Measurements, Models, Systems and Design

531 s.

Cutting tool wear in turning 316L stainless steel in the conditions of minimized lubrication

316L stainless steel has emerged as one of the most used material in design and manufacturing for automotive, aerospace, marine, civil nuclear to produce critical components (valves, seats, pipes etc.). Despite, their huge application, during the machining of 316L stainless steel numerous challenges arise in terms of tool wear that are very detrimental for the surface of machined part. To obtain an extended life of tool used for machining commonly 316L stainless steel two different methods of cooling based on minimum lubrication condition, namely Minimum Quantity Lubrication (MQL) method and Minimum Quantity Cooling Lubrication (MQCL) with the addition of extreme pressure and anti-wear (EP/AW) method, respectively were settled. The use of the MQL method resulted in a reduction of the cutting tool wear by approximately 9% compared to the MQCL + EP / AW method and by approximately 21% compared to dry machining. Further, the highest values of wear indices were achieved during dry machining and the lowest ones in the method of minimized lubrication which validate the minimum lubrication as beneficial for reducing the wear progress

The Mathematical Model for the Secondary Breakup of Dropping Liquid

Investigating characteristics for the secondary breakup of dropping liquid is a fundamental scientific and practical problem in multiphase flow. For its solving, it is necessary to consider the features of both the main hydrodynamic and secondary processes during spray granulation and vibration separation of heterogeneous systems. A significant difficulty in modeling the secondary breakup process is that in most technological processes, the breakup of droplets and bubbles occurs through the simultaneous action of several dispersion mechanisms. In this case, the existing mathematical models based on criterion equations do not allow establishing the change over time of the process’s main characteristics. Therefore, the present article aims to solve an urgent scientific and practical problem of studying the nonstationary process of the secondary breakup of liquid droplets under the condition of the vibrational impact of oscillatory elements. Methods of mathematical modeling were used to achieve this goal. This modeling allows obtaining analytical expressions to describe the breakup characteristics. As a result of modeling, the droplet size’s critical value was evaluated depending on the oscillation frequency. Additionally, the analytical expression for the critical frequency was obtained. The proposed methodology was derived for a range of droplet diameters of 1.6–2.6 mm. The critical value of the diameter for unstable droplets was also determined, and the dependence for breakup time was established. Notably, for the critical diameter in a range of 1.90–2.05 mm, the breakup time was about 0.017 s. The reliability of the proposed methodology was confirmed experimentally by the dependencies between the Ohnesorge and Reynolds numbers for different prilling process modes

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace

State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach

X-Press Compactor for 1000x Reduction of Test Data

The paper presents a two-stage test response compactor with an overdrive section and scan chain selection logic. The proposed solution is capable of handling a wide range of X state profiles, offers compaction much higher than the ratio of scan chains to compactor outputs, and provides excellent diagnostic resolution 1