in children and exposure to melamine: A review of the epidemiological literature

Melamine (ME) is a high production volume compound, mainly used for the synthesis of ME-formaldehyde resins. There is an extensive toxicological and epidemiological database on this compound, but the epidemiology has so far not been reviewed. An extensive literature search in PubMed was conducted and relevant ME epidemiology studies were identified and assessed. The epidemiology data nearly entirely consisted of studies on the health effects due to the food tampering catastrophe in China discovered in 2008 where ME was intentionally added to powdered milk products. Most of these studies were aimed at assessing the extent of the catastrophe and the clinical follow-up of affected children. A reliable dose–response relationship could not be derived from the epidemiology studies. However, they do contain several strong data points that can assist in ranging the No Observed Adverse Effect Level (NOAEL) for humans, for children in particular.

Integrating Impedance Control and Learning Based Search Scheme for Robotic Assemblies Under Uncertainty

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.