Ultra-precision Machining Process Dynamics and Surface Quality Monitoring

Cheng, Changqing; Wang, Zimo; Hung, Wayne; Bukkapatnam, Satish T. S.; Komanduri, R.

doi:10.1016/j.promfg.2015.09.044

Cited by 39 publications

(17 citation statements)

References 5 publications

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“…This case study is aimed at evaluating the performances of dynamic network algorithms for quality inspection of imaging profiles from the UPM process (see Figure A). It is generally known that UPM produces mirror finish surfaces, which are used for a variety of engineering applications, including sensors, laser scanners, computer hard drives, medical equipment and optical elements in aerospace industries . The UPM process uses a single crystal diamond tool to remove metal materials at the depth of cut in the micrometer range, i.e., 1˜50 μm, which can achieve surface finish ( R a ) in the nanometer range.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…It is generally known that UPM produces mirror finish surfaces, which are used for a variety of engineering applications, including sensors, laser scanners, computer hard drives, medical equipment and optical elements in aerospace industries. [32][33][34] The UPM process uses a single crystal diamond tool to remove metal materials at the depth of cut in the micrometer range, i.e., 1˜50 μm, which can achieve surface finish (R a ) in the nanometer range. In the industry practice, real-world uncertainty factors such as microstructure heterogeneity, environmental changes and process drifts often cause the variations of surface finishes in UPM machining (see Figure 12).…”

Section: Case Study In Upmmentioning

confidence: 99%

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Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

Chen

Yang

2017

Quality & Reliability Eng

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In modern industries, advanced imaging technology has been more and more invested to cope with the ever‐increasing complexity of systems, to improve the visibility of information and enhance operational quality and integrity. As a result, large amounts of imaging data are readily available. This presents great challenges on the state‐of‐the‐art practices in process monitoring and quality control. Conventional statistical process control (SPC) focuses on key characteristics of the product or process and is rather limited to handle complex structures of high‐dimensional imaging data. New SPC methods and tools are urgently needed to extract useful information from in situ image profiles for process monitoring and quality control. In this study, we developed a novel dynamic network scheme to represent, model, and control time‐varying image profiles. Potts model Hamiltonian approach is introduced to characterize community patterns and organizational behaviors in the dynamic network. Further, new statistics are extracted from network communities to characterize and quantify dynamic structures of image profiles. Finally, we design and develop a new control chart, namely, network‐generalized likelihood ratio chart, to detect the change point of the underlying dynamics of complex processes. The proposed methodology is implemented and evaluated for real‐world applications in ultraprecision machining and biomanufacturing processes. Experimental results show that the proposed approach effectively characterize and monitor the variations in complex structures of time‐varying image data. The new dynamic network SPC method is shown to have strong potentials for general applications in a diverse set of domains with in situ imaging data.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Case Study In Upmmentioning

confidence: 99%

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

Chen

Yang

2017

Quality & Reliability Eng

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“…The research on ultra-precision turning machining processes and residual stress becomes wider and deeper in recent years [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. At present, the combinations of molecular dynamics and experiment or finite element simulation and experiment are adopted in the actual research of ultra-precision turning.…”

Section: Prefacementioning

confidence: 99%

Simulation and experimental study on surface residual stress of ultra-precision turned 2024 aluminum alloy

Liu

Xiong

Zhang

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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In order to reveal the influence of cutting parameters on the surface residual stress of 2024 aluminum alloy in ultra-precision cutting, the thermal coupling simulation of 2024 aluminum alloy ultra-precision cutting process was carried out based on ABAQUS software, and it was verified by the residual stress test of ultra-precision machined samples. The results show that the dominant factor of the machining residual stress of 2024 aluminum alloy ultra-precision with single crystal diamond tool is the plastic deformation caused by cutting force. With the normal ultra-precision machining parameters, the residual stress of the machined surface of 2024 aluminum alloy is mainly compressive stress. With the increase in distance to the machined surface, the residual compressive stress value decreases and gradually transforms into tensile stress, and the total residual stress depth is less than 8 μm. With the increase in cutting depth (0.01-0.03 mm), the reduction of cutting speed (150-50 m/ min) and the increase of feed (0.01-0.024 mm/r), there is an increasing trend of the residual stress on the machined surface of 2024 aluminum alloy workpiece, and the influences of feed rate and cutting depth on residual stress are slightly greater than that of cutting speed.

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“…Precision and ultra‐precision machining technologies have attracted considerable attention in the field of mechanical engineering which offers great advantages over conventional machining techniques in terms of high material removal rate , excellent machining precision , and low surface roughness . In precision and ultra‐precision machining, surface roughness is influenced by many factors, including the machine tool, cutting conditions, tool geometry, environmental conditions, material property, chip formation, tool wear, vibration etc.…”

Section: Introductionmentioning

confidence: 99%

“…The machine tool bed is a fundamental component in precision and ultra‐precision machining machine tools that plays an important role in vibration alleviating. In order to obtain high machining precision, the machine tools must have a high damping property . Cast iron and welded steel are the most widely used materials in machine tool bed ; although they have been acceptable in the past, they can no longer meet the requirements of precision and ultra‐precision machining.…”

Section: Introductionmentioning

confidence: 99%

Effect of fly ash on the overall performance of particulate‐filled polymer composite for precision machine tools

Yin

Bian

et al. 2017

Polymer Composites

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Particulate‐filled polymer composite (PFPC) consisting of epoxy resin, granite aggregate and fly ash (FA), is a new polymer composite that is prepared at room temperature. Exhibiting excellent vibration damping, PFPC has attracted wide attention in the field of precision machine tools. However, the major drawbacks that have limited the use of PFPC in manufacture of precision machine tools include their low mechanical properties, high coefficient of thermal expansion and high water absorption. In this article, the effect of FA content on the mechanical properties, damping ratio, thermal expansion coefficient, and water absorption of PFPC were systematically investigated. The experimental results showed that the compressive strength and elastic modulus of the PFPC initially increased and then decreased with the increase in the FA content. Also, it was found that the flexure strength, average thermal expansion coefficient (ATEC) and the maximum water absorption of the PFPC decreased as the FA content increased. The results also showed that the damping ratio first decreased and then increased with the increasing FA content, where the damping ratio of the PFPC exhibited a slight reduction when the FA content exceeded 5%. The maximum compressive strength, maximum elastic modulus, and the best damping ratio were obtained when the FA content was 5%. Simultaneously, the PFPC with a FA content of 5% exhibited high flexural strength, low thermal expansion, and low water absorption. Thus, the optimum FA content of PFPC was found to be 5% for use in precision machine tool fabrication. POLYM. COMPOS., 39:3986–3993, 2018. © 2017 Society of Plastics Engineers

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Ultra-precision Machining Process Dynamics and Surface Quality Monitoring

Cited by 39 publications

References 5 publications

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

Simulation and experimental study on surface residual stress of ultra-precision turned 2024 aluminum alloy

Effect of fly ash on the overall performance of particulate‐filled polymer composite for precision machine tools

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