In this study, surface error calculations and stability conditions are presented for milling operations in case of slender parts. The dynamic behavior of the flexible beam-type workpiece is modeled by means of finite element method (FEM), while the varying dynamical properties related to the feed motion as well as the material removal process are incorporated in the model. The FEM-generated direct frequency response function is verified through a closed-form solution based on the distributed transfer function method. Relative errors and convergence of the FEM are investigated based on the analytical solutions of the continuum model, from which appropriate element size and mode number can be selected for modal coordinate transformations. The pattern in the variation of the natural frequencies is explored using the analytical model in case of high radial depth of cut relative to the original cross section of the beam-like workpiece. Both the stability conditions and the resulted surface errors are predicted as a function of the tool position. The presented approach and the results are validated by laboratory tests.

The dynamic performance of the steel–Basalt fiber polymer concrete (BFPC) machine tool joint surface (referred to as the joint surface) has a significant impact on the overall BFPC machine tool performance; however, its dynamic characteristics remain unclear. In order to solve this problem, the influence of roughness and surface pressure on the dynamic performance of joint surface was studied experimentally, and a neural network prediction model for the dynamic performance of the joint surface was established. A BFPC bed was designed and manufactured, and BFPC bed’s dynamic performance was tested experimentally. The finite element simulation model of BFPC bed was established with equivalent spring-damper element. The BFPC bed’s dynamic performance without considering the influence of the joint surface and considering the influence of the joint surface was studied separately. The results show that the maximum error of the natural frequency of the BFPC bed was 6.937% considering the influence of the joint surface, which was much lower than the error without considering the influence of the joint surface. The maximum amplitude error of the X-axis and Z-axis acceleration of the BFPC bed was 6.917% and 5.15%, which were much smaller than the error without considering the influence of the joint surface. It proves the accuracy of the neural network prediction model for dynamic performance of the joint surface and the validity of the finite element simulation method for the joint surface. It provides theoretical support for the design analysis of BFPC machine tool.

A parameter identification procedure for identifying the parameters of a volumetric error model of a large machine tool requires hundreds of random volumetric error components in its workspace and thus takes hours of measurement time. It causes thermal errors of a large machine difficult to be tracked and compensated periodically. This paper demonstrates the application of the optimal observation design theories to volumetric error model parameter identification of a large five-axis machine. Optimal designs maximize the amount of information carried in the observations. In this paper, K-optimal designs are applied for the construction of machine-tool error observers by determining locations in the workspace at which 80 components of volumetric errors to be measured so that the model parameters can be identified in 5% of an 8-h shift. Many of optimal designs tend to localize observations at the boundary of the workspace. This leaves large volumes of the workspace inadequately represented, making the identified model inadequate. Therefore, the constrained optimization algorithms that force the distribution of observation points in the machine’s workspace are developed. Optimal designs reduce the number of observations in the identification procedure. This opens up the possibility of tracking thermal variations of the volumetric error model with periodic measurements. The design, implementation, and performance of a constrained K-optimal in tracking the thermal variations of the volumetric error over a 400-min period of operation are also reported. About 70–80% of machine-tool error can be explained using the proposed thermal error modeling methodology.

Recent efforts in smart manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications, and computational platforms, along with statistical methods to collect and analyze the real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem-solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other’s function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized system approach—where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well-established error mitigation frameworks from human factors experts—who have promoted human–system integration for decades—to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.

A new adaptive disturbance feedforward control strategy of the strip thickness in a hot strip rolling mill with online parameter estimation is proposed. The feedforward control strategy makes use of the measured strip temperature and strip entry thickness. To avoid that these disturbances cause a nonuniform strip exit thickness, the Sims’ roll gap model and a linear mill stand deflection model are used to compute control inputs, which compensate for these disturbances. By minimizing the difference between the expected roll force from the model and the measured roll force, uncertain parameters of the model and also errors of the strip tracking are estimated in real time. The estimated parameters are immediately used in the adaptive feedforward controller. Experimental results of the proposed control approach obtained from an industrial hot strip rolling mill show a significant improvement of the strip thickness accuracy compared to the existing standard controllers. The proposed adaptive feedforward control strategy is now in permanent operation at the considered rolling mill.

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

Dynamic milling forces have been widely used to monitor the condition of the milling process. However, it is very difficult to measure milling forces directly in operation, particularly in the industrial scene. In this paper, a dynamic force identification method in time domain, conjugate gradient least square (CGLS), is employed for reconstructing the time history of milling forces using acceleration signals in the peripheral milling process. CGLS is adopted for force identification because of its high accuracy and efficiency, which handles the ill-conditioned matrix well. In the milling process, the tool with high-speed rotation has different transfer functions between tool nose and accelerometers at different angular positions. Based on this fact, the averaged transfer functions are employed to reduce the error amplification of regularization processing for milling force identification. Moreover, in order to eliminate the effect of idling and high-frequency components on identification accuracy, the Butterworth band-pass filter is adopted for acceleration signals preprocessing. Finally, the proposed method is validated by milling tests under different cutting parameters. Experimental results demonstrate that the identified and measured milling forces are in good agreement on the whole time domain, which verifies the effectiveness and generalization of the indirect method for milling force measuring. In addition, the Tikhonov regularization method is also implemented for comparison, which shows that CGLS has higher accuracy and efficiency.

When a cutter traverses a region local to the singularity in 5-axis machining, the stability of machine tool motion may be violated and inevitably lead to a reduction in machining quality and accuracy. In this paper, the underlying cause of the singular machine behaviors is first investigated by differentiating tool path motions, on the basis of the tool path motion expressions in part and machine coordinate systems. A further investigation indicates abrupt kinematic changes to be inevitable when the rotary axes approach a singularity. To eliminate such possible singular risks in 5-axis machining, a local tool path modification method is proposed by adjusting the two rotary axes out of a singular configuration. The critical kinematics smoothing and the consequent gouging concerns resulting from reorientation are comprehensively incorporated in the process of singularity avoidance, by means of a novel tool orientation optimization model. Specifically, the algorithm starts with the determination of an appropriate adjustment range in a simple yet effective manner, and then the primary rotary axis is adjusted in a constrained region away from zero, so as to avoid singularity. After that, the second rotary axis is accordingly adjusted, with no gouging requirements being violated. In this way, singularity problems in 5-axis machining are solved, and both the machine axes kinematics and surface gouging errors are under control. Machining simulation and laboratory experiments were conducted to validate the effectiveness of the proposed method.

Manufacturing technologies deliver products that can suffer from various defects, one of which is internal porosity. Pores are present in most of the parts produced by, e.g., casting, additive manufacturing, and injection molding and can significantly affect the performance of the final products. Due to technological and economic limits, typically porosity cannot be completely removed by optimizing process parameters. It is therefore essential to have a measurement technique that can detect and evaluate these defects accurately. Apart from conventional nondestructive techniques, such as ultrasonic testing or Archimedes’ method that suffer from various limitations, X-ray computed tomography has emerged as a promising solution capable of measuring size, spatial distribution, and shape of pores. In this paper, a method to achieve traceable computed tomography measurements of internal porosity using a reference object with calibrated internal artificial defects is described and demonstrated on an industrial case study. Furthermore, the possibility to improve measurement results by optimizing parameters used for the evaluation of acquired data is discussed. The optimization method is based on an iterative procedure that reduces to ±5 × 10 −5 mm 3 the error of the measured values of total void content in the reference object.

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.

Linear tool path segments of computer numerical control (CNC) machine tools need to be smoothed and interpolated in order to guarantee continuous and steady machining. However, because of the highly nonlinear relation between arc lengths and spline parameters, it is difficult to develop algorithms to simultaneously achieve real-time corner smoothing and interpolation with high-order continuity, although it is important to guarantee both high calculation efficiency and good dynamic performance of high-speed CNC machining. This paper develops a computationally efficient real-time corner smoothing and interpolation algorithm with C 3 continuous feature. The corners at the junction of linear segments are smoothed by inserting Pythagorean-hodograph (PH) splines under the constraints of user-defined tolerance limits. Analytical solutions of the arc length and curvature of the smoothed tool path are obtained by evaluating a polynomial function of the spline parameter. The smoothed tool path is interpolated in real time with continuous and peak-constrained jerk. Simulations and experimental results show that the proposed tool path smoothing and interpolation algorithm can be executed in real time with 0.5 ms control period. Acceleration and jerk continuity of each axis are achieved along the tool path. Comparisons with existing corner smoothing algorithms show that the proposed method has lower jerk than existing C 2 algorithms and the real-time interpolation algorithms based on the Taylor series expansion.

Tool-workpiece deflection is one of the major error sources in machining thin walled structures like blades. The traditional approach in industry to eliminate this error is based on modifying tool positions after measuring the error on the machined part. This paper presents an integrated model of cutting force distribution on the tool–blade contact, automatic update of blade static stiffness matrix without resorting to time-consuming finite element solutions as the material is removed, the prediction and compensation of static deflection marks left on the blade surface. The main focus of the paper is to compensate the deflection errors by respecting the maximum form errors, collision of tool/machine/workpiece, cutting speed limit at the tool tip, and ball end—blade surface contact constraints. The compensation has been carried out by two modules. The first module adjusts the tool orientation along the path to reduce the error by constructing an optimization problem. This module is computationally inexpensive and results in about 70% error reduction based on the conducted experiments. The modified tool path resulted from the first module is fed to the second module for further reduction of the form errors if needed at the violated cutter locations; hence it takes less computational time than the stand alone approach proposed in the literature. The proposed algorithms have been experimentally validated on five-axis finish ball end milling of blades with about 80% reduction in cutting force induced form errors.

Wire electrical discharge machining (WEDM) is a demanding high-precision process for machining of hard-to-machine materials. The main issue is manufacturing errors in shape and radius of small arcs generation. In this paper, a novel model about spark variable gap sizes and nonuniform spark distribution around the wire on arced path machining is first theoretically developed using spark angle domain and WEDM dynamic analysis. Applying spark-force distributed around the wire and resulting wire deflection are estimated by the WEDM conditions influenced by plasma channel specifications, discharge frequency, wire guide clearance, wire tension, and arc radius. Total theoretical arced machining errors including wire deflection and spark gap size variation around the wire interface are calculated based on the proposed model. In addition, machining errors of straight and small arced paths are experimentally analyzed under variation of WEDM influential parameters including discharge frequency, arced path radius (150, 300 and 450 μ m), and wire tension through the statistical full factorial. Comparison of the results for different sets of variable parameters shows that the theoretical values of the arced machining errors can be consistent with the experimental one by a coefficient which depends on the machining conditions and the WED machine type. Finally, based on the theoretical and experimental results, a theoretical algorithm and an operational method with mean accuracy of 84.8% are proposed for predicting and compensating the errors of WEDM on the arced paths. Findings of this research can be used in high-accurate WEDM applications and industries.

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

Additive manufacturing (or three-dimensional (3D) printing) is constantly growing as an innovative process for the production of complex-shape components. Among the seven recognized 3D printing technologies, fused deposition modeling (FDM) covers a very important role, not only for producing representative 3D models, but, mainly due to the development of innovative material like Peek and Ultem, also for realizing structurally functional components. However, being FDM a production process involving high thermal gradients, non-negligible deformations and residual stresses may affect the 3D printed component. In this work we focus on meso/macroscopic simulations of the FDM process using abaqus software. After describing in detail the methodological process, we investigate the impact of several parameters and modeling choices (e.g., mesh size, material model, time-step size) on simulation outcomes and we validate the obtained results with experimental measurements.

The continuing evolution of ultraprecision machining places an increasing need to perform surface measurement in the manufacturing environment. Development of on-machine surface measurement (OMSM) tools for ultraprecision machining processes will enable the reduction of measurement cycle time as well as the potential improvement of machining accuracy. In the present study, an in-house designed interferometer probe is integrated onto an ultraprecision diamond turning machine. System configuration, calibration scheme and various scanning strategies are first presented. The benefit of OMSM preserves the consistency between the machining and measurement coordinate system. Two applications of OMSM for ultraprecision turning process are further investigated. To further improve the surface accuracy, corrective machining is carried out based on the on-machine measured data. The profile accuracy of a cosine curve sample was improved after corrective machining with OMSM. Moreover, process investigation with OMSM was employed to model the effect of process parameters on the form error in ultraprecision cylindrical turning process. OMSM enables the consistent measurement of part coordinates for each experimental run, which is critical for acquiring a deterministic response for empirical modeling. A reduced quadratic model was built by means of response surface methodology and verified by the test for significance of the regression model. The confirmation tests show that the model predicted value conformed to the experimental value with a difference less than 4%.

Understanding the capture efficiency of powder during direct laser deposition (DLD) is critical when determining the overall manufacturing costs of additive manufacturing (AM) for comparison to traditional manufacturing methods. By developing a tool to predict the capture efficiency of a particular deposition process, parameter optimization can be achieved without the need to perform a costly and extensive experimental study. The focus of this work is to model the deposition process and acquire the final track geometry and temperature field of a single track deposition of Ti–6Al–4V powder on a Ti–6Al–4V substrate for a four-nozzle powder delivery system during direct laser deposition with a LENS™ system without the need for capture efficiency assumptions by using physical powder flow and laser irradiation profiles to predict capture efficiency. The model was able to predict the track height and width within 2 μ m and 31 μ m, respectively, or 3.3% error from experimentation. A maximum of 36 μ m profile error was observed in the molten pool, and corresponds to errors of 11% and 4% in molten pool depth and width, respectively. Based on experimentation, the capture efficiency of a single track deposition of Ti–6Al–4V was found to be 12.0%, while that from simulation was calculated to be 11.7%, a 2.5% deviation.

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.

The dimensional accuracy of fused deposition modeling (FDM) machines is dependent on errors caused by processing parameters and machine motions. In this study, an integrated error model combining these effects is developed. Extruder temperature, layer thickness, and infill density are selected as parameters of this study for three FDM machines, namely, Flashforge Finder, Ultimaker 2 go, and XYZ da Vinci 2.0 Duo. Experiments have been conducted using Taguchi method and the interactions between processing parameters are analyzed. Based on the dimensional deviations between fabricated parts and the computer aided design (CAD) geometry, a set of coefficients for the integrated error model are calculated to characterize each machine. Based on the results of the integrated error model, the original CAD geometry is optimized for fabrication accuracy on each machine. New parts are fabricated using the optimized CAD geometries. Through comparing the dimensional deviations of parts fabricated before and after optimization, the effectiveness of the integrated error model is analyzed and demonstrated for the three FDM machines.

An accurate analytical method is normally the preferred choice in engineering practice since this approach usually does not require additional software and can be applied for different situations. A number of analytical methods have been proposed for the air bending process, however, none of them has the capacity to deal with large radius bending. Large radius bending is characterized by a high ratio of the punch radius to the die opening and it is often applied for high-strength steels because of their limited bendability. This bending mode is used to fulfill the imposed level of maximum strain during the forming process. This contribution develops an analytical solution based on the assumption that the bent plate profile can be represented by two straight lines and a circular segment. Three different hardening laws—linear, Swift, and Aerens—are used for the bending moment calculation. Unit moment measurements are used in order to avoid extrapolation of hardening curves obtained by tensile testing. The model is compared with a wide range of experiments using the coefficient of determination, relative and absolute average errors, in addition to the mean standard error. The analytical prediction based on the circular approximation is found to be an accurate and robust tool for the calculation of the major bending characteristics for large radius air bending of high-strength steels.