Geometric Accuracy Of Parts Research Articles

Numerical evaluation of cutting strategies for thin-walled parts

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

Hybrid offline-online optimization, monitoring and control of milling processes

A hybrid offline-online optimization, monitoring and control (HOMC) system was developed for milling processes. Safe cutting regions (SCRs) are defined based on offline analysis considering process dynamic stability, tool deflection and machined part geometric accuracy and surface quality. Near-optimum cutting conditions are defined offline using the cutter-workpiece contact information along the toolpath. A deep-learning tool condition monitoring approach is developed to detect the wear state in real-time using minimal learning efforts. The HOMC monitors the machining power signals and adaptively controls the feedrate within SCR using online predictions. Validation tests proved better process productivity, part quality and extended tool life.

Improvement of Part Accuracy by Combination of Pulsed Wave (PW) and Continuous Wave (CW) Laser Powder Bed Fusion

In this paper, a processing strategy is investigated to increase the geometric accuracy of parts fabricated by laser powder bed fusion (LPBF). Pulsed wave emission (pw) is used for contour exposure in combination with a continuous wave (cw) emission for bulk volume exposure. With the goal of discrete solidification of adjacent melt pools, process parameters of laser power PL, scanning speed vs, and relative pulse overlap ∆xo are developed for contour exposure. Samples with variable contour angles are fabricated to investigate the effect of the pw contour exposure on excessive melting in critical areas of the part prone to overheating. Geometric accuracy and surface roughness are evaluated using SEM images of surface topography and optical surface roughness measurements, respectively. It is observed that excessive melting can be suppressed by using pw contour exposure and usage of modified process parameters. Due to the discretized energy input in pw emission mode, smaller melt pools with lower melt pool fluctuation and powder erosion are produced. The maximum applicable scanning speed is limited by the solidification time of the melt pool and is significantly lower compared to conventional cw contour exposure parameters. Therefore, a combination of cw volume exposure with high process productivity and pw contour exposure for high geometric accuracy is beneficial to limit productivity losses and increase accuracy in part building.

Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts

Industrial-scale production applications of additive manufacturing (AM) are growing rapidly, and scalable AM production requires quality systems that monitor and control part geometric accuracy across multiple machines that operate within a factory. However, most published research on AM geometric accuracy focuses on a single part or a single set of hardware. This research develops a hybrid hierarchical modeling (HHM) approach to characterize the geometric accuracy of parts produced across multiple identical AM machines. Our approach organizes the geometric accuracy data into a hierarchy that represents data from individual parts, the positions of parts within the builds, and the machines that produced those parts. The part accuracy is modeled as the sum of a part-to-part trend and a small-scale variability at the feature level. A hierarchical Bayesian linear model accounts for this hierarchy and captures how the part-level accuracy depends upon each factor. The small-scale feature-level variability is modeled using Gaussian process (GP) regression. The effectiveness of the proposed method is demonstrated by case studies using experimental data collected from 70 polymer hexagonal lattice parts over seven builds produced by three identical Carbon M2 printers. Each part contains 237 walls, and the wall thickness is measured as the geometric feature. By leveraging data from different printers, the performance of the part-level geometric accuracy modeling is substantially improved compared with competing methods. The average root mean squared error (RMSE) of the part-level modeling is as small as 4.8 µm even when sampling only three of ten parts in one build, demonstrating excellent data efficiency. The part-level modeling method also demonstrates outstanding robustness against sampling randomness with the interquartile ranges of the prediction errors on most positions on a build smaller than 3 µm. The data efficiency is further improved by measuring selected geometric features from each sampled parts and utilizing feature-level modeling. HHM consistently achieves accurate feature-level prediction, with RMSE less than 10.1 µm when the feature measurement density is only 30%. On average, HHM permits a 0.62-µm reduction in RMSE compared to using part-level modeling only. HHM also provides fine-scale information about the statistical distribution of within-part geometric accuracy. We develop empirical rules for the part-level sampling design to improve the robustness of geometric accuracy modeling, which can reduce the RMSE by 78.8% compared with random sampling. The modeling approach is extensible to other types of AM and could be used as part of a quality system within AM factory.

Analysis of UV Curing Strategy on Reaction Heat Control and Part Accuracy for Additive Manufacturing.

In this research, the relationship between the curing strategies and geometrical accuracy of parts under UV light was investigated. An IR camera was utilized to monitor the process using different combinations of photosensitive resin and curing strategies. The influences of curing strategies on different material compositions were studied with single-factor analysis. With the different exposure frequencies of the UV light, the peak temperature was adjusted to avoid overheating. The three-dimensional geometry of casting tensile bars was measured to investigate the shrinkage and warpage during the curing process. Different material compositions were also selected to study the effects of the maximum temperature on the shrinkage of the parts. The findings of this work show that, with the same amount of energy input, a more fragmented exposure allows for a more controllable max temperature, while one-time exposure leads to a high temperature during the process. With the decrease of the released heat from the reaction, the shrinkage of the casting part has a slightly increasing tendency. Moreover, the warpage of the parts decreased drastically with the decrease of temperature. The addition of fillers enhances the control over temperature and increases the geometrical accuracy.

A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process

Finishing determines the final dimension and geometric accuracy of parts, and the finishing process directly affects the stiffness and residual stress redistribution of the workpiece, so the optimisation of the finishing process plays a very important role in deformation control. At present, existing data-driven methods for deformation control need a large amount of labelled training data, which is always a challenge in the manufacturing area, especially for machining deformation. To address the above issues, this paper presents a meta-reinforcement learning model incorporated by simulation and real data, which is trained in a simulation environment with a piecewise sampling strategy for data collection, and can be updated in a real machining environment through a very small number of real monitoring data. The finishing process optimisation for deformation control can be realised using the proposed approach. Finally, the effectiveness of the proposed method is verified both in simulation environment and actual machining, and better results are obtained compared with other existing methods.

Experimental and Numerical Examination on Formability and Microstructure of AA3003-H18 Alloy in Single Point Incremental Forming Process

The single-point incremental forming process has witnessed significant advantages in automobiles, aerospace, and medical applications in recent years because of its flexibility in manufacturing complex shapes. In detail, the components are produced only using the toolpath, which is guided by computer-aided manufacturing software. However, during the forming process, the parts might experience fractures, which could heavily impact the formed part's geometric accuracy. The main purpose of this study is to analyze the formability of an AA3003-H18 aluminum alloy material in the SPIF process; for this purpose, the material properties are extracted from the experimental simple tensile test in three directions corresponding to the material rolling direction. At first, a simple tensile test is modeled and estimated the material properties for conducting the numerical simulations. Second, the real-time experiments of the SPIF process in terms of predefined forming conditions are performed, and then the surface roughness was measured to check the surface quality of the formed parts. Then, the formed parts are scanned using a 3D ATOS scanner and compared against the desired computer-aided design (CAD) model. Eventually, the numerical results are discussed in comparison with the experimental outcome and displayed a significant correlation toward the expected results. This results comparison communicates that the introduced finite element (FE) model can be adopted for investigating the appearance of thinning location, thinning reduction, distributions of stress and strain. The overall results show that satisfying material formability in better surface finish and geometric dimensional accuracy can be accomplished when the forming conditions are designed appropriately.

Multistage Tool Path Optimisation of Single-Point Incremental Forming Process.

Single-point incremental forming (SPIF) is a flexible technology that can form a wide range of sheet metal products without the need for using punch and die sets. As a relatively cheap and die-less process, this technology is preferable for small and medium customised production. However, the SPIF technology has drawbacks, such as the geometrical inaccuracy and the thickness uniformity of the shaped part. This research aims to optimise the formed part geometric accuracy and reduce the processing time of a two-stage forming strategy of SPIF. Finite element analysis (FEA) was initially used and validated using experimental literature data. Furthermore, the design of experiments (DoE) statistical approach was used to optimise the proposed two-stage SPIF technique. The mass scaling technique was applied during the finite element analysis to minimise the computational time. The results showed that the step size during forming stage two significantly affected the geometrical accuracy of the part, whereas the forming depth during stage one was insignificant to the part quality. It was also revealed that the geometrical improvement had taken place along the base and the wall regions. However, the areas near the clamp system showed minor improvements. The optimised two-stage strategy successfully decreased both the geometrical inaccuracy and processing time. After optimisation, the average values of the geometrical deviation and forming time were reduced by 25% and 55.56%, respectively.

Conformance and nonconformance in segmentation-free X-ray computed tomography geometric inspection

Additive Manufacturing (AM) is changing the manufacturing paradigm as it makes it possible to generate complex geometries that are impossible using conventional technologies. However, conventional GPS/GD&T practices are inadequate both at specifying and verifying geometric tolerances. In both cases, they lack the required flexibility. Applying volumetric instead of surface representations helps to solve the problem of specifying tolerances and coheres with topological optimization. The verification paradigm must be modified, too, as AM allows an increase in part complexity without a corresponding increase of cost. Among measurement techniques, only X-ray computed tomography (XCT), which is volumetric, is capable of easily measure complex parts. Leaving the discussion of volumetric tolerance specifications to the future, the aim of this work is exploring a part geometric accuracy verification by direct comparison between its nominal geometry and geometric tolerance volumetric representation, and an XCT volumetric image of it. Unlike the conventional use of XCT for geometric verification, this is a segmentation-free verification. The method is based on the “mutual information” of the two, i.e. information shared by the measured and nominal representations. The output is a conformance statement that does rely on a measurement but nor on a specific measured value not rely on a measurement result. This makes defining a decision rule considering consumer's and producer's risks difficult: uncertainty does not exist in this case. Statistic and simulation techniques make it possible to estimate these risks, defining a numerical model of the distribution of the gray values in a specific portion of the XCT image. Finally, an additive manufacturing case study validates the methodology.

Ensuring the accuracy of aircraft parts by the VST method

The regularities of the violation of the geometric accuracy of parts are considered, technological schemes for experimental studies of the VST process are suggested, and the structure of the process control model is determined.

Increasing Part Geometric Accuracy in High Speed Machining using Cascade Iterative Learning Control

High speed machining provides high productivity and low machining cycle times. Post machining, there can exist differences between desired and measured part geometry due to tool deflection induced from higher feedrates. Reducing the feedrate leads to an increase in machining time. Using predicted drive responses on a virtual CNC with an integrated surface location error model, this research is the first time Iterative Learning Control (ILC) has been applied to reduce part geometry errors from tool deflection. Validation machining trials demonstrated that the ILC scheme improved machining performance whilst maintaining machining times when compared to a baseline part program.

Data-Driven Energy Efficiency and Part Geometric Accuracy Modeling and Optimization of Green Fused Filament Fabrication Processes

Abstract Nowadays, increasing awareness of environmental protection has evoked the adoption of green technologies in design and manufacturing. As a revolutionizing manufacturing technology that produces components in a layer-by-layer fashion, additive manufacturing (AM) has followed this trend. Among a variety of AM processes, fused filament fabrication (FFF) is one of the most commonly used technologies. However, AM (including FFF) is inherently energy expensive and energy inefficient compared with the conventional manufacturing. Thus, an urgent investigation is needed to reduce the energy consumption for AM production. On the other hand, part geometric accuracy is an important aspect for the quality of additively manufactured components. It is not meaningful to improve AM’s energy consumption performance with compromised part geometric accuracy. Therefore, it is necessary to jointly consider energy consumption as well as part geometric accuracy in the AM process design. This study applies the statistical regression approach to model AM energy consumption and part geometric accuracy. The nondominated sorting genetic algorithm II (NSGA-II) and the technique for order of preference by similarity to ideal solution (TOPSIS) method together are used to locate the compromised optimal solution for AM process parameter settings. The effectiveness of the proposed approach is demonstrated through a case study developed with the FFF process and a specific part design. The results of this study are significant to both AM energy consumption and part geometric accuracy in terms of qualitative and quantitative analyses. Furthermore, the study can potentially guide the future AM sustainability model development and be extended to future AM process improvement.

Investigation of the vibration state of the turbine rotor taking into account the influence of geometric accuracy of parts

The article describes the method of estimating the state of vibration of the rotor taking into account the geometrical deviations parts. The results obtained theoretical studies of the relationship of vibration characteristics and geometrical deviations of the rotor of low pressure turbine. To conduct research, a parametric rotor model has been created, as well as a finite element model in the ANSYS. To determine the center of mass of a part in the process of balancing, an algorithm developed in the program MATLAB.

Part Build Orientation Optimization and Neural Network-Based Geometry Compensation for Additive Manufacturing Process

Significant advancements in the field of additive manufacturing (AM) have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small fusible openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes lead to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel artificial neural network (ANN)-based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part's geometric accuracy.

Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

Effect of cold vapour treatment on geometric accuracy of fused deposition modelling parts

PurposeThe purpose of this paper is to investigate the influence of low-cost chemical vapour treatment process on geometric accuracy and surface roughness of different curved and freeform surfaces of fused deposition modelling (FDM) specimens build at different part building orientations.Design/methodology/approachParts with different primitive and curved surfaces are designed and modelled to build at three different part orientations along X orientation (vertical position resting on side face), Y orientation (horizontal position resting on base) and Z orientation (upright position). Later, the parts are post-processed by cold vapours of acetone. Geometric accuracy and surface roughness are measured both before and after the chemical treatment to investigate the change in geometric accuracy, surface roughness of FDM parts.FindingsThe results indicate that surface roughness is reduced immensely after cold vapour treatment with minimum variation in geometric accuracy of parts. Parts build vertically over its side face (X orientation) provides the overall better surface finish and geometric accuracy.Originality/valueThe present study provides an approach of post-built treatment for FDM parts and observes a significant improvement in surface finish of the components. The present approach of post-built treatment can be adopted to enhance the surface quality as well as to achieve desired geometric accuracy for different primitive, freeform/curved surfaces of FDM samples suitable for functional components as well as prototypes.

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

Geometrical product specification and verification in additive manufacturing

The geometric freedom associated with additive manufacturing (AM) processes create new challenges in defining, communicating, and assessing the dimensional and geometric accuracy of parts. Starting from a review of the ASME-GD&T and ISO-GPS current practices, a new approach is proposed in this paper. The new approach combines current tolerancing practices with an enriched voxel-based volumetric representation scheme to overcome the limitations of standard methods. Moreover, the new approach enables a linkage between product design optimization and product verification with respect to the AM process chain. A case study is considered to demonstrate the concept.

On Surface Finish and Dimensional Accuracy of FDM Parts after Cold Vapor Treatment

Fused deposition modeling (FDM) process is an additive manufacturing technology where objects are manufactured in layers. In the present days, FDM is commercially used to build prototypes, functional components; however, these parts majorly suffer from poor surface quality and dimensional accuracy even for basic part geometries. In the present paper, first the effect of part deposition orientation on surface finish and dimensional accuracy of FDM parts are studied. The part selected for this study is designed in such a way that different primitive geometric features at different directions are present. The parts are built at different orientations (0°, 15°, 30°, 45°, 60°, 75°, 90°) using acrylonitrile butadiene styrene P430 material, and surface finish and dimensional accuracy are measured at different surfaces. Next, the FDM parts are postprocessed by cold vapor treatment of dimethylketone (acetone) and improvement in surface finish and change in dimensional accuracy are investigated. The results show that surface finish of the components is greatly improved by this vapor treatment process with minimal variations in part geometric accuracy after the treatment.

Post-processing opportunities of professional and consumer grade 3D printing equipment: a comparative study

The paper describes to the machine user community how to improve surface quality and dimensional stability of additively produced parts. A case geometry is designed to compare the subtractive finishing process of FDM parts. Two different machines are used to produce a sample to be post-processed with a CNC milling machine. Geometrical and dimensional features are analysed with a 3D scanner to assess the reliability and repeatability of the post-processing for engineering applications. In addition, causal relationships of the hybrid approaches and final part geometric accuracy can be understood. Results show that the reliability of consumer grade machines needs to be improved substantially to implement them into engineering applications. Geometry distortions, due to thermal warping and delamination problems and due to lack of shear strength, are the obstacles to overcome in the development of hybrid subtractive processes. Nevertheless, machine users can benefit from the methodology presented in order to plan hybrid processes and improve final manufacturing quality.