Post-processor development for a turning and milling composite machine tool

The accomplishment of a turning and five-axis milling in only one setup is extremely useful and is possible on a turning and milling composite machine tool. In this work, we present a control algorithm and develop a post-processor for this machine, which has six linear and three rotary axes. To calculate a generalized kinematics model, coordinate systems are established by analyzing the basic kinematic chain relation of the turning and milling composite machine tool. The two vectors, which control the motions of the cutter contact workpiece, are simultaneously transformed to provide the algorithms of the rotary angles and motion coordinate. A special post-processor written in JAVA language is developed according to the proposed algorithm. To evaluate the effectiveness and accuracy of the developed post-processor, a specimen (blade) is used in the cutting simulation and real machining experiment. Experimental results showed the effectiveness and accuracy of the proposed algorithm. Furthermore, Compatibility is improved by adding new functions such as change of target machine, cutter location data change, workpiece origin offset, and cutting feed rate control.     

Use of FIB to Study ZDDP Tribofilms

Focussed ion beam milling (FIB) followed by TEM has been used to study ZDDP tribofilms on rubbed steel surfaces. It has been found that the impact of high energy platinum and gallium ions during FIB causes significant morphological and structural changes to the uppermost 30–50 nm of a ZDDP tribofilm. This can be prevented by the low energy deposition of a quite thick gold layer prior to installation of the sample in the FIB facility. This problem, and its solution, have been quite widely reported in the non-tribology literature but have not previously been highlighted in the application of FIB to study tribological surfaces. It has also been found, using this gold pre-deposition method, that the bulk of the ZDDP tribofilm studied has a polycrystalline structure.     

Tribological Characteristics of Aqueous Graphene Oxide,Graphitic Carbon Nitride,and Their Mixed Suspensions

The tribological performance of graphene oxide (GO), graphitic carbon nitride (g-C₃N₄), and their mixed (g-C₃N₄/GO) aqueous suspensions was investigated. The 0.06 wt% GO, 0.06 wt% g-C₃N₄, and 0.06 wt% 1:1 g-C₃N₄/GO suspensions reduced the coefficient of friction (COF) by 37, 26 and 37% and wear mark radius by 19.1, 16.0 and 19.6%, respectively, in comparison with water. Pure g-C₃N₄ and GO suspensions showed unstable lubrication in the tests with relatively high loads and speeds, while the g-C₃N₄/GO mixed suspension had superior tribological performance in all tested conditions. This is because in the mixed suspension g-C₃N₄ agglomerates became smaller, and GO nanosheets exhibited fewer wrinkles and less stacking, which enabled the formation of a layer of tribo-composite film. As a result, the friction, wear and tribo-corrosion were reduced during sliding.     

Lyapunov exponent of friction-induced vibration under smooth friction curve

This paper studies the nonlinear behavior of the friction-induced vibration by using spring-mass model subject to the smooth frictionvelocity curve. The nonlinearity and instability of the friction may produce the chaotic vibration depending on the friction curve. In order to show this, the Lyapunov exponents are calculated for a variety of the slope and magnitude in the smooth friction curve. In turn, the dependency of the friction curve on the chaotic attractor is illustrated.     

Experimental study on the life prediction of servo motors through model-based system degradation assessment and accelerated degradation testing

The advent of smart factories has resulted in the frequent utilization of industrial robots within factories to increase production automation and efficiency. Due to the increase in the number of industrial robots, it has become more important to prevent any unexpected breakdowns of the factory. As a result, the lifespan prediction of machinery has become a crucial factor because such failures can be directly associated with factory productivity resulting in significant losses. Most of the failures occur within one of the core components of the robot arm, the servo motor, and thus we will focus on the analysis of the servo motor in this study. However, sensor attachment to such equipment is considered difficult due to the dynamic movement of the robot arm, meaning that internal instrumentation should be utilized during analysis. In addition, no definite measure to determine the degradation of the motor exists, and thus a new degradation index is proposed in this study. Therefore, in this study, the lifespan of the servo motor will be estimated through accelerated degradation testing methods based on a new system degradation assessment method, which estimates the fault of the system using observer-based residuals with encoder data obtained from internal instrumentation.     

A study on piezoelectric energy harvester using kinetic energy of ocean

Electricity generation through fossil fuels has caused environmental pollution. Accordingly, research on new renewable energy (solar, wind, geothermal heat, etc.) to replace fossil fuels and solve this problem is in progress. These devices can consistently generate power. However, they have several drawbacks, such as high installation costs and limitations in possible set-up environments. Therefore, the piezoelectric harvesting technology, which is able to overcome the limitations of existing energy technologies, is actively being studied. The piezoelectric harvesting technology uses the piezoelectric effect, which occurs in crystals that generate voltage when stress is applied. Its advantages include a wider installation base and a lower technological cost. This study investigated a piezoelectric energy-harvesting device based on constant wave motion. This device can harvest power in a constant turbulent flow in the middle of the sea. The components of the device are circuitry, percussion bar, triple layer piezoelectric bender, bearing and rudder. A multiphysical analysis coupled with the structure and piezoelectric elements was also conducted to estimate the device performance. The analysis accuracy was improved by applying the impact energy to the bender calculated based on the shape of the wave in the East Sea. The proposed device’s performance was finally confirmed by experiments.     

Holzbrücken – Erkenntnisse zum Feuchteverhalten in den Tragquerschnitten aus Langzeitmessungen

Timber bridges – Long term monitoring results of moisture diffusion in the load carrying cross‐sections Timber road bridges have been built worldwide for centuries. The high performance of wood is approved through many constructions. However, there are still doubts about using wood by the planning engineer, which reduce the number of realized projects. The moisture induced stresses in cross‐sections according to the varied ambient climate are mainly investigated under laboratory conditions. The results show that the moisture content changes differently over the cross‐section and leads to moisture induced stresses. The discussion about methods for the assessment of the moisture induced stresses in timber bridge cross‐ections is continuing. Results observed in the long term monitoring of seven timber bridges provide first guidelines for practitioners. For all constructions, the moisture content was measured regarding the ambient micro climate. As result, the influences according to the ambient micro climate are not insignificant. Positive is that the directly measured moisture content is less compared to the equilibrium moisture content calculated from the data of a close by meteorological station. The analysis of the moisture gradient over the cross‐section allows the definition of an active or passive zone.     

Preface

Evolutionary Algorithms(EAs)mimicking the principles of natural evolution can be used as powerful search and optimization methods.These Evolutionary Optimization methods are superior to traditional optimization methods especially in situations where gradient information is not readily available.EAs are generic and robust optimization methods since they do not make any assumption about the underlying search space.This is clearly evident by the success of EAs in a diverse range of real-world applications.T...     

Circulating miR-200 Family and CTCs in Metastatic Breast Cancer before,during, and after a New Line of Systemic Treatment

The extracellular circulating microRNA (miR)-200 regulates epithelial-mesenchymal transition and, thus, plays an essential role in the metastatic cascade and has shown itself to be a promising prognostic and predictive biomarker in metastatic breast cancer (MBC). Expression levels of the plasma miR-200 family were analyzed in relationship to systemic treatment, circulating tumor cells (CTC) count, progression-free survival (PFS), and overall survival (OS). Expression of miR-200a, miR-200b, miR-200c, miR-141, and miR-429, and CTC status (CTC-positive ≥ 5 CTC/7.5 mL) was assessed in 47 patients at baseline (BL), after the first completed cycle of a new line of systemic therapy (1C), and upon the progression of disease (PD). MiR-200a, miR-200b, and miR-141 expression was reduced at 1C compared to BL. Upon PD, all miR-200s were upregulated compared to 1C. At all timepoints, the levels of miR-200s were elevated in CTC-positive versus CTC-negative patients. Further, heightened miR-200s expression and positive CTC status were associated with poorer OS at BL and 1C. In MBC patients, circulating miR-200 family members decreased after one cycle of a new line of systemic therapy, were elevated during PD, and were indicative of CTC status. Notably, increased levels of miR-200s and elevated CTC count correlated with poorer OS and PFS. As such, both are promising biomarkers for optimizing the clinical management of MBC.     

De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.