圆盘类零件加工自动上下料系统设计

针对批量加工皮带轮用圆盘类零件中人工上下料问题,设计一种经济型数控车床适用的自动上下料方案,有效提高了生产自动化程度。整个装置基于CK6136-750机床,采用SolidWorks软件对自动上下料装置进行了设计,包括上料机构、下料机构、坯料推进机构等。通过数控系统二次开发,结合气动技术、PLC控制技术与传感器技术,开发自动上下料控制系统,并添加进CNC系统的辅助功能中,实现了上料、加工、下料的一体化控制,可以一人多机操作,降低了生产成本,缩短了加工时间,具有广泛的市场需求和工业应用前景。     

Kinetic parameter estimation for cooling crystallization process based on cell average technique and automatic differentiation

In this paper, a cell average technique(CAT) based parameter estimation method is proposed for cooling crystallization involved with particle growth, aggregation and breakage, by establishing a more efficient and accurate solution in terms of the automatic differentiation(AD) algorithm. To overcome the deficiency of CAT that demands high computation cost for implementation, a set of ordinary differential equations(ODEs) entailed from CAT based discretized population balance equation(PBE) are solved by using the AD based high-order Taylor expansion. Moreover, an AD based trust-region reflective(TRR) algorithm and another interior-point(IP) algorithm are established for estimating the kinetic parameters associated with particle growth, aggregation and breakage. As a result, the estimation accuracy can be further improved while the computation cost can be significantly reduced, compared to the existing algorithms. Benchmark examples from the literature are used to illustrate the accuracy and efficiency of the AD-based CAT, TRR and IP algorithms in comparison with the existing algorithms. Moreover, seeded batch cooling crystallization experiments of β form L-glutamic acid are performed to validate the proposed method.     

自动拆垛不锈钢分层

本文针对行业一直存在的冲压双料的问题,通过介绍了磁铁分张的通用方法,特别针对不锈钢分张的难题,详细介绍了不锈钢分张的方法,并对具体操作步骤和注意事项做了说明,对不锈钢的分张提供了解决方法和思路,推动自动化线生产稳定的上料技术发展。     

Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method

Automatic tool changer (ATC) is one of the key basic parts in CNC machining centers, and the globoidal indexing cam and the groove cam are the functional units for tool changing. Thus the condition monitoring is important for highly efficient and precision machining. In this paper, a condition monitoring system is constructed for the ATC, especially for the globoidal indexing cam, including vibration signal acquisition, fault feature extraction and localization, and condition assessment. In the constructed system, sparsity-enabled signal decomposition method is introduced to extract transient component and reduce noises in the complex vibration signals, and the transient component is always a key feature for fault localization. Simulation study shows that the sparsity-enabled signal decomposition method is effective in transient feature extraction. The experimental application in condition assessment for the ATC demonstrates that the constructed condition monitoring system has the potential to assess the working condition of the ATC in practical application.     

How to predict the global instantaneous feeling induced by a facial picture?

Picture selection is a time-consuming task for humans and a real challenge for machines, which have to retrieve complex and subjective information from image pixels. An automated system that infers human feelings from digital portraits would be of great help for profile picture selection, photo album creation or photo editing. In this work, two models of facial pictures evaluation are defined. The first one predicts the overall aesthetic quality of a facial image, and the second one answers the question “Among a set of facial pictures of a given person, on which picture does the person look like the most friendly?”. Aesthetic quality is evaluated by the computation of 15 features that encode low-level statistics in different image regions (face, eyes, and mouth). Relevant features are automatically selected by a feature ranking technique, and the outputs of 4 learning algorithms are fused in order to make a robust and accurate prediction of the image quality. Results are compared with recent works and the proposed algorithm obtains the best performance. The same pipeline is considered to evaluate the likability of a facial picture, with the difference that the estimation is based on high-level attributes such as gender, age, and smile. Performance of these attributes is compared with previous techniques that mostly rely on facial keypoint positions, and it is shown that it is possible to obtain likability predictions that are close to human perception. Finally, a combination of both models that selects a likable facial image of good quality for a given person is described.     

Efficient automatic parallelization of a single GPU program for a multiple GPU system

Single GPU scaling is unable to keep pace with the soaring demand for high throughput computing. As such executing an application on multiple GPUs connected through an off-chip interconnect will become an attractive option to explore. However, much of the current code is written for a single GPU system. Porting such a code for execution on multiple GPUs is difficulty task. In particular, it requires programmer effort to determine how data is partitioned across multiple GPU cards and then launch the appropriate thread blocks that mostly accesses the data that is local to that card. Otherwise, cross-card data movement is an expensive operation. In this work we explore hardware support to efficiently parallelize a single GPU code for execution on multiple GPUs. In particular, our approach focuses on minimizing the number of remote memory accesses across the off-chip network without burdening the programmer to perform data partitioning and workload assignment. We propose a data-location aware thread block scheduler to schedule the thread blocks on the GPU that has most of its input data. The scheduler exploits well known observation that GPU workloads tend to launch a kernel multiple times iteratively to process large volumes of data. The memory accesses of the thread block across different iterations of a kernel launch exhibit correlated behavior. Our data location aware scheduler exploits this predictability to track memory access affinity of each thread block to a specific GPU card and stores this information to make scheduling decisions for future iterations. To further reduce the number of remote accesses we propose a hybrid mechanism that enables migrating or copying the pages between the memory of multiple GPUs based on their access behavior. Hence, most of the memory accesses are to the local GPU memory. Over an architecture consisting of two GPUs, our proposed schemes are able to improve the performance by 1.55× when compared to single GPU execution across widely used Rodinia [17], Parboil [18], and Graph [23] benchmarks.     

A method of automatic extraction of parameters of multi-LoD BIM models for typical components in wooden architectural-heritage structures

A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiple-level-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.     

Easily too difficult: Estimating item difficulty in computer simulated microworlds

Dealing with complexity and dynamics is increasingly becoming part of people's everyday lives. The necessity of dealing with complex systems has instigated the use of computer simulations, so-called microworlds (MWs), to assess and study human behavior in complex situations. Although these MWs enjoy great popularity with both practitioners and researchers, their psychometric qualities have been questioned, and studies that have investigated these qualities have been sparse. In particular, only a few studies have investigated the factors that contribute to item difficulty in MWs. To fill this gap, we analyzed data from 3128 Finnish students with a linear logistic test model. Our results suggest that item difficulty in MWs can be almost perfectly predicted by six basic item characteristics, namely, (a) the use and number of eigendynamics, the numbers of (b) input and (c) output variables, the numbers of (d) input and (e) output variables not related to any other variables, and (f) the total number of relations between all variables. In addition, we provide evidence for the necessity of differentiating between the difficulty of controlling an MW (knowledge application) and understanding its underlying structure (knowledge acquisition). Finally, we discuss further theoretical and practical implications of an increased understanding of MWs for their use as assessment instruments.     

Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller

This article presents the automatic generation control of an unequal three area thermal system. Single stage reheat turbines and generation rate constraints of 3%/min are considered in each control area. Controllers such as Integral (I), Proportional – Integral (PI), Proportional – Integral – Derivative (PID), and Proportional – Integral – Derivative Plus Second Order Derivative (PID + DD) are treated as secondary controllers separately. A nature inspired optimization technique called Ant Lion Optimizer (ALO) algorithm is used for simultaneous optimization of the controller gains. Comparison of dynamic responses of frequencies and tie line powers corresponding to ALO optimized I, PI, PID and PID + DD controller reveal the better performance of PID + DD controller in terms of lesser settling time, peak overshoots as well as reduced oscillations. Robustness of the optimum gains of best controller obtained at nominal conditions is evaluated using sensitivity analysis. Analysis exposed that the optimum PID + DD controller gains obtained at nominal are robust and not necessary to reset again for changes in loading, parameter like inertia constant (H), size and position of disturbance. Furthermore, the performance of PID + DD controller is found better as compared to PID controller against random loading pattern condition.     

Design and analysis of multi-source multi-area deregulated power system for automatic generation control using quasi-oppositional harmony search algorithm

The present article focuses on the study of automatic generation control (AGC) of a realistic power system having a distinct combination of multi-area multi-source generating units in each control area under deregulated framework. An attempt is made in this paper to integrate reheat thermal, hydro and gas generating unit in a single control area and, then, extended this combination to five control areas. In this work, six reheat thermal, six hydro and three gas generating units are taken into account for the modeling of five-area power system. Some important physical constraints like time delay, governor dead band and generation rate constraint are imposed in the power system dynamics to get an accurate perception of the deregulated AGC subject. The highlighting features of the present work are to model, simulate, optimize and co-relate their inter-related dynamic performances for the purpose of AGC study. For such a complex AGC model, the vital role of the proposed quasi-oppositional harmony search (QOHS) algorithm, as an optimizing tool, is signified while solving the AGC problem in deregulated regime. The simplicity of the structure and acceptability of the responses of the well-known proportional–integral–derivative controller, inherently, enforces to employ in this work. The three classes of extensive deregulated cases (in the presence of load following and physical constraints) are demonstrated by examining the closed loop performance of the studied model. The simulation results show that the designed power system model may be a feasible one and the proposed QOHS algorithm may be a promising optimization technique under these circumstances.