Calibration is an important way to improve and guarantee the accuracy of machine tools. This paper presents a systematic approach for position independent geometric errors (PIGEs) calibration of five-axis machine tools based on the product of exponentials (POE) formula. Instead of using 4 × 4 homogeneous transformation matrices (HTMs), it establishes the error model by transforming the 6 × 1 error vectors of rigid bodies between different frames resorting to 6 × 6 adjoint transformation matrices. A stable and efficient error model for the iterative identification of PIGEs should satisfy the requirements of completeness, continuity, and minimality. Since the POE-based error models for five-axis machine tools calibration are naturally complete and continuous, the key issue is to ensure the minimality by eliminating the redundant parameters. Three kinds of redundant parameters, which are caused by joint symmetry information, tool-workpiece metrology, and incomplete measuring data, are illustrated and explained in a geometrically intuitive way. Hence, a straightforward process is presented to select the complete and minimal set of PIGEs for five-axis machine tools. Based on the established unified and compact error Jacobian matrices, observability analyses which quantitatively describe the identification efficiency are conducted and compared for different kinds of tool tip deviations obtained from several commonly used measuring devices, including the laser tracker, R-test, and double ball-bar. Simulations are conducted on a five-axis machine tool to illustrate the application of the calibration model. The effectiveness of the model is also verified by experiments on a five-axis machine tool by using a double ball-bar.

Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes high-speed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

A three-dimensional (3D) inner surface inspection system is developed in this research based on circle-structured light, which is an improved laser triangulation method. A conical reflector is used to reflect the laser and generate radial laser plane that is called circle-structured light, and a CCD camera is used to capture the light stripe on the inner surface. Then, the 3D coordinates of points on the light stripe are calculated through laser triangulation algorithm. Compared with existing inner surface measurement systems, this research takes assembly errors and refraction distortion into consideration and proposes a laser plane mathematical model with four degrees-of-freedom along with corresponding flexible laser plane calibration technique based on binocular vision that is easy to operate. The proposed inspection system calibrated by proposed algorithm performs well in diameter measurement experiment, in which the absolute error is superior to 3 μm, and defect detecting experiment, in which the defect resolution is superior to 0.02 mm. Moreover, the system also performs well in straightness and roundness evaluation. Experiments indicate that this system is applicable in inner surface measurement and inspection, and the calibration method is accurate and easy to operate.

A general calibration method of cutter runout and specific cutting force coefficients (SCFCs) for flat-end cutter is proposed in this paper, and a high accuracy of cutting force prediction during peripheral milling is established. In the paper, the cutter runout, the bottom-edge cutting effect, and the actual feedrate with limitation during large tool path curvature are concerned comprehensively. First, based on the trochoid motion, a tooth trajectory model is built up and an analytical instantaneous uncut chip thickness (IUCT) model is put forward for describing the cutter/workpiece engagement (CWE). Second, a noncontact identification method for cutter runout including offset and inclination is given, which constructs an objective function by using the cutting radius relative variation between adjacent teeth, and identifies through a numerical optimization method. Thirdly, with consideration of bottom-edge cutting effect, the paper details a three-step calibration procedure for SCFCs based on an enhanced thin-plate milling experiment. Finally, a series of milling tests are performed to verify the effectiveness of the proposed method. The results show that the approach is suitable for both constant and nonconstant pitch cutter, and the generalization has been proved. Moreover, the paper points out that the cutter runout has a strong spindle speed-dependent effect, the milling force in cutter axis direction exists a switch-direction phenomenon, and the actual feedrate will be limited by large tool path curvature. All of them should be considered for obtaining an accurate milling force prediction.

Springback is an important issue for the application of advanced high-strength steels (AHSS) in the automobile industry. Various studies have shown that it is an effective way to predict springback by using path-dependent material models. The accuracy of these material models greatly depends on the experimental test methods as well as material parameters calibrated from these tests. The present cyclic sheet metal test methods, like uniaxial tension–compression test (TCT) and cyclic shear test (CST), are nonstandard and various. The material parameters calibrated from these tests vary greatly from one to another, which makes the usage of material parameters for the accurate prediction of springback more sophisticated even when the advanced material model is available in commercial software. The focus of this work is to compare the springback prediction accuracy by using the material parameters calibrated from tension–compression test or cyclic shear test, and to further clarify the usage of those material parameters in application. These two types of nonstandard cyclic tests are successfully carried out on a same test platform with different specimen geometries. One-element models with corresponding tension–compression or cyclic shear boundary conditions are built, respectively, to calibrate the parameters of the modified Yoshida–Uemori (YU) model for these two different tests. U-bending process is performed for springback prediction comparison. The results show, for dual phase steel (DP780), the work hardening stagnation is not evident by tension–compression tests at all the prestrain levels or by cyclic shear test at small prestrain γ = 0.20 but is significantly apparent by cyclic shear tests at large prestrain γ = 0.38, 0.52, 0.68, which seems to be a prestrain-dependent phenomenon. The material parameters calibrated from different types of cyclic sheet metal tests can vary greatly, but it gives slight differences of springback prediction for U-bending by utilizing either tension–compression test or cyclic shear test.

Uncertainty quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. Although it has been successfully applied to computer simulation models in areas such as structural engineering, climate forecasting, and medical sciences, this powerful research area is still lagging behind in materials simulation models. These are broadly defined as physics-based predictive models developed to predict material behavior, i.e., processing-microstructure-property relations and have recently received considerable interest with the advent of emerging concepts such as Integrated Computational Materials Engineering (ICME). The need of effective tools for quantifying the uncertainties associated with materials simulation models has been identified as a high priority research area in most recent roadmapping efforts in the field. In this paper, we present one of the first efforts in conducting systematic UQ of a physics-based materials simulation model used for predicting the evolution of precipitates in advanced nickel–titanium shape-memory alloys (SMAs) subject to heat treatment. Specifically, a Bayesian calibration approach is used to conduct calibration of the precipitation model using a synthesis of experimental and computer simulation data. We focus on constructing a Gaussian process-based surrogate modeling approach for achieving this task, and then benchmark the predictive accuracy of the calibrated model with that of the model calibrated using traditional Markov chain Monte Carlo (MCMC) methods.

In the tool orientation planning for five-axis sculptured surface machining, the geometrical constraints are usually considered. Actually, the effect of nongeometrical constraints on tool orientation planning is also important. This paper studied one nongeometrical constraint which was cutting force induced static deflection under different tool orientations, and proposed a cutter deflection model based on that. In the study of the cutting force, the undeformed chip thickness in filleted end milling was modeled by geometrical analysis and coordinate transformation of points at the cutting edge. In study of static flexibility of multi-axis machine, static flexibility of the entire machining system was taken into consideration. The multi-axis machining system was divided into the transmission axes-handle (AH) end and the cutting tool end. The equivalent shank method was developed to calculate the static flexibility of the AH end. In this method, static flexibility anisotropy of the AH end was considered, and the equivalent lengths of the AH end were obtained from calibration experiments. In cutter deflection modeling, force manipulability ellipsoid (FME) was applied to analyze the static flexibility of the AH end in arbitrary directions. Based on the synthetic static flexibility and average cutting force, cutter deflections were derived and estimated through developing program realization. The predicted results were compared with the experimental data obtained by machining 300 M steel curved surface workpiece, and a good agreement was shown, which indicated the effectiveness of the cutter deflection model. Additional experiments of machining flat workpiece were performed, and the relationship of cutter deflections and tool orientations were revealed directly. This work could be further employed to optimize tool orientations for suppressing the surface errors due to cutter deflections and achieving higher machining accuracy.

Mechanistic force prediction models require a calibration phase to determine the cutting coefficients describing the tool–target material interaction. The model prediction performance depends on the experimental correctness and representativeness of input data, especially in micromilling, where facing process uncertainties is a big challenge. The present paper focuses on input data correctness introducing a clear and repeatable calibration experimental procedure based on accurate force data acquisitions. Input data representativeness has been directly connected to the calibration window choice, i.e., the selection of the space of process parameters combinations used to calibrate the model. Also, the model validation has to be carefully carried out to make the model significant: the present paper proposes a clear and repeatable validation procedure based on the model performance index calculation over the whole process operating window, i.e., the space of parameters where the process works correctly. An objective indication of the model suitability can be obtained by applying this procedure. Comparisons among prediction performances produced by different calibration windows are allowed. This paper demonstrates how the calibration window selection determines the model prediction performance, which seems to improve if calibration is carried out where forces assume high values. Some important considerations on the process parameters role on cutting forces and on the model capability have also been drawn from the model validation results.

This paper proposes an assembly system for ultraviolet-lithographie galvanik abformung (UV-LIGA) parts with a robotic manipulator. Both images of base part and object part could be obtained simultaneously from an in-house orthogonal optical alignment vision system. Two microgrippers were introduced to realize the reliable clamping. An initial calibration method was presented to ensure assembly accuracy. Assembly experiments were conducted with success rates of 80% and the time consumption of 20 min for all four parts assembly. Suspected causes of failure are motion mechanisms' uncertainty, part dislocation resulted from inertia force when microgripper is moving, and the error which is produced in the detection process because of random factors.

For on-machine measurement of workpiece position, orientation, and geometry on machine tools, five-axis continuous (scanning) measurement by using a laser displacement sensor has a strong advantage in its efficiency, compared to conventional discrete measurement using a touch-triggered contact probe. In any on-machine measurement schemes, major contributors to their measurement uncertainty are error motions of the machine tool itself. This paper formulates the influence of geometric errors of rotary axis average lines on the measurement uncertainty of the five-axis on-machine measurement by using a laser displacement sensor. To validate the present simulator, experimental comparison of measured and simulated trajectories is conducted on five-axis on-machine measurement of a precision sphere of the precalibrated geometry. For total 28 paths measured on the spherical surface, an error in the simulated trajectories from measured trajectories (properly low-pass filtered) was at maximum 5 μm. Uncertainty assessment demonstration for more practical application example of a turbine blade measurement is also presented.

Most additive manufacturing (AM) processes are layer-based with three linear motions in the X, Y, and Z axes. However, there are drawbacks associated with such limited motions, e.g., nonconformal material properties, stair-stepping effect, and limitations on building-around-inserts. Such drawbacks will limit AM to be used in more general applications. To enable 6-axis motions between a tool and a work piece, we investigated a Stewart mechanism and the feasibility of developing a low-cost 3D printer for the multidirectional fused deposition modeling (FDM) process. The technical challenges in developing such an AM system are discussed including the hardware design, motion planning and modeling, platform constraint checking, tool motion simulation, and platform calibration. Several test cases are performed to illustrate the capability of the developed multidirectional AM system. A discussion of future development on multidirectional AM systems is also given.

Previous methods of measuring high velocities, e.g., during electromagnetic forming (EMF) and magnetic pulse welding processes where the workpiece is deforming, include photon Doppler velocimetry (PDV), laser micrometers, and high speed photography. In this paper, an alternative method is presented, implementing a fiber optic, reflectance dependent sensor. The sensor is shown to be an attractive, low purchase cost solution to measure high velocities. Data are shown with respect to sensor characterization including various surface reflectivity values, curvatures, and misalignments; implementation in two EMF/welding processes; and verification with high velocity PDV measurements. The sensor system is one twentieth the purchase cost of a PDV system, and yet measures velocities accurately (using PDV measurements as the reference) to at least 150 m/s provided that local curvature is not extreme and the displacement is less than approximately 27 mm. Sensor performance is also enhanced by the use of retroreflective tape, which is shown to increase the displacement range by 9×, decrease sensitivity to misalignment, and increase repeatability and ease of implementation.

Accurate numerical modeling of laser shock processing, a typical complex physical process, is very difficult because several input parameters in the model are uncertain in a range. And numerical simulation of this high dynamic process is very computational expensive. The Bayesian Gaussian process method dealing with multivariate output is introduced to overcome these difficulties by constructing a predictive model. Experiments are performed to collect the physical data of shock indentation profiles by varying laser power densities and spot sizes. A two-dimensional finite element model combined with an analytical shock pressure model is constructed to obtain the data from numerical simulation. By combining observations from experiments and numerical simulation of laser shock process, Bayesian inference for the Gaussian model is completed by sampling from the posterior distribution using Morkov chain Monte Carlo. Sensitivities of input parameters are analyzed by the hyperparameters of Gaussian process model to understand their relative importance. The calibration of uncertain parameters is provided with posterior distributions to obtain concentration of values. The constructed predictive model can be computed efficiently to provide an accurate prediction with uncertainty quantification for indentation profile by comparing with experimental data.

Process physics understanding, real time monitoring, and control of various manufacturing processes, such as battery manufacturing, are crucial for product quality assurance. While ultrasonic welding has been used for joining batteries in electric vehicles (EVs), the welding physics, and process attributes, such as the heat generation and heat flow during the joining process, is still not well understood leading to time-consuming trial-and-error based process optimization. This study is to investigate thermal phenomena (i.e., transient temperature and heat flux) by using micro thin-film thermocouples (TFTC) and thin-film thermopile (TFTP) arrays (referred to as microsensors in this paper) at the very vicinity of the ultrasonic welding spot during joining of three-layered battery tabs and Cu buss bars (i.e., battery interconnect) as in General Motors's (GM) Chevy Volt. Microsensors were first fabricated on the buss bars. A series of experiments were then conducted to investigate the dynamic heat generation during the welding process. Experimental results showed that TFTCs enabled the sensing of transient temperatures with much higher spatial and temporal resolutions than conventional thermocouples. It was further found that the TFTPs were more sensitive to the transient heat generation process during welding than TFTCs. More significantly, the heat flux change rate was found to be able to provide better insight for the process. It provided evidence indicating that the ultrasonic welding process involves three distinct stages, i.e., friction heating, plastic work, and diffusion bonding stages. The heat flux change rate thus has significant potential to identify the in-situ welding quality, in the context of welding process monitoring, and control of ultrasonic welding process. The weld samples were examined using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) to study the material interactions at the bonding interface as a function of weld time and have successfully validated the proposed three-stage welding theory.

Many pieces in structure of a dynamometer may negatively affect the accuracy of the measurements since each piece may display a different strain and stress depending on its material and construction. A one piece dynamometer has been designed to eliminate these negative effects and to facilitate reliable force measurements. A dynamometer that its dimensions were confirmed following strength calculations was manufactured and calibrated in two different ways. The first one was multipoint calibration method in which certain loads were applied to the dynamometer and strain values corresponding to these loads were matched. The second calibration method was implemented using Kistler dynamometer that its results are accepted to be accurate by everyone and it was based on equivalency of force values resulted from work piece processing and force values resulting from machining work pieces with the same parameters to the manufactured dynamometer. The manufactured dynamometer was capable of measuring cutting forces and feeding forces.

For a range of precision machining and micromachining operations, the crystallographic anisotropy plays a critical role in determining the machining forces. Part II of this work presents the calibration and validation of the rate-sensitive plasticity-based machining (RSPM) model developed in Part I. The five material parameters, including four hardening parameters and the exponent of rate sensitivity, for both single-crystal aluminum and single-crystal copper are calibrated from the single-crystal plunge-turning data using a Kriging-based minimization approach. Subsequently, the RSPM model is validated by comparing the specific energies obtained from the model to those from a single-crystal cutting test. The RSPM model is seen to capture the experimentally observed variation of specific energies with crystallographic anisotropy (orientation), including the mean value, symmetry, specific trend, amplitude, and phase of the peak specific energy. The effects of lattice rotation, hardening, and material-parameter variations on the predicted specific energies is then analyzed, revealing the importance of both lattice rotation and hardening in accurately capturing the specific energies when cutting single-crystals. Using the RSPM model, the effects of crystallographic orientation, rake angle and friction angle on specific energies are also analyzed. Lastly, a simplified model that uses Merchant’s shear angle, thereby circumventing the minimization procedure, is constructed and evaluated.

Developed in this paper is a hybrid method for calibration of modular reconfigurable robots (MRRs). The underlying problem under study is unique to MRRs, that is, how to calibrate a set of MRR’s geometric parameters that are applicable to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace, and configuration-space. The final set of global solutions is generated progressively from the results of these three levels of search. The effectiveness of this method is demonstrated through a case study.

Dimensional inspection using a contact-based coordinate measurement machine (CMM) is time consuming because the part can only be measured point-by-point. A 3D sensor may replace the traditional CMM technology because it can measure a surface patch-by-patch. Therefore, the automotive industry has been seeking a practical solution for rapid surface inspection using a 3D sensor. However, the challenge is the capability to meet all the requirements including sensor accuracy, resolution, system efficiency, and system cost. In this paper, we develop a robot-aided 3D sensing system, which can automatically allocate sensor viewing points, measure the freeform part surface, and generate an error map for quality control. The measurement accuracy meets the industrial standards. This paper briefly describes the system architecture, design principle, calibration methods, and the present results.

The pitch accuracy of a gear is graded on the order of 0.1 μ m in ISO 1328-1; therefore, it is necessary for gear measuring instruments (GMIs) to be able to measure gears with the required high accuracy. GMIs are evaluated by measuring a calibrated gear or a gearlike artifact. It is, however, difficult to obtain a measurement uncertainty of less than 0.1 μ m . The reason for this difficulty is that a gear artifact has a form error and surface roughness, and that the measurement position on the gear face differs slightly from the calibrated position. In view of this situation, we propose a novel multiball artifact (MBA), which is composed of equally spaced pitch balls, a centering ball, and a datum plane. The pitch balls are assumed to act as gear teeth by calibrating the angular pitch between the centers of each pitch ball. The centering ball and the datum plane are used to set a reference axis of the virtual gear. We manufactured an MBA with the pitch balls arranged on a curvic coupling. The angular pitch deviation between the centers of each pitch ball was calibrated using a coordinate measuring machine (CMM) and adopting the multiple-orientation technique. A master gear was also calibrated for comparison. The measurement uncertainty for the cumulative angular pitch deviation was 0.45 arc sec for the MBA and 1.58 arc sec for the master gear. The MBA could be calibrated with small uncertainty compared with the master gear. After the calibration, a virtual gear of the MBA was built using the calibration value. The virtual gear was measured using the gear-measuring software on the CMM. The measurement value was equal within the range of uncertainty of calibration value. It is verified that the superiority of the MBA to the gear artifact is due to the following reasons: (1) The balls can be manufactured with an accuracy of several tens of nanometers. (2) The calibrated result for the MBA is almost independent of a probe-positioning error because the centers of each pitch ball can be measured at multiple points. (3) In setting the reference axis, the gear artifact generally uses a datum cylinder, in contrast, the MBA uses more accurate ball.

Dimensional measurement feedback in manufacturing systems is critical in order to consistently produce quality parts. Considering this, methods and techniques by which to accomplish this feedback have been the focus of numerous studies in recent years. Moreover, with the rapid advances in computing technology, the complexity and computational overhead that can be feasibly incorporated in any developed technique have dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. More specifically, research publications pertaining to the use of coordinate measurement machines to improve the machining process are discussed.