Measuring of positioning, circularity and static errors of a CNC Vertical Machining Centre for validating the machining accuracy

Usop, Zuriani; Sarhan, Ahmed A.D.; Mardi, N.A.; Wahab, Nizam Abd

doi:10.1016/j.measurement.2014.10.025

Cited by 27 publications

(12 citation statements)

References 8 publications

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“…Finally, a tighter integration while maintaining the modular characteristics of the system would be a huge improvement, such as linking the layout planning module directly to the robotic arm and CNC machining simulation for a better material flow. With these improvements, previously developed methodologies for validation 60 can be used to determine the simulation accuracy.…”

Section: Discussionmentioning

confidence: 99%

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Pai

Yap

Dawal

et al. 2016

Sci Rep

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This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

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Section: Discussionmentioning

confidence: 99%

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Pai

Yap

Dawal

et al. 2016

Sci Rep

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“…For the bending deformation of the beam, the relationship between Z directional deformation and flexural rigidity has been presented by equation (3).…”

Section: Fdm-based Correction Model Of the Results Of Fem Simulationmentioning

confidence: 99%

“…Static errors of heavy-duty machine tools were detected and compensated by laser instruments to improve the accuracy of large computer numerical control (CNC) machine tools. 2,3 Urbikain et al 4 established models in heavy-duty turning operations for a large horizontal lathe to identify the machine design features limiting lathe productivity, as well as the threshold values for choosing good cutting parameters. The accuracy of heavy-duty machine tools is primarily affected by the geometric errors caused by imperfections, misalignments, wear of the linkages, and elements of the machine tool structure by static loadinduced errors.…”

Section: Introductionmentioning

confidence: 99%

Finite difference method–based calculation of gravity deformation curve for the large-span beam of heavy-duty vertical lathe

Han

Wan-jin

Shao

et al. 2016

Advances in Mechanical Engineering

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To solve the problem that gravity deformation curve of large-span cast-iron beam analyzed by finite element method simulation is inaccurate due to material imperfection, a discretization calculation considering the inhomogeneity of the material based on finite difference method is proposed. Supposing the flexural rigidity of the beam is different along the length, the continuous beam is discretized into segments based on finite difference method, and equivalent flexural rigidity is presented to characterize the inhomogeneity of the material. Correction model of bending deformation is constructed to revise the results of finite element method simulation applying equivalent flexural rigidity that could be obtained by combining the discretization model and deformation data acquired in a simple self-load experiment in which the beam is simply supported without any assembly process. Finally, flowchart of application is presented, and the approach is illustrated through an example from real case. The experimental results show that the computational accuracy is improved from 73.14% to 88.33%, compared with just finite element method simulation.

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“…This section discusses how kernel density estimation (KDE) functions can be used to automatically capture the pattern in historical runs, and exemplified by the runs in Figure 4. Since CNC machine tools are highly repeatable machines [28], it is expected that two runs of the same program should yield very similar results (i.e., be repeatable). For such a repeatable processes, knowing the region in space where the data should be can be used for monitoring purposes.…”

Section: Kernel Density Estimationmentioning

confidence: 99%

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Bernard

Achiche

Girard³

et al. 2021

JMMP

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Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.

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Measuring of positioning, circularity and static errors of a CNC Vertical Machining Centre for validating the machining accuracy

Cited by 27 publications

References 8 publications

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Finite difference method–based calculation of gravity deformation curve for the large-span beam of heavy-duty vertical lathe

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

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