The thermal deformation of a feed drive system in machine tools, which is induced by the deformation of the bearings, ball screw, and nut, causes workpiece geometrical inaccuracies in machining, which is a major concern in high-accuracy machining . Various thermal models that estimate thermal deformation according to the temperature measured on the bearing or ball screw nut have been proposed using an inverse approach or machine learning algorithms . Nevertheless, related studies have seldom adopted direct methods. Thus, the accuracy of the estimated thermal displacement is strongly dependent on the accuracy of the thermal regression model; however, the generalizability and robustness of thermal models are questionable. Consequently, thermal models may require modification when some component is replaced as part of regular machine maintenance.
In this study, the thermal deformation of a feed drive system was directly measured and monitored using a computer-based two-dimensional displacement sensing system. The foundation of this system is a cost-effective embedded sensor in the feed drive system with a microcontroller. The aforementioned sensing system consists of a laser mouse as an image acquisition device, an Arduino microcontroller for data acquisition, and a computer for directly calculating the axial thermal deformation. Although this system has a low cost, it efficiently monitors the thermal deformation and satisfies the accuracy and precision requirements of < 10 µm.
The displacement sensing principle is based on digital image correlation (DIC) [3–5], which is a well-developed concept and has been proven to be an effective deformation measurement method because of its noninvasiveness and reusability [6, 7]. Continually improving the accuracy and efficiency of DIC algorithms in terms of subset size selection, intensity interpolation, and pattern matching has been the focus of academic research [6–9], and impressive performance has been achieved . Nevertheless, displacement measurement based on DIC has not developed into a mature embedded technique or become a value-added function for machines mainly because of its hardware requirement, namely an expensive and bulky camera.
Compared with charge-coupled device sensors, complementary metal oxide semiconductor (CMOS) sensors consume less power, and the images captured by CMOS sensors are not susceptible to smearing and blooming. Moreover, CMOS sensors enable facile integration with an on-chip image processing circuit for applications such as artificial vision and optical laser mice for gaming. A cost-effective optical CMOS sensor in laser mice with an acceptable theoretical accuracy has become invaluable in numerous applications. Moreover, the use of optical mice has been proposed in various applications, such as in displacement sensors [11–18]. However, several disadvantages limit the industrial application of laser mice; for example, they are extremely sensitive to the measurement surface , can lose focus because of focal distance changes [12, 13], and exhibit poor performance while in circular trajectories [11–15]. Most importantly, neither the accuracy nor precision of laser mice is satisfactory for specific industrial applications, such as in machine tools. Nevertheless, with advances in both hardware and image processing techniques due to highly competitive industry, the laser mouse has considerable potential to serve as an embedded sensor for machines in various industrial applications .
Several factors influence the accuracy of DIC, most of which have been thoroughly investigated. These factors can be categorized into three aspects: the hardware, such as the lens ; the image processing methods, such as the subpixel algorithm, subset size, subpixel intensity interpolation scheme, and subset shape function [20–22]; and the speckle pattern. Previous study proved the feasibility that using varying image processing methods may improve the displacement accuracy and then the measuring system can be utilized in linear guideway monitoring , although further detailed investigation and continuous improvements are required.
In this study, a CMOS sensor was mounted on a fixture that not only maintains the focal distance at approximately 2.6 mm but also shields out the noise due to environmental light. This approach leverages the short focal distance feature of the laser mouse sensor. Hardware such as CMOS sensors can be continually upgraded because of advances in the highly competitive CMOS industry; therefore, image processing for DIC and the speckle pattern are the remaining elements of the displacement sensing system that may benefit from accuracy and precision improvements.
The quality of images captured by the CMOS sensor varies according to the image processing method employed. In this study, techniques such as interpolation, edge detection, and contrast adjustment were adopted and a nonrepetitive speckle pattern was designed and optimized. These approaches maximize the image discrimination for the calculation of the later displacement through image correlation. Conventional image processing methods were adopted in this study; however, the focus was placed on the selection and integration of suitable methods matched with a carefully optimized speckle pattern. The objective was to construct a displacement sensing system with an accuracy and a precision of < 5 µm, which would enable its use in novel industrial applications, such as machine tools.
In addition to hardware and image processing techniques, a unique speckle pattern plays a crucial role in DIC. In this study, the correlation between speckle images captured before and after a test specimen deformed were calculated for displacement measurement based on DIC. A speckle pattern comprises tiny dots distributed randomly in the displacement measurement range. Numerous techniques, such as paint spraying [22–24], chemical etching of the object surface , and laser beam etching , have been proposed to create the speckle pattern on the surface of test specimens. In the current study, to achieve well-controlled image contrasts and speckle sizes and to fulfill the requirement of reproducibility and consistency, the speckle pattern was computer-generated and printed out on a piece of paper by using a laser printer.
The improvement in displacement measurement performance of this sensing system after image processing and speckle pattern optimization was assessed experimentally. This paper describes the utilization of an optical sensor for the on-line monitoring of the thermal deformation of a feed drive system and highlights the potential of the proposed displacement sensing system. The remainder of this paper is structured as follows. Sections 2 and 3 present a brief discussion on the working principles of the laser mouse sensor and the displacement measurement method based on DIC, respectively. Section 4 describes the image processing techniques adopted to improve the quality of images acquired from the adopted CMOS sensor. The speckle pattern that enhances the resolution in displacement measurement is also described in this section. Section 5 describes the experimental assessment of the performance of the proposed laser-mouse-based displacement sensor system. Section 6 summarizes the results and contributions of this study.