A local tool path smoothening scheme for micromachining

Linear and circular representations are widely used to define tool paths, however, the tangency discontinuity between the linear and circular segments leads to large fluctuations in velocity and acceleration, as a result, the machining accuracy and efficiency are degraded. It becomes the key problem in some micromachining situations where the quality of freeform surfaces is critical, such as moulds and knee implants, etc. This research aims to develop a local tool path smoothening scheme to achieve C2 continuity at the transition positions. This scheme applies to sections consisting of high density of short segments. These segments will be approximated by cubic B-splines. The approximation is carried out within the specific error tolerance. High frequency energy to be injected into the servo loop control system is greatly reduced by the C2 continuity. The proposed scheme is feasible to be implemented in real-time microcontrollers due to the computational efficiency and reliability of B-spline algorithms

Reconfigurable software architecture for a hybrid micro machine tool

Hybrid micro machine tools are increasingly in demand for manufacturing microproducts made of hard-to-machine materials, such as ceramic air bearing, bio-implants and power electronics substrates etc. These machines can realize hybrid machining processes which combine one or two non-conventional machining techniques such as EDM, ECM, laser machining, etc. and conventional machining techniques such as turning, grinding, milling on one machine bed. Hybrid machine tool developers tend to mix and match components from multiple vendors for the best value and performance. The system integrity is usually at the second priority at the initial design phase, which generally leads to very complex and inflexible system. This paper proposes a reconfigurable control software, architecture for a hybrid micro machine tool, which combines laser-assisted machining and 5-axis micro-milling as well as incorporating a material handling system and advanced on-machine sensors. The architecture uses finite state machine (FSM) for hardware control and data flow. FSM simplifies the system integration and allows a flexible architecture that can be easily ported to similar applications. Furthermore, component-based technology is employed to encapsulate changes for different modules to realize “plug-and-play”. The benefits of using the software architecture include reduced lead time and lower cost of development

Design of a new fast tool positioning system and systematic study on its positioning stability

The challenge of maintaining good surface quality under high operational frequencies in freeform machining invokes the need for a deterministic error analysis approach and a quantitative understanding on how structural design affects the positioning errors. This paper proposes a novel stiff-support positioning system with a systematic error analysis approach which reveals the contributions of disturbances on the tool positioning errors. The new design reduces the structural complexity and enables the detailed modelling of the closed loop system. Stochastic disturbances are analysed in the frequency domain while the non-stochastic disturbances are simulated in the time domain. The predicted following error spectrum agrees with the measured spectrum across the frequency range and this approach is justified. The real tool positioning error, which is free from sensor noise, is revealed for the first time. The influences of moving mass under various bandwidth settings have been studied both theoretically and experimentally. It is found that a larger moving mass helps combating disturbances except the sensor noises. The influences of cutting force are modelled and experimentally verified in the micro lens array cutting experiments. The origins of the form errors of the lenslet are discussed based on the error analysis model

On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations

Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging problem for real-world clinical applications. In this paper, we propose a novel method for fair representation learning with respect to multi-sensitive attributes. We pursue the independence between target and multi-sensitive representations by achieving orthogonality in the representation space. Concretely, we enforce the column space orthogonality by keeping target information on the complement of a low-rank sensitive space. Furthermore, in the row space, we encourage feature dimensions between target and sensitive representations to be orthogonal. The effectiveness of the proposed method is demonstrated with extensive experiments on the CheXpert dataset. To our best knowledge, this is the first work to mitigate unfairness with respect to multiple sensitive attributes in the field of medical imaging

Dual-buck arbitrary voltage divider with one output having reduced ripples

In this paper, a dual-buck voltage divider is further studied to provide two arbitrary, instead of balanced, voltage outputs. The two voltage outputs can be the same or different and are robust against parameter drift. The low-frequency ripples in one output are significantly reduced by actively diverting low-frequency ripple currents away from the corresponding output. Note that these are achieved by designing an advanced controller, without changing the topology. The controller consists of a PI controller to split the voltage, a repetitive controller and a resonant controller to deal with the low-frequency ripples at different frequencies. Experimental results are presented to validate the effectiveness of the proposed strategy

A real-time interpolator for parametric curves

Driven by the ever increasing need for the high-speed high-accuracy machining of freeform surfaces, the interpolators for parametric curves become highly desirable, as they can eliminate the feedrate and acceleration fluctuation due to the discontinuity in the first derivatives along the linear tool path. The interpolation for parametric curves is essentially an optimization problem, and it is extremely difficult to get the time-optimal solution. This paper presents a novel real-time interpolator for parametric curves (RTIPC), which provides a near time-optimal solution. It limits the machine dynamics (axial velocities, axial accelerations and jerk) and contour error through feedrate lookahead and acceleration lookahead operations, meanwhile, the feedrate is maintained as high as possible with minimum fluctuation. The lookahead length is dynamically adjusted to minimize the computation load. And the numerical integration error is considered during the lookahead calculation. Two typical parametric curves are selected for both numerical simulation and experimental validation, a cubic phase plate freeform surface is also machined. The numerical simulation is performed using the software (open access information is in the Acknowledgment section) that implements the proposed RTIPC, the results demonstrate the effectiveness of the RTIPC. The real-time performance of the RTIPC is tested on the in-house developed controller, which shows satisfactory efficiency. Finally, machining trials are carried out in comparison with the industrial standard linear interpolator and the state-of-the-art Position-Velocity-Time (PVT) interpolator, the results show the significant advantages of the RTIPC in coding, productivity and motion smoothness

Development of a compact ultra-precision six-axis hybrid micro-machine

High precision miniature and micro products which possess 3D complex structures or free-form surfaces are now widely used in industries. These micro products are usually fabricated by several machining processes in order to apply for various materials such as hard-to-machine steel and ceramic etc. The integration of these machining processes onto one machine becomes necessary since this will help reduce realignment errors and also increase the machining efficiency. In this research, an ultra-precision hybrid micro-machine which is capable of micro milling, micro grinding, micro turning, laser machining and laser assisted micro-machining has been designed and commissioned. Control software for on-machine metrology system (contact probe and dispersed reference interferometry (DRI)) and several plug-in modules including camera and handle system are integrated through a customised human-machine interface (HMI)

A generic control architecture for hybrid micro-machines

Hybrid micro-machining, which integrates several micro-manufacturing processes on one platform, has emerged as a solution to utilize the so-called "1 + 1 = 3" effect to tackle the manufacturing challenges for high value-added 3D micro-products. Hybrid micro-machines tend to integrate multiple functional modules from different vendors for the best value and performance. However, the lack of plug-and-play solutions leads to tremendous difficulty in system integration. This paper proposes a novel three-layer control architecture for the first time for the system integration of hybrid micro-machines. The interaction of hardware is encapsulated into software components, while the data flow among different components is standardized. The proposed control architecture enhances the flexibility of the computer numerical control (CNC) system to accommodate a broad range of functional modules. The component design also improves the scalability and maintainability of the whole system. The effectiveness of the proposed control architecture has been successfully verified through the integration of a six-axis hybrid micro-machine. Thus, it provides invaluable guidelines for the development of next-generation CNC systems for hybrid micro-machines

Selecting a semantic similarity measure for concepts in two different CAD model data ontologies

Semantic similarity measure technology based approach is one of the most popular approaches aiming at implementing semantic mapping between two different CAD model data ontologies. The most important problem in this approach is how to measure the semantic similarities of concepts between two different ontologies. A number of measure methods focusing on this problem have been presented in recent years. Each method can work well between its specific ontologies. But it is unclear how accurate the measured semantic similarities in these methods are. Moreover, there is yet no evidence that any of the methods presented how to select a measure with high similarity calculation accuracy. To compensate for such deficiencies, this paper proposes a method for selecting a semantic similarity measure with high similarity calculation accuracy for concepts in two different CAD model data ontologies. In this method, the similarity calculation accuracy of each candidate measure is quantified using Pearson correlation coefficient or residual sum of squares. The measure with high similarity calculation accuracy is selected through a comparison of the Pearson correlation coefficients or the residual sums of squares of all candidate measures. The paper also reports an implementation of the proposed method, provides an example to show how the method works, and evaluates the method by theoretical and experimental comparisons. The evaluation result suggests that the measure selected by the proposed method has good human correlation and high similarity calculation accuracy

Characteristics of the Temporal Variation in Temperature and Precipitation in China’s Lower Yellow River Region

We analyzed the spatial and temporal distributions of temperature and precipitation in China’s Yellow River Region between 1960 and 2001 by compiling meteorological data using anomalies, climate trend rate, linear regression, trend analysis, spline functions, and other methods. The results show that the average temperatures in the Region have an upward trend at a rate of 0.19°C every 10 years. There are no significant changes in the Region’s summers, but the winters have become visibly warmer, with the temperatures significantly increasing from the 1980s. The average annual precipitation rate has shown a downwards trend at a rate of −11.7 mm every 10 years. Even though the precipitation rate shows variations, the amount of precipitation is inconsistent with the most significant decrease in precipitation rates being seen during summer followed by autumn, while the rates actually slightly increased during spring and winter. Over the 42 years, the Region as a whole showed a trend of climate warming and drying with 77% of the total sites studied showing these combined trends. Before the 1980s, mainly a drying and cooling trend was observed. In the mid-to-late 80s the temperatures rose, resulting in the change to a warming and drying trend