Geometric Shape Deviations Research Articles

ОПТИМІЗАЦІЯ РЕЖИМІВ ЛАЗЕРНОГО РІЗАННЯ НЕРЖАВІЮЧОЇ СТАЛІ МЕТОДОМ ТАГУЧІ

Today, laser cutting is widely used in the automotive, shipbuilding and other industries where aluminium, low-alloy and stainless steel are cut. The choice of a laser system for a cutting process is heavily influenced by criteria such as achieving high cutting speeds and maximising production productivity, which can help to achieve high quality cuts and eliminate the need for rework. Increasing the efficiency of the laser cutting process, as well as its flexibility and quality, can reduce production costs. Machining stainless steel is associated with a number of difficulties, which, in turn, are caused by the material's properties. That is why laser cutting is one of the most effective methods of processing stainless steel. The quality of manufactured parts is inextricably linked to cutting modes. The paper envisages the optimisation of stainless steel laser cutting modes using the Taguchi method. Such parameters of the cut as roughness, heat affected zone, and geometric shape deviation were studied. The studies were carried out on a Smart 3015 laser cutting machine. The control variables of laser cutting according to the Taguchi method and their influence on the response variables were determined. According to the results of the studies, it was found that the smallest deviations in geometric shape, thermal impact zone, and surface roughness are achieved at a laser speed of 40 cm/min, laser power of 1000 W, frequency of 50 Hz, and gas pressure of 0.5 MPa. In addition, the percentage effect of each cutting parameter on the response variables was determined. A study using the Taguchi method showed that 80-90% of the influence is exerted by the laser power.

Bayesian analysis for geometric shapes in additive manufacturing

In additive manufacturing, geometric shape deviations are built through statistical deviation models. Nonetheless, the resource constraints limit the manufacturers to test shapes. However, in addition to the power, the simplicity of the deviation models has been demonstrated with illustrative cases for in-plane deviation modelling for regular pentagon, irregular octagon as well as straight edges in free-form shapes utilizing only data and models for single regular pentagon and cylinders. Bayesian deviation models, built for geometric shapes, provide a better fit on the data and help in achieving better predictive accuracy. The main aim of this study is to assess the effect of the error distribution, generally assumed as the normal, and evaluate its impact on the deviation model. In particular, we consider Laplace, logistic, Cauchy, and exponential distributions for the errors. It is shown that logistic distribution provides better alternative error distribution for deviation models as compared to the normal distribution.

Optimization of the 2 ½ D Processing Method of Complex Parts, through a Predictive Algorithm for Controlling the Geometric Shape Deviations Resulting from Processing

This article intends to define a new methodology that allows the processing of complex surfaces in space through processing cycles, in parallel superposed planes—the variant known as generic processing in 2 ½ D—but with predictable control over the deviation from the geometric form of the surface to be processed. The novel methodology consists of identifying the optimal distances between the working planes and the corresponding successive positions so that the deviations from the resulting geometric form fall within the prescribed limits. It is also envisaged that the method will provide facilities in terms of the possibilities for evaluation of deviations from the given form of the surface, and keeping them under control by the stage of elaboration of the numerical control programs. The new optimization is designed to determine the maximum distances between successive processing planes and their position in space, depending on the spatial shape of the surface to be processed. Thus, the aim is to obtain a small number of processing planes with a favorable effect on productivity, but under conditions that respect the tolerances of the surface or the profile, a restriction that otherwise has a negative effect on the same process.

Automated Geometric Shape Deviation Modeling for Additive Manufacturing Systems via Bayesian Neural Networks

A significant challenge in comprehensive geometric accuracy control of an additive manufacturing (AM) system is the specification of shape deviation models for different computer-aided design products manufactured on its constituent AM processes. Current deviation modeling techniques do not satisfactorily address this challenge because they can require substantial user inputs and efforts to implement. We present a new model building methodology based on a class of Bayesian neural networks (NNs) that directly addresses this challenge with much less effort. Our method enables automated deviation modeling of different shapes and AM processes and yields models with higher predictive accuracies compared to the existing modeling methods on the same samples of manufactured products. A fundamental innovation in our methodology is the design of new and connectable NN structures that facilitate the leveraging of previously specified deviation models for adaptive model building of new shapes and AM processes. The power and broad scope of our method are demonstrated with several case studies on both in-plane and out-of-plane deviations for a wide variety of shapes manufactured under different stereolithography processes. Our Bayesian methodology for automated and comprehensive deviation modeling can ultimately help to advance flexible, efficient, and high-quality manufacturing in an AM system. Note to Practitioners —Additive manufacturing (AM) systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of constituent processes. Learning how to control geometric shape accuracy in a comprehensive manner for an AM system is vital to its operation. This task is challenging due to constraints on the number of test shapes that can be manufactured and user efforts that can be devoted for learning and predicting geometric errors of different sets of shapes and AM processes. This article presents an automated machine learning methodology for comprehensive learning and prediction of geometric errors in an AM system based on a limited number of test shapes manufactured under different processes. Several case studies serve to validate the potential of our methodology to learn effective geometric accuracy control policies for general AM systems in practice.

Multimodal Data Fusion in 3-D Printing Quality Prediction

Additive manufacturing, or 3-D printing, is an emerging technology that enables the direct fabrication of complex shapes. The material solidification in 3-D printing, however, may yield geometric shape deviation in the 3-D-printed parts, which adversely affects product quality and performance in engineering applications. The advancement in sensing technology enables us to collect measurement data to facilitate automated quality inspection for 3-D printing. The objective of this study is to develop a data fusion method that can effectively fuse the different sources of sensor data and process data to evaluate the quality of 3-D printed parts. Specifically, this study fuses the information extracted from three data sources with different accuracy and measurement efficiency to evaluate the geometric shape deviation of the printed parts. Significant features about part quality are extracted from the 3-D surface measurement data using principal component analysis (PCA) and uncorrelated multilinear PCA. Selected features are then fed into classification and regression to infer the quality of the part. The proposed prediction model starts with a preliminary classifier for the initial inspection using only low-resolution 3-D surface measurement, followed by a more refined classifier trained by high-resolution 3-D surface measurement to update the quality class labels. Such a two-level classification model is able to guarantee time efficiency while maintaining high accuracy. Results show that the proposed fusion method is promising for efficient and automated quality inspection.

Bayesian Model Building From Small Samples of Disparate Data for Capturing In-Plane Deviation in Additive Manufacturing

ABSTRACTQuality control of geometric shape deviation in additive manufacturing relies on statistical deviation models. However, resource constraints limit the manufacture of test shapes, and consequently impede the specification of deviation models for new shape varieties. We present an adaptive Bayesian methodology that effectively combines in-plane deviation data and models for a small sample of previously manufactured, disparate shapes to aid in the model specification of in-plane deviation for a broad class of new shapes. The power and simplicity of this general methodology is demonstrated with illustrative case studies on in-plane deviation modeling for polygons and straight edges in free-form shapes using only data and models for cylinders and a single regular pentagon. Our Bayesian approach facilitates deviation modeling in general, and thereby can help advance additive manufacturing as a high-quality technology. Supplementary materials for this article are available online.

Detection of Workpiece Shape Deviations for Tool Path Adaptation in Robotic Deburring Systems

Robotic systems have the potential to automate deburring processes along edges of arbitrarily shaped workpieces. The desired robot movement can be realized by proper trajectory planning using the computer-aided design (CAD) model of the manufactured workpiece. A fundamental problem is that geometric shape deviations between the nominal CAD model and the manufactured parts might be beyond acceptable limits for correct execution of the deburring process. This paper proposes a novel, easy to implement and practical approach to detect shape deviations of workpieces for automatic adaptation of robotic deburring processes. The approach only uses dimensional tolerance specifications of the manufactured part provided by the product design to derive possible variations of the workpiece geometry model. A matching process is performed between point clouds for each of the considered variants and a measured point cloud from the manufactured workpiece using an Iterative Closest Point (ICP) approach. Resulting point distances are used for evaluation of shape similarity between the compared point clouds. Finally, the most similar geometry model is identified for subsequent trajectory planning and workpiece localization. Experimental validation is performed by an industrial robot equipped with a stereo camera for shape sensing and a milling tool for subsequent execution of a deburring process. The results demonstrate the effectiveness and practicality of the proposed approach in industrial applications and an increased deburring quality.

Finite Element Simulation for Quality Dependent Lifetime Analysis of Micro Gears

Abstract Nowadays, micro motors are used in combination with micro transmissions in manifold industrial applications such as dental drills or the equipment for minimally invasive surgery in the medical industry, hexapod micro positioning systems for wafer processing in the field of industrial automation or adjustable automotive components such as fixings of LCD monitors. Micro transmissions consist of micro gears, which are critical to their functionality. Micro gears are typically defined as gears with a module which is lower than 200 μm. To ensure proper operation of the micro gears for their expected purpose, a reliable prediction of their lifetime is crucial. Lifetime evaluation is particularly important for micro gears, as the influence of their geometric shape deviations on their load rating is significantly higher in comparison to gears with larger modules. This is a consequence of the larger shape deviations of micro gears in relation to their part size due to their manufacturing processes. The lifetime of micro gears can be evaluated by an experimental approach. Within this a pair of micro gears is systematically worn under realistic, clearly defined conditions, until a defect of one of the micro gears can be detected. This can be conducted by means of a highly precise experimental setup. In this article, a methodology to calculate the characteristic loads at the tooth flanks of the pair of micro gears during the experiments based on finite element analysis is introduced. For this purpose, CAD models of the real gear geometry of the specimen are deducted by means of high precision 3D measurements and spline interpolation. On the basis of these data, the lifetime of the micro gears dependent on their shape deviations can be predicted by means of a model based on reliability statistics.

A model-based approach for monitoring of shape deviations in peripheral milling

This paper presents a model-based approach for monitoring of shape deviations for milling operations. In order to detect occurring shape deviations of the machined workpiece during the milling process, different kinds of process models are presented and discussed for their application on manufacturing quality monitoring. Thereby, a model-based system was presented for the monitoring of shape deviations based on measured cutting forces. For the transformation of cutting forces into shape deviations, a tool deflection model and material removal model were designed and applied to a monitoring system. The presented model-based monitoring approach delivers accurate quality information, like geometric shape deviations, which can be monitored against geometric tolerances, providing a quality monitoring of manufacturing processes. The reconstruction of shape deviations from measured cutting forces is verified experimentally by comparing measured and reconstructed shape contours.

Plastic Buckling of Imperfect Hemispherical Shells Subjected to External Pressure

Plastic buckling/collapse pressures for externally pressurized imperfect hemispherical shells were calculated for several values of the yield point ( syp), the radius–thickness ratio ( R/t) and the amplitude of the initial imperfection at the pole (δ0). The well-known elastic–plastic shell buckling program BOSOR 5 was used in the calculations and two axisymmetric initial imperfection shapes were studied, viz. a localized increased-radius type and a Legendre polynomial. The numerical collapse pressures ( pc) for both types of imperfection were normalized and plotted versus λ ( a parameter proportional to[Formula: see text]. Approximate algebraic equations were then derived which give pc/ pyp as a function of λ and δ0/t. The values of pc given by these equations agree well with the computer results. Using the maximum values of the geometric shape deviations allowed by some national Codes, the corresponding theoretical buckling strengths were calculated. These were then compared with an approximate lower bound of test results obtained on externally pressurized spherical shells. The agreement between the two curves was not very good for BS 5500 but was fair for the DnV rules. The agreement with BS 5500 can be improved by increasing simp, the arc length over which the initial imperfections are measured. The foregoing lower bound of test results on externally pressurized spherical shells can also be obtained, approximately, using increased-radius and Legendre polynomial imperfections in which the ratio Rimp/ R is not restricted. The magnitude of the initial imperfection required for approximate agreement between the experimental and theoretical results was δ0/t ∼ 0.5. This seems a reasonable value. However, more study of this aspect of the problem is required in both the elastic and plastic buckling regions. The limitation of Rimp/ R ≥ 1.3 imposed by some Codes should also be reviewed, particularly in the plastic regime.