Rare earth doping effect on the thermal stability of Ce0.35Zr0.60M0.05O2: insights from experiment and simulation

CexZr1-xO2 (CZ) plays an important role in many environment catalytic fields such as automobile three-way catalysts (TWCs), but improving their thermal stability is still a great challenge. In this work, a strategy was proposed to enhance the thermal stability of CZ by combining experiment with ab-initio molecular dynamics (AIMD) method. It is found that the thermal stability of Ce0.35Zr0.60M0.05O2 (M represent La, Y, and Nd elements) could be adjusted by doping the suitable rare earth (RE) elements in the surface of CZ. With this strategy, the thermal stability of Ce0.35Zr0.60M0.05O2 (CZ-Y) with surface doping of Y is highest among these Ce0.35Zr0.60M0.05O2 samples. In comparison with the CZ sample without doping (specific surface area SSA=20.16 m2⋅g-1), CZ-Y exhibit superior thermal stability (SSA=26.83 m2⋅g-1) after thermal treatment (1100 °C/10 h). To give a deep insight into the RE doping effects, the thermal displacement rate (TDR) of Ce0.35Zr0.60M0.05O2 are further calculated by AIMD. It is found that CZ-Y has the lowest TDR values, which is beneficial for suppressing the thermal displacement of atoms and improving the thermal stability of CZ. This study provides a deep insight into the origin of rare earth (RE) doping effect on CexZr1-xO2 (CZ), which is of fundamental interest for the development of high performance CZ in practical applications.

On-Line Monitoring of The Axial Thermal Displacement of Machine Tools By Using A Laser Mouse Sensor

This paper presents a low-cost on-line system for monitoring the axial thermal displacement of machine tools. The proposed monitoring system includes an embedded optical sensor derived from a laser mouse; an image acquisition microcontroller; speckle patterns; and an edge computer that hosts software including an image display module, a displacement calculation module, an image enhancement module, and a data visualization module. The proposed sensing system can measure the displacement in two orthogonal directions simultaneously by employing digital image correlation; thus, the proposed system is a two-dimensional displacement sensor. The sensing system benefits from image enhancement techniques and customized optimal speckle patterns printed using a standard low-cost monochrome laser printer. Experimental results indicate that the proposed displacement sensing system has an accuracy and a precision of less than 5 mm in both orthogonal directions; however, the measurement range is only 1 mm for a static measurement. The two-dimensional displacement sensing system was used for the on-line monitoring of the thermal deformation of a feed drive system for machine tools, and the performance of the sensing system was assessed experimentally.

Supports

Requirements for supports and other devices to restrain the piping are provided in Chapter II of ASME B31.3, specifically in Section 321, Piping Support. ASME B31.3 provides general requirements for piping supports as well as descriptions of conditions for which they must be designed. The support elements (e.g., springs, hanger rods, etc.) are within the scope of ASME B31.3, but the support structures to which they are attached are not. The supports must achieve the objectives in the design of the piping for sustained and occasional loads as well as thermal displacement.

On-Line Monitoring of the Axial Thermal Displacement of Machine Tools by Using a Laser Mouse Sensor

This paper presents a low-cost on-line system for monitoring the axial thermal displacement of machine tools. The proposed monitoring system includes an embedded optical sensor derived from a laser mouse; an image acquisition microcontroller; speckle patterns; and an edge computer that hosts software including an image display module, a displacement calculation module, an image enhancement module, and a data visualization module. The proposed sensing system can measure the displacement in two orthogonal directions simultaneously by employing digital image correlation; thus, the proposed system is a two-dimensional displacement sensor. The sensing system benefits from image enhancement techniques and customized optimal speckle patterns printed using a standard low-cost monochrome laser printer. Experimental results indicate that the proposed displacement sensing system has an accuracy and a precision of less than 5 mm in both orthogonal directions; however, the measurement range is only 1 mm for a static measurement. The two-dimensional displacement sensing system was used for the on-line monitoring of the thermal deformation of a feed drive system for machine tools, and the performance of the sensing system was assessed experimentally.

Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool

In the precision processing industry, maintaining the accuracy of machine tools for an extensive period is crucial. Machining accuracy is affected by numerous factors, among which spindle thermal elongation caused by an increase in machine temperature is the most common. This paper proposed a key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool. First, highly correlated temperature points were clustered into groups, and the characteristics of small differences within groups and large differences between groups were realized. The optimal number of key temperature points was then determined using the elbow method. Meanwhile, the long short-term memory (LSTM) modeling method was proposed to establish the relationship between the spindle thermal error and changes of the key temperature points. The results show the largest root mean square errors (RMSEs) of the proposed LSTM model and the key temperature point selection algorithm were within 0.6 µm in the spindle thermal displacement experiments with different temperature changes. The results demonstrated that the combined methodology can provide improved accuracy and robustness in predicting the spindle thermal displacement.