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Because of fast frequency response, high stiffness, and displacement resolution, the piezoelectric actuators (PEAs) are widely used in micro/nano driving field. However, the hysteresis nonlinearity behavior of the PEAs affects seriously the further improvement of manufacturing accuracy. In this paper, we focus on the modeling of asymmetric hysteresis behavior and compensation of PEAs. First, a polynomial-modified Prandtl–Ishlinskii (PMPI) model is proposed for the asymmetric hysteresis behavior. Compared with classical Prandtl–Ishlinskii (PI) model, the PMPI model can be used to describe both symmetric and asymmetric hysteresis. Then, the congruency property of PMPI model is analyzed and verified. Next, based on the PMPI model, the inverse model (I-M) compensator is designed for hysteresis compensation. The stability of the I-M compensator is analyzed. Finally, the simulation and experiment are carried out to verify the accuracy of the PMPI model and the I-M compensator. The results implied that the PMPI model can effectively describe the asymmetric hysteresis, and the I-M compensator can well suppress the hysteresis characteristics of PEAs.

With the trend for global collaboration, there is a need for collaborative design between geographically distributed teams and companies. In particular, this need is inevitable in the companies doing their business based on one-of-a-kind production (OKP). One important problem is the lack of interoperability and compatibility of data between different CAx systems. This problem is further highlighted in data exchange in cloud manufacturing. To the best of authors' knowledge, current studies have limitations in achieving the interoperability and compatibility of data. In this paper, a STEP-based data model is proposed to represent OKP product data/knowledge, which contains four categories of product knowledge (i.e., customer, product, manufacturing, and resource resp.). A STEP-based data modelling approach is proposed to describe each category of knowledge separately and then connect them to form the final integrated model. Compared with most current product models, this model includes the more complete product data/knowledge involved in OKP product development (OKPPD), and thus it can provide more adequate knowledge support for OKPPD activities. Based on the proposed STEP-based data model, a product data exchange and sharing (DES) framework is proposed and developed to enable DES in collaborative OKPPD in the cloud manufacturing environment. Case studies were carried out to validate the proposed data model and DES framework.

Measurement and compensation of the geometric errors can improve the machining accuracy of machine tool. However, measuring bar of the double ball bar (DBB) is not variable. In this paper, a novel measuring equipment based on telescopic ball bar with an indexing joint (IJ-TBB) is proposed for the geometric errors. Firstly, the basic structure and measuring principle of IJ-TBB are introduced. Then, the effect of structural errors of IJ-TBB on the measurement accuracy is studied. Next, the error amplification characteristic and measurement resolution of IJ-TBB are analyzed. Finally, an experiment was carried out to validate the accuracy of IJ-TBB. The results reveal that the IJ-TBB has a high precision and high efficiency. In addition, the measurement resolution of IJ-TBB can reach up to 0.03μm, which is much higher than DBB. It is valuable to extend the measurement range and improve the measurement accuracy of machine tool.