On-machine and in-process surface metrology for precision manufacturing
Introduction
As an important discipline of manufacturing metrology, surface metrology is defined by Whitehouse as the measurement of the deviations of a workpiece from its intended shape specified in the design drawing [241]. Surface metrology includes the measurement of surface texture and surface form errors, specified, for example, by roundness, straightness, together defined here as surface topography [136]. Since the function and performance of a workpiece, as an element in a machine or a mechanical system, are strongly influenced by its surface topography, surface metrology is an essential operation in manufacturing of the workpiece. Both the instrumentation and characterization technologies of surface metrology have been well established over the long history of manufacturing. A large variety of commercially-available surface measuring instruments, from the mechanical stylus profilers [240] to the noncontact optical instruments [94], [132] to scanning probe microscopes [33], can be selected to examine the surface of a workpiece for acquiring cross-sectional profile or areal topography data of the surface. Software associated with the instruments or stand-alone software packages can be employed to calculate the surface parameters defined by international standards for quantitative characterization of the surface [36], [111]. A calibration and verification infrastructure has also been developed to support surface metrology [134], although the relevant international standards are still under development. In addition, surface metrology has been expanded to cover complex surfaces, such as structured surfaces [45] and freeform surfaces [56], [195].
Surface metrology is an important part of post-manufacturing inspection of a manufactured workpiece, which is typically carried out in an environmentally well-controlled metrology room, to determine whether the surface parameters of the workpiece meet the designed requirements for the purpose of quality control of the workpiece. On the other hand, as stated by McKeown, the maximum efficiency of quality control occurs when the measurement is carried out at the closest possible point to the manufacturing process, e.g. on-machine and in-process surface metrology [164]. This kind of practice has long been observed in precision manufacturing of telescope mirrors, due to the tight tolerances of surface parameters and the large mirror size [35], [84], [211]. For instance, the process chain of manufacturing a 4.2 m off-axis aspheric telescope mirror was composed of casting, generating, grinding, polishing and figuring process stages [182]. Different measuring instruments, including laser trackers, IR and visible deflectometry and computer generated hologram (CGH) null interferometry were employed to acquire the surface form error data at different stages of the process chain. Meanwhile, the surface waviness and roughness data were acquired by sub-aperture deflectometry and temporal phase-shifting interferometry, respectively, in the polishing and figuring stages. The measurement data were employed in the compensation machining to ensure that the mirror surface satisfies the required form and roughness tolerances.
In addition to quality control of workpieces, on-machine and in-process surface metrology are also effective for control of manufacture through optimizing the manufacturing process and the machine tool settings, based on the fact that the surface texture is representative of the process characteristics and the surface form errors are footprints of the machine tool imperfections; i.e. vibration, geometric error motions and thermal distortion [111], [241]. It is becoming more feasible to implement surface metrology on the machine tool and in the manufacturing process with improvements in the stability of the machine tool [80]. In recent years, on-machine and in-process surface metrology is playing an increasingly important role in the process chain of traditional manufacturing that is composed of cutting, grinding and polishing for precision workpieces with complex shapes and/or extremely tight tolerances, such as freeform optics in head-up displays, large roll moulds for roll-to-roll replication and turbine blades of airplanes [27], [108], [220]. Similar trends are observed in the non-traditional manufacturing processes, such as additive manufacturing [199] and nano-scale manufacturing [54], [55].
The classifications, tasks, and requirements of on-machine and in-process surface metrology for precision manufacturing, as well as the associated measuring instruments and sensor technologies, are presented in this paper, as a complement to the past CIRP keynote papers on measurement of surfaces. Error separation algorithms for compensation of kinematic or dynamic errors of the machine tools are overviewed, followed by a discussion on the issues of calibration and traceability. The data flow in the manufacturing and measurement system, from both software and hardware points of view, will then be reviewed. It should be noted that the measurement of dimensions and surface integrity, although both important aspects dominating the function of workpiece surfaces, will not be covered in this paper. Since in-process is the ideal case of on-machine condition, it is treated as an independent term in the title of the paper to emphasize the importance of in-process surface metrology.
Section snippets
Classifications
In addition to “on-machine” and “in-process”, the terms “in-situ” and “in-line/on-line” are frequently used to describe the condition of a measurement activity, not limited to measurement of workpiece surface, in manufacturing metrology. However, there are no clear classifications for these terms. This can often lead to confusion and misunderstanding when specifying a manufacturing metrology activity. For example, “in-situ” is used as synonymous of “on-machine” and “in-process” in some
On-machine measurement systems
The on-machine measurement systems to be presented in this section are listed in Fig. 8 with a brief summary of the major types of surfaces and environment that the systems can be applied for.
Measurement systems for on-machine and in-process surface metrology can be classified into full-field systems and probe-scan systems. In an on-machine full-field system, which is usually an optical system, a light beam is projected onto a certain area of the workpiece surface for capturing the
Calibration and traceability
Traceability is defined in Ref. [105] as “the property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty” (see also Refs. [43], [88]). For on-machine and in-process measurements, this implies that for traceability to be demonstrated, a number of calibration steps have to be taken. Calibration is defined as “an operation that, under specified conditions, in a first step,
Data flow
Data flow (also called stream processing or reactive programming) is a software paradigm describing how data streams are processed using computing into useful information, e.g a measurement result. The central element of the concept is the data, which flows through several steps (Fig. 31). This model is naturally described by flowchart diagrams and allows for a simple description of parallel and sequential processes with focus on data. The flow of data from one processing step to the next and
Conclusions
On-machine surface metrology plays an important role for precision manufacturing in a number of tasks. The first task is for initial alignment and/or positioning of a workpiece on the machine tool, which is the first step in a manufacturing process. The most straightforward task of on-machine surface metrology is to replace the conventional post-manufacturing inspection of the workpiece surface made on an off-machine and stand-alone surface measuring instrument. The on-machine results can also
Acknowledgements
The fruitful discussions and suggestions from many colleagues in CIRP STCs P and S are appreciated. H. Martin and X. Jiang of Huddersfield University read and commented on the manuscript. D.W. Kim of Arizona University, L.C. Chen of NTU, S. Shirakawa of Heidenhain KK, K. Taknaka and M. Fukuda of Toshiba Machine, M. Tano of JTEKT, Y. Nagaike of Olympus, K. Kamiya of Toyama Prefectural University, G. Chapman of Moore Nanotech, H. Ogawa of Enable KK, A. Archenti and K. Szipka of KTH, R. Mayer of
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