Geometric Tolerances Research Articles

Interaction analysis of geometric tolerance of multi-axis machine tools based on kinematic Jacobian-Torsor model

For the purpose that the multi-axis machine tools can machine workpieces flexibly and accurately, it is important to ensure that each axis of the machine tool can be linked with each other, and the geometric tolerances are within their required range. However, each tolerance is not only related to its own kinematic axis, but also affected by the comprehensive influence of other kinematic axes, which brings great difficulties to the modeling of geometric tolerances. This paper focus on developing a modeling methodology to describe the relationship between the range of machining geometric tolerance and the motional domain of machine tool axe based on an improved Jacobian-Torsor theory. General formulas for the expression of the geometric tolerances are derived. Experiments are carried out on five four-axis machine tools, type CNC-SH50, to verify the effectiveness of the proposed kinematic Jacobian-Torsor model in dealing with the interaction analysis of geometric tolerances.

Complex Assembly Analysis for Geometric and Dimensional Tolerance to Obtain Selective Assembly from Partitioned Bins Using a Multi-objective Approach to Control Clearance Variation of IC Engine

Complex Assembly Analysis for Geometric and Dimensional Tolerance to Obtain Selective Assembly from Partitioned Bins Using a Multi-objective Approach to Control Clearance Variation of IC Engine

Study on surface roughness and geometrical tolerances of Inconel 718 superalloy polished by magnetic abrasive finishing process

Magnetic Abrasive Finishing (MAF) is an advanced finishing process that has the prominent characteristics such as the ability to machine complex surfaces with complex geometry. Geometric tolerances are among the important parameters in the qualitative evaluation of machining parts. Due to the large number of abrasive edges participating in the finishing process, it is very difficult to achieve accurate geometric tolerances in the MAF process. This problem is especially considerable in workpieces with low machinability such as nickel based superalloys. In this study, the effect of process input parameters such as feed rate, rotational speed, and working gap on surface roughness and geometric parameters such as circularity and cylindricity of the external surfaces of an Inconel 718 shaft was investigated. The results indicate that surface quality and geometric tolerances improve with increasing rotational speed. However, the increase of rotational speed by a certain amount leads to turbulence in the movement of abrasive particles and no significant changes are seen. As well as, by increasing feed rate, surface roughness and geometric tolerance increase. Furthermore, with the working gap increasing due to the reduction of magnetic abrasive brush width, circularity, and cylindricity tolerances increase, but smoother surface roughness is achieved.

Analysis of the Influence of Tolerance Design on Unbalanced Mass of Rotating Pressure Vessel

The unbalanced mass will cause forced vibration of the pressure vessel when it rotates, and the magnitude of the unbalanced mass is positively correlated with the amplitude of the pressure vessel. Therefore, the study of unbalanced mass is of great significance to the safety of rotating pressure vessels. The influence of form and position tolerances and assembly tolerances on the unbalanced quality of pressure vessel parts is analyzed, and the normal distribution model is used to simulate the tolerance distribution of parts in mass production, and then the total unbalanced mass of the pressure vessel is calculated by the vector synthesis method. Through sensitivity analysis, the parts and key positions that have a greater impact on the total unbalanced mass of the pressure vessel are found, and the geometric tolerances of the parts with high process difficulty are optimized, which provides a theoretical basis for the tolerance optimization design work of the rotating pressure vessel.

Drought tolerance screening of rice genotypes in mid-hills of Nepal using various drought indices

ABSTRACT Drought is a major abiotic factor causing rice yield loss in rainfed and drought-prone areas, so screening of the cultivars for drought tolerance is crucial. Our study screens 10 commercial rice cultivars grown in the mid-hills of Nepal and four pipeline genotypes. Our objective is to identify the superior drought-tolerant cultivar and suitable indices for screening. The 14 rice genotypes were evaluated under both drought stress and non-stress conditions in randomized complete block design with three replications. The yield reduction for the cultivars ranged from 12-54% during water-stressed conditions. The drought indices mean productivity, geometric mean productivity and stress tolerance index showed a positive and high correlation with grain yield. Based on drought indices, genotype NR 119 showed the highest mean productivity, geometric mean productivity, stress tolerance index and lowest yield loss. Further, principal component analysis bolsters our results by clustering similar drought indices and drought-tolerant cultivars. The NR 119 is followed by Chaite 5 and Chaite 4 as a drought-tolerant genotype, therefore, we recommend it for drought-prone areas of the mid-hill region of Nepal. We identified mean productivity, stress tolerance index and geometric mean productivity as important drought indices, so we recommend using this for drought screening.

Use of X-Ray Computed Tomography for Dimensional Metrology and Geometric Dimensioning and Tolerancing

Many advances in X-ray computed tomography (CT) scanning have allowed for its use in dimensional metrology and geometric dimensioning and tolerancing (GD&T) applications. This is in addition to the use of CT for discontinuity inspection. For internal passages and features where touch probe/coordinate measurement and optical-based methods are not applicable, CT is often the only possible alternative for dimensional measurements. This capability has become even more important in recent years with the growing popularity of complex additively manufactured parts. In some situations, CT may serve as a substitute for traditional coordinate measurement completely. As such, CT offers the ability to optimize product design and manufacturing processes. In this article, the use of CT for dimensional metrology and GD&T is described. We will discuss factors affecting the successful use of CT for these applications, provide a workflow for analyzing CT data for dimensional metrology and GD&T, and finally show a simple example for dimensional metrology and GD&T analysis of CT volume data.

Assessment of the surface integrity of ground cemented tungsten carbide cutting inserts and its influence on tool wear in turning of ferritic nodular cast iron

In order to improve surface finish, geometric and dimensional tolerances of cemented tungsten carbide cutting tools produced by powder metallurgy, grinding is commonly applied. Due to the high thermal and mechanical loads generated during this process, surface and metallurgical characteristics are directly affected and, as a result, tool performance. Within this context, the influence of grinding wheel characteristics on surface integrity parameters, like surface and edge roughness, hardness and residual stress, was investigated and cutting inserts ground under extreme conditions were applied in rough orthogonal turning of a ferritic nodular cast iron to evaluate their performance regarding tool life. The obtained results showed that higher compressive residual stress values, induced by a vitrified bonded diamond grinding wheel with grain size of 46 µm, contributed to increase tool life, as a protective layer of adhered material was formed on the cutting edge. Flank wear by abrasion was thus avoided and crack formation was the determining factor for the end of the tests. • Finishing process of cutting inserts significantly affects their performance. • Compressive residual stress contributes to increase tool life in rough turning. • Insert surface and edge roughness do not influence tool life in turning cast iron.

Coaxiality error analysis and optimization of cylindrical parts of CNC turning process

High precision rotary shafts with precise geometrical tolerances are generally mounted with a micron level clearance between the gears and casing during operation in industrial applications. Dynamics cyclic loads are inevitable in most of these applications which has an adverse effect on the fatigue life of the critical parts. Ensuring close dimensional tolerances and coaxiality during machining is highly desirable, as it affects the rotary characteristics in many applications. Thus, control of coaxiality error plays a vital role in rotating shafts and high precision machine tools. However, use of high precision machining would drastically increase the cost of manufacturing. Thus, a cost-effective machining process that could potentially reduce the coaxiality error is of high industrial importance. The present research efforts made an attempt to achieve minimum coaxiality error on cylindrical machined parts by optimizing parameters (cutting speed, feed rate, depth of cut and cutting tool nose radius). Experiments are planned, viz. central composite design matrix and statistical analysis determine the influence of machine parameters on coaxiality error of high-strength Al 7075 alloy by applying response surface methodology. Feed rate and depth of cut factors showed significant effect on coaxiality error. All machining parameters showed a non-linear effect on coaxiality error, which defines the strong interaction factor effects. The empirical equations derived were used to minimize coaxiality error by determining a set of machining parameters, viz. applying Big-Bang and Big Crunch and Rao (Rao-1, Rao-2 and Rao-3) algorithms. Rao algorithms outperform the Big-Bang and Big Crunch algorithm both in computation effort and solution accuracy. The results of Rao algorithms are experimentally verified, which resulted in reduced coaxiality error equal to 1.013 µm and resulted in 72.6% improvement compared to CCD experiments.

Evaluating the Productivity of some Barely Genotypes under Deficient Water Application in Clayey Soils

This study is intended to identify barely genotypes efficient for water use. For this, a field experiment was conducted at Sakha Research Station. The experimental design was randomize complete block with three replicates during 2018/2019 and 2019/2020 growing seasons. Twenty barely genotypes were grown under two water application treatments ((full irrigation (FI) and water stress (WS)). The values of applied irrigation water were 3430 and 1995 m3/ha under WS and FI, respectively, being lower by 42%, compared to FI amount, average over the two seasons. The interaction between barley genotypes and irrigation water revealed that plant height of line-7, spike length and number of grains/spike of line-6, number of spikes/m2 of Giza133, and grain and biological yields of line-5 were the least affected by WS, compared with their values under FI. The highest values of WUE under WS were found for line-6 and line-11, which also attained the highest WP Line-8 and line-13, expressed the highest value of mean productivity, geometric mean productivity and stress tolerance indices. Furthermore, line-7 had the highest value of stress susceptibility index. Thus, based on WUE, WP and drought tolerance indices, it could be concluded that line-6, line-7, line-8, line-11 and line-13 have the ability to withstand water stress and could be selected for breeding programs for water use efficiency.

Design and Modeling of MEMS Microgrippers for Laser-Based Additive Manufacturing

The geometrical constraints and dimensional tolerances lead to specific design issues of MEMS manipulators for biological applications. The target properties become even more important in the case of in vitro manipulation of cells. Several design solutions have been proposed in the literature, however, some issues related to the thermal heating of microgripper tips and to the electric voltage effects still remain unsolved. This paper reports the design for additive manufacturing (DFAM) of micro-electro mechanical systems (MEMS) microgrippers. The design limitations imposed by the micro-stereolithography fabrication process are considered. The design solution proposed in this study is based on compliant structures and external actuation; this layout provides the potential elimination of the main issues related to cells micro-manipulators represented by the excessive thermal heating and the voltage exposure of samples. The simulation through finite elements method (FEM) models of the structure in terms of force–displacement relation and stress distribution supports the design evolution proposed.

Technical layout and fabrication of a compact all-glass four-channel beam splitter based on a Kösters design

We report on the design and fabrication of a novel all-glass four-channel beam splitter based on a Kösters prism for use in space. The Kösters prism, which consists of three pairs of individual prisms, is used to separate an incoming telescope beam into four spectral channels (λ = 800–1700 nm) with the goal to obtain a multi-band photometry of cosmic sources in the optical/near-infrared bands. We performed optical design studies to evaluate the influence of geometrical tolerances of the six individual prisms on the image quality. A stray light analysis revealed the impact of the composition on the overall optical performance. Mechanical design studies benchmarked possible mounting strategies. We considered optical adhesives, soldering and clamping. The influence of the mechanical loads during a rocket launch as well as thermal loads at 140 K (the operation temperature of the optical element) were studied. We optimized the coating properties of the prisms by considering the results from the optical design study as well as the technological requirements for the direct bonding of the prisms. Bonding strategies to realize the prism pairs were developed and successfully tested. A demonstrator Kösters prism was manufactured and is ready to validate its optical performance.

Dimension Measurement and Quality Control during the Finishing Process of Large-Size and High-Precision Components

The accurate measurement and control of the geometric dimensions and shape errors of large-size and high-precision key components are key factor to ensure the machining quality of the equipment package. Aiming to address the urgent problems of poor measurement conditions, complicated error propagation, and difficulty in obtaining accurate measurement results, this research studied the measurement method and control of dimensional accuracy and geometric tolerance in the process of hole machining at room temperature, taking Metso MP 1250 cone level 6 precision point pair workpiece as the object. Through linear analysis and demonstration of quality problems in the existing processing technology, the influence of the expansion coefficient of the workpiece material, measurement error and other factors is analyzed. After correcting the temperature error model, many measurements were carried out in machining experiments, and the normal ability was analyzed by Minitab software. The experimental results showed that the workpiece temperature rises by 1°C, the workpiece will be deformed by 8.503 um. In the measurement process of high-precision and large-size components, the correction accuracy of the temperature error is affected by the combined influence of the model error, the material expansion coefficient error, and the temperature measurement error. The results of this study are of guiding significance for the measurement of the dimension and geometric tolerance precision of large-size and high-precision castings.

A reaction–diffusion model for grayscale digital light processing 3D printing

Digital light processing (DLP) 3D printing is an additive manufacturing process that utilizes light patterns to photopolymerize a liquid resin into a solid. Due to the accuracy of modern digital micromirror devices (DMD) and recent advances in resin chemistry, it is now possible to create functionally graded structures using different light intensity values, also known as grayscale DLP (g-DLP). Different intensities of light lead to differences in the polymer crosslinking density after curing, which ultimately produces a part with gradients of material properties. However, g-DLP is a complicated process. First, the DLP printing is a highly coupled chemical and physical process that involves light propagation, chemical reactions, species diffusion, heat transfer, volume shrinkage, and changes in mechanical behaviors of the curing resin. Second, in g-DLP, light gradients create strong in plane gradients of chemical species concentrations in the curing liquid resin due to the strong dependence of light intensity on the rate of monomer crosslinking. Furthermore, light gradients through the depth create concentration gradients due to the degree of cure dependent light absorption and the use of photoabsorbers. These complex physical features of the printing process must be understood in order to properly control printing parameters such as light exposure time, printing speed, and grayscale variations to achieve accurate mechanical properties. In this paper, a photopolymerization reaction–diffusion model is developed and used in conjunction with experiments to investigate the coupled effects of light propagation, chemical reaction rates, and species diffusion during g-DLP 3D printing. The model is implemented numerically utilizing the finite difference method and simulation results are compared to experimental findings of simple printed structures. The agreement between experimental and model predictions of simple quantities of interest, such as geometric feature sizes, shows that the model can capture the overcure due to free-radical and other species diffusion during printing when grayscale patterns are employed. This model lays the groundwork for future extensions that can incorporate more complex coupled physics such as heat transfer, volume shrinkage, and material property evolution, which are critically important in utilizing g-DLP 3D printing for the fabrication of high-performance parts which excellent geometric and material property tolerances.

Geometric Error Propagation Model-Based Accuracy Synthesis and Its Application to a 1T2R Parallel Manipulator

Abstract To meet the increasing requirements for precision hybrid machine tools, this paper presents a geometric error propagation model-based accuracy synthesis method for parallel manipulators (PMs) with one translational and two rotational (1T2R) motion abilities. A unified geometric error propagation model of a family of 1T2R PMs is established with the first-order kinematic perturbation method. A set of geometric error propagation intensity indexes is formulated to describe the geometric error propagation characteristics in a quantitative manner. The equivalent effects of specified terminal accuracy are derived to directly determine the allowable values of all geometric source errors. Based on these, a computation algorithm is summarized for designating a tolerance allocation scheme to meet the specified terminal accuracy of a 1T2R PM. To demonstrate the accuracy synthesis method, a novel 1T2R PM with a 2UPR-1RPS (“R,” revolute joint; “U,” universal joint; “S,” spherical joint; “P,” prismatic actuated joint) topology is taken as an example to allocate geometric tolerances under the specified terminal accuracy. Following the tolerance allocation scheme, a laboratory prototype of the exemplary PM is fabricated and further experimentally measured. The measured kinematic results indicate that the prototype possesses an acceptable position error smaller than 0.15 mm, verifying the feasibility of the proposed accuracy synthesis method. Hence, the proposed method can be applied as a forward tolerance design tool to reduce the design iterations and development risks of low-mobility parallel robots.

Study on Die Shoulder Patterning Method (DSPM) to Minimise Springback of U-Bending

U-bending is increasingly used in the sheet metal industry such as car door pillars. However, springback phenomenon always tends to occur after removing sheet metal from the fixtures and resulting in changing product effectiveness, wasting material as well as increasing manufacturing costs. Thus, minimizing springback in the bending of sheet metal is vital to maintain close geometric tolerances in the deformed part. Many researchers have done investigated and predicted the springback occurrence by experiments and simulations. Neverthessly, there is no actual study on the die shoulder patterning method (DSPM) to reduce springback of hat-shaped parts. In this paper, the hat-shaped part is deformed using the new developed forming method and have been experimented with as well as validated using three-way Anova and graphical analysis. As expected, DSPM dominates the springback sensitivity, with higher contact area reducing the springback of hat-shaped parts. For AISI 1030 of pattern 2, springback is dramatically reduced as the sliding stress between the die shoulder and surface of the blank is optimized.

An Operational Image-Based Digital Twin for Large-Scale Structures

This study presents a novel methodology to create an operational Digital Twin for large-scale structures based on drone inspection images. The Digital Twin is primarily used as a virtualized representation of the structure, which will be updated according to physical changes during the life cycle of the structure. The methodology is demonstrated on a wind turbine transition piece. A three-dimensional geometry reconstruction of a transition piece as manufactured is created using a large number (>500) of RGB images collected from a drone and/or several LiDAR scans. Comparing the reconstruction to the original design will locate and quantify geometric deviations and production tolerances. An artificial intelligence algorithm is used to detect and classify paint defects/damages from images. The detected and classified paint defects/damages are subsequently digitalized and mapped to the three-dimensional geometric reconstruction of the structure. These developed functionalities allow the Digital Twin of the structure to be updated with manufacturing-induced geometric deviations and paint defects/damages using inspection images at regular time intervals. The key enabling technologies to realize the Digital Twin are presented in this study. The proposed methodology can be used in different industrial sectors, such as the wind energy, oil, and gas industries, aerospace, the marine and transport sector, and other large infrastructures.

Branch pulmonary artery stenosis after arterial switch operation: The effect of preoperative anatomic factors on reintervention

BackgroundWe hypothesized that preoperative patient characteristics and branch pulmonary artery (PA) size might influence the rate of postoperative branch PA reintervention in patients with transposition of the great arteries who undergo the arterial switch operation (ASO). MethodsThe retrospective single-center study included 262 consecutive (2008-2017) newborns who underwent the ASO. Demographic characteristics, echocardiography, and clinical outcomes were reviewed. Competing risk analysis modeled incidence of branch PA reintervention and cause-specific hazard regression for predictors analyses. ResultsMedian age and weight were 7 (range, 5-11) days and 3.4 (range, 3.1-3.8) kg, respectively. Various types of early branch PA reinterventions (concomitant revision or reintervention during the intensive care unit stay) were required in 28 (10.7%) patients. These patients had prolonged ventilation (P < .001), intensive care unit duration (P < .001), worse right ventricular function (P = .043), and high in-hospital mortality (P = .010). Branch PA dimensions significantly decreased immediately after ASO compared with baseline measurements. The median follow-up duration was 20.8 (range, 0.9-44.7) months. Branch PA reintervention was common among survivors without early reinterventions (9.4%), and even more frequent among those with early reinterventions (25%). Subsequent reintervention (all catheter-based) was necessary for more than one-third of patients after initial branch PA reintervention. The multivariable analysis showed preoperative dimension of the left PA (hazard ratio, 0.527 [95% CI, 0.337-0.823]; P = .005), and right PA (hazard ratio, 0.503 [95% CI, 0.318-0.796]; P = .003) were independently associated with late branch PA reinterventions. ConclusionsBranch PA reintervention was common and often required surgical or catheter-based reinterventions after ASO. PA branch diameters became significantly smaller after ASO. Smaller preoperative branch PA predicted late branch PA reintervention, indicating a smaller margin of geometrical tolerance to this effect.

Analysis of the incremental sheet forming process and process parameters on aluminum sheets based on the vertical step sizes

The incremental sheet forming (ISF) is an effective forming method developed especially for rapid prototyping and small-batch production of sheet metals. The selection of parameters (in particular, vertical step size and toolpath geometry) in ISF is crucial. Besides, each parameter has different effects on the desired geometry, sheet thickness, forming time, and productivity. In this study, the ISF process was analyzed by investigating the effects of ISF parameters. For this purpose, AA5754 sheets were formed by different ISF parameters. The effects of toolpath strategies (based on the vertical step size) were examined with three spindle speeds and three feed rates. To achieve that objective, constant, increasing, and decreasing vertical step sizes were used for the first time in the same study. After the forming, the forming time, geometric tolerance, and thickness values were analyzed. Besides, the efficiency of each parameter on the ISF process was investigated with the Pareto analysis. It has been revealed that each strategy has some advantages and disadvantages in ISF. The shortest forming time (1.55 min), the best geometric tolerance values (−0.05 mm, +0.2 mm), and the best thickness values (0.58 mm, 0.51 mm) were achieved following toolpath strategy 1, strategy 2, and strategy 3, respectively. The Pareto analysis showed that spindle speed and feed rate have a significant effect on toolpath strategies. The results obtained from this study will shed light on the effective advancement of small-batch production of sheets. Furthermore, the study contributes to the determination of parameters according to which of the sheet metal properties are desired.

Impacts of Multi-Dimensional Geometrical Uncertainties on Field Characteristics of Traveling-Wave Tube in Data-Driven Perspective

Recent prominent analyses on impacts of fabrication errors tend to start from Pierce parameters by assuming a normal distribution on them. We manage to analyze the complicated effects of multi-dimensional uncertainties directly from geometrical tolerances and present a general pattern for multi-dimensional geometrical uncertainty analysis on field characteristics. Aided by artificial neural networks (ANNs) which would get hold of the cost surface quickly and serve as rapid interfaces with a calculation speed several orders faster than full-wave code, Monte Caro analysis is accessible and effective to simulate the actual probabilistic distributions through a large-volume sampling on neural networks. Under such data-driven perspective which circumvents sophisticated theoretical analysis, the variations of geometrical parameters are analyzed as an entirety from the viewpoint of distribution. The dynamics of features of distributions of three field characteristics as well as geometrical parameters are therefore possible to be delved and analyzed. Such an analysis pattern is general since none of the steps presented cannot be readily transplanted to other topologies.

Deep Learning for improving the efficiency of dimensional measurement workflows with high-resolution X-ray computed tomography

High-resolution X-ray computed tomography (CT) instruments, also known as three-dimensional (3D) X-ray microscopes, can be adapted for dimensional metrology applications such as geometric dimensioning and tolerancing of metallic components. However, CT scanning times can be prohibitively high for industrial measurement inspection tasks owing to the poor contrast from X-ray attenuation in Ferrous metals, especially if the measurement of spatial resolutions under 5 µm are required. This paper describes a software-defined approach to dramatically reducing total exposure time (or scanning time) while maintaining resolution loss within 2 micrometers as compared to the baseline scans acquired over 6 hours. Here, we combine two deep learning (DL) codes in our surface extraction workflow to compensate for lower signal-to-noise ratio in short exposure data (acquired with lower number of projections): (1) a surface determination (post-reconstruction) , and (2) a denoising algorithm (pre-reconstruction). Training data was acquired from a scan of an 8-hole automotive fuel injector (sample 1) with a 165 µm nominal diameter per hole. For testing the accuracy of the workflow, a separate scan of a 6-hole side-mount injector (sample 2) was acquired. For both samples, the acquired X-ray projections (or radiographs) were binned down to 10X such as to simulate faster scans. For training and testing workflows, the full exposure scans (baseline) were used as target and the shorter exposure scans as inputs to the deep learning models. To determine loss of surface accuracy from the baseline case, a metric is formulated (in micrometers) and the trends are reported for when the total measurement time was reduced by up to 10X (up to 0.6 hours, using only 360 projections). We report that scan times can be reduced by over 10X while retaining the limiting the resolution loss to under 1 micrometer.