Multistage Machining Processes Research Articles

Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes

Allowance distribution and parameters optimization for high-performance machining of low rigidity parts in multistage machining processes

Predicting error for machining thin-walled blades considering initial error

A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the Gaussian process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Geometric Deviation Representation and Variation Propagation Modeling in Multistage Machining Processes

Geometric Deviation Representation and Variation Propagation Modeling in Multistage Machining Processes

Modeling of the Variation Propagation for Complex-Shaped Workpieces in Multi-Stage Machining Processes

Variation prediction and quality control for complex-shaped workpieces in automotive and aerospace fields with multi-stage machining processes have drawn significant attention because of the widespread application and increasing diversity of these kinds of workpieces. To finish the final workpieces with complex shapes, multiple setups and operations are often applied in machining processes. However, sources of geometric error, such as fixture error, datum error, machine tool path error, and the dimensional quality of the product, interact complicatedly at different stages. These complex interactions pose significant challenges to final product error prediction and reduction. Manufacturing error prediction based on stream of variation is an effective way to control the machining quality. However, there are few integrated models that can describe the interactions among types of geometric error sources from different stages for different kinds of complex workpieces. This paper proposes a modified error prediction model to systematically capture the interactions of different error sources among different operations for complex-shaped workpieces in multi-stage machining processes. Using differential motion vectors, the connection of all key variations from machine, fixture, and workpiece is established. This modified model can not only handle general fixture layouts for complex workpieces, but also introduce machining-induced variations. Based on this model, the main error sources identification method and error compensation method are proposed. In order to evaluate the effectiveness of the proposed method, engine blocks are used to be machined as an example. Compared with a machining process without a compensating strategy, the average machining error of the key feature is reduced by 80.5% after compensating for the main error sources.

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Nowadays, advanced manufacturing models, such as the stream-of-variation (SoV) model, have been successfully applied to derive the complex relationships between fixturing, manufacturing, and datum errors throughout a multistage machining process. However, the current development of the SoV model is still based on 3-2-1 fixturing schemes, and although some improvements have been done, e.g., N-2-1 fixtures, the effect of general workholding systems, such as bench vices or three-jaw chucks, has not yet been included into the model. This article presents the extension of the SoV model to include fixture and datum errors considering both bench vices and three-jaw chucks as fixturing devices in multistage machining processes. The model includes different workholding configurations, and it is shown how to include the workholding accuracy to estimate part quality. The extended SoV model is validated in a three-stage machining process by both machining experimentation and CAD simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Part quality estimation in multistage machining systems is a challenging issue. The stream-of-variation (SoV) model is a straightforward model that can be used for this purpose. However, the current model is limited to fixture based on punctual locators, and common shop-floor devices are not considered yet. To overcome this limitation, this article extends the current SoV model to include vices and three-jaw chucks as workholding devices. The proposed methodology lets practitioners estimate the manufacturing capability of a process considering the technical specifications of these devices (e.g., parallelism and perpendicularity of vice surfaces and total indicator runout of chucks), or it can be used for diagnosing workholding issues. The model assumes that the workpiece acts as a rigid part and errors as deformations during clamping are assumed to be negligible in comparison with fixture- and datum-induced errors.

A plastic strain energy method exploration between machined surface integrity evolution and torsion fatigue behaviour of low alloy steel

To explore the evolution mechanism of multistage machining processes and torsional fatigue behaviour based on strain energy for the first time and provide process optimization of axis parts of low alloy steel for service performance, four multistage machining processes were applied to the 45CrNiMoVA steel, including the Rough Turning process (RT), RT+ the Finish Turning process (FRT), FRT+ the Grinding process (GFRT) and RT+ the Finish Turning process on dry cutting condition (FRT0). The result showed that the FRT process’s average low-cycle torsional fatigue life increased by 50% when it evolved from the RT process. The lower surface roughness of Ra 1.3 μm caused the total strain energy to increase by 163.8 Pa mm/mm instead of the unchanged strain energy density, and the crack feature evolved from some specific bulges to flat shear plane characteristics. When the GFRT process evolved from the FRT process, its average fatigue life increased by 1.45 times, compared with the RT process. Plastic strain amplitude decreased by 21%, and the strain energy density decreased by 4% due to more considerable compressive residual stress (−249 MPa). Plastic deformation layer depth had a consistent tendency with surface roughness. In this paper, surface integrity evolutions on cyclic characteristics and fatigue behaviour have also been explained. A fatigue life prediction model based on the energy method for machined surface integrity is proposed.

Variation propagation modeling in multistage machining processes considering form errors and N-2-1 fixture layouts

Variation propagation modeling of multistage machining processes enables variation reduction by making an accurate prediction on the quality of a part. Part quality prediction through variation propagation models, such as stream of variation and Jacobian-Torsor models, often focus on a 3-2-1 fixture layout and do not consider form errors. This paper derives a mathematical model based on dual quaternion for part quality prediction given parts with form errors and fixtures with N-2-1 (N>3) layout. The method uses techniques of Skin Model Shapes and dual quaternions for a virtual assembling of a part on a fixture, as well as conducting machining and measurement. To validate the method, a part with form errors produced in a two-stationed machining process with a 12-2-1 fixture layout was considered. The prediction made following the proposed method was within 0.4% of the prediction made using a CAD/CAM simulation when form errors were not considered. These results validate the method when form errors are neglected and partially validated when considered.

Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium

In multistage machining processes (MMPs), variations from key control characteristics (KCCs) continue to propagate and eventually accumulate to deviations in key product characteristics (KPCs). Therefore, the variation control of KCCs is significant to ensure the final product quality. In this paper, a variation management framework for KCCs in MMPs is established to address this issue. The new concept of variation management consists of process-oriented tolerancing and maintenance planning, and the optimal variation management strategy for each KCC is assigned based on its impact to the manufacturing system. The proposed framework deduces corresponding KCC variation distributions for previously unresearched locating schemes, thereby expanding the application scenarios of this method. For the quality specification constraints, geometric tolerances are integrated for the first time beside traditional dimensional tolerances, which expand the error scale of quality control. The modified Chebyshev goal programming (MCGP) approach is adopted to find the equilibrium point between quality and cost effectively. The superiority of the proposed method is verified by a case study of an automotive engine cylinder block MMP. The results show a remarkable improvement on the manufacturing system performance in terms of quality and cost.

A Novel Error Compensation Method for Multistage Machining Processes Based on Differential Motion Vector Sets of Multiple Contour Points

Abstract Modeling the variation propagation based on the stream of variation (SoV) methodology for multistage machining processes (MMPs) has been investigated intensively in the past two decades; however, little research is conducted on the variation reduction and the existing work fails to be applied to irregular features caused by the machining-induced variation varying with the positions of the contour points on the machined surface. This paper proposes a novel error compensation method for MMPs by modifying the tool path to reduce variation for general features. The method based on differential motion vector (DMV) sets of multiple contour points is presented to represent the deviation of the irregular feature. Then, the conventional stream of variation (SoV) model is further extended to more accurately describe variation propagation for irregular features considering the actual datum-induced variations and the varying machining-induced variations, especially the deformation errors for the low stiffness workpiece. Based on the extended SoV model and error equivalence mechanism, the datum error and fixture error are transformed to the equivalent tool path error. Then, the original tool path is modified through shifting the machine zero points of machine tools with no need for changing the original G code and workpiece setup. A real cutting experiment validates the effectiveness of the proposed error compensation method for MMPs with an average precision improvement of over 60%. The application of the extended SoV model significantly contributes to compensating more complex error sources for MMPs, such as the clamp force, the internal residual stress, etc.

Part Quality Prediction in Multistage Machining Processes with Fixtures Based on Locating Surfaces Using Dual Quaternions

The mathematical modelling of variation propagation in multistage machining processes helps to perform a quick analysis and diagnosis of the processes. The models for part quality prediction, such as Stream of Variation, include homogeneous transformations of the vectorial representations of parts and fixtures. However, these prediction models are complex when considering fixtures with locating surfaces and the associated matrix size is large. Towards mitigating the mathematical complexity, dual quaternions are proposed in representing and transforming a virtual part and fixture. To achieve this, the primary feature datum is assembled to the primary locating surface, followed by sliding the part to secondary and tertiary locating surfaces by reducing the distance between the vertices of the part and the locating surface. The prediction following the proposed approach gave a result within 0.36 % of the prediction made using CAD/CAM models and maintained the largest matrix size of 9 by 8 for a part with 9 features.

Evaluation of Manufacturing Process based on the Geometric Variation Model in Multi-station Machining Processes

The objective of this paper is to explore the evaluation method of manufacturing process to verify its effectiveness based on the limitation of the variations which occur in multi-station machining processes. Firstly, the manufacturing process of a mechanical part is considered as a mechanism mainly consisted of machine-tool, part-holders, machined part, and cutting tools; And small displacement torsors (SDTs) are applied to describe all deviations in the manufacturing process, including the variation deviations of the machined surfaces of a part with regards to their nominal positions, the gap deviations associated to each joint between two contact surfaces, etc; Then, the 3D manufacturing variation model is established based on the relations between the machining feature variations and the functional tolerance requirements to realize the evaluation of manufacturing process. Finally, an application example is given to illustrate the proposed method.

Variation propagation modelling in multistage machining processes using dual quaternions

Variation propagation models play an important role in part quality prediction, variation source identification, and variation compensation in multistage manufacturing processes. These models often use homogenous transformation matrix, differential motion vector, and/or Jacobian matrix to represent and transform the part, tool and fixture coordinate systems and associated variations. However, the models end up with large matrices as the number features and functional element pairs increase. This work proposes a novel strategy for modelling of variation propagation in multistage machining processes using dual quaternions. The strategy includes representation of the fixture, part, and toolpath by dual quaternions, followed by projection locator points onto the features, which leads to a simplified model of a part-fixture assembly and machining. The proposed approach was validated against stream of variation models and experimental results reported in the literature. This paper aims to provide a new direction of research on variation propagation modelling of multistage manufacturing processes.

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

Modeling of Machining Errors’ Accumulation Driven by RFID Graphical Deduction Computing in Multistage Machining Processes

Interpreting the mechanism of machining errors' accumulation (MEA) is hard in multistage machining processes. This article addresses a novel three-dimensional graphical model named RFID-MEA for quantitating MEA inspired by the radio-frequency identification (RFID) graphical deduction computing model. RFID-MEA aims at revealing the MEA's mechanism instead regarding the machining process as a traditional “black box” model. First, Taylor's second-order expansion is employed to advise the mathematical representation of MEA and its calculation formula. Then, RFID data are collected to bond the MEA formula's variables with certain data silos in the right type at the right time. After that, raw not only structured query language (NoSQL) data computing is executed in edge nodes for assigning values to variables of the MEA formula accurately. Finally, machining errors are estimated by solving the MEA formula. By illustrating an industrial case, it is shown that the RFID-MEA has better performance in estimating machining errors, as well as with higher reliability and interpretability.

State space modelling of variation propagation in multistage machining processes for variable stiffness structure workpieces

Satisfying the quality requirement of products in multistage machining processes (MMPs) is significant and challenging nowadays. In spite of the success of the stream of variation (SoV) theory in variation propagation modelling for MMPs, the absence of elastic deformation variations could be an important factor that limits its application in variable stiffness structure (VSS) workpieces. To this end, a generic variation propagation framework in MMPs for VSS workpieces is established, incorporating the induction and propagation of elastic deformation variations. Region division strategy is adopted according to the characteristics of VSS workpieces, and the analytic solutions of elastic deformation in different regions are solved by contact theory and elastic theory. The effectiveness and accuracy of the proposed model are verified by a six-stage machining process on a four-cylinder engine block, and the proposed model is compared with the conventional SoV model resulting in a significant improvement on quality prediction.

An integrated stochastic model to optimize the machining condition and tool maintenance policy in the multi-pass and multi-stage machining

ABSTRACTDeveloping the mathematical optimization models for multi-pass and multi-stage machining processes, due to their mathematical and implicational complexity is important. Despite extensive research on the optimization of machining processes by considering different assumptions, few studies have been conducted on the processes in which the tool passes over the workpiece surface several times at different machining stages. Another challenge associated with optimizing the machining processes is to present a flexible and efficient policy for tools replacement and inspection. In this research, a nonlinear mathematical model is proposed to optimize the machining conditions and the tool replacement time based on a hybrid policy in which tool condition monitoring could be discretely and continuously. Considering these assumptions, when the tool life in the various stages of machining follows the Weibull Distribution, it brings the problem closer to real conditions. The objective of the developed model is to optimize the total machining costs and quality of the manufactured products in multi-pass and multi-stage machining. A particle swarm optimization (PSO) algorithm is developed to solve the mathematical optimization model presented in this study, then the PSO results are evaluated using the sequential quadratic programming (SQP) algorithm results.

Variation Propagation in Multistage Machining Processes Using Dual Quaternions

The application of rigid transformations matrices in variation propagation has a long tradition in manufacturing community. However, the matrix-based modeling of variation propagation in multistage machining processes is complicated. Moreover, there is a need to improve the computational efficiency of manipulation of geometrical models for variation analysis purposes. This paper introduces the representation of rigid transformation by dual quaternions, which have a computational advantage and mathematical elegance compared to matrices. In comparison to a commercial tool, the implementation of the purposed method predicted parallelism with an average error of 4.3 % in a hypothetical two stage machining process. The proposed approach has a potential to be an alternative to matrix based rigid transformation practices in variation and tolerance analysis.

Three-Dimensional Tolerance Analysis Modelling of Variation Propagation in Multi-stage Machining Processes for General Shape Workpieces

The quality control of general shape workpieces has become one of research hotspots because of the increasing diversity of products. The theory of stream of variation (SoV) for machining processes is a successful method in researching variation propagation rule. However, with the consideration of all key elements in manufacturing system, there is no unified and integrated model for workpieces in different kinds of shapes. In this paper, a new variation propagation model in multi-stage machining processes for general shape workpieces is established. It visually demonstrates the variation propagation chain and expands the universality of current SoV models. The connection of all key elements in manufacturing system is defined as an assembly chain, in which the variations are defined and propagated by modified three-dimensional tolerance analysis method. The equivalent conversion of the connection between workpiece and fixture realizes the modelling of general shape workpiece regardless of its machining method and locating scheme. Real experiments validate the effectiveness and accuracy of the new SoV model for different shape workpieces. This model has great potential to be applied toward multi-scale variation modelling, process control, and fault diagnosis for general shape workpieces.

Variation propagation of bench vises in multi-stage machining processes

Variation propagation has been successfully modeled by the Stream of Variation (SoV) approach in multistage machining processes. However, the SoV model basically supports 3-2-1 fixtures based on punctual locators and other workholding systems such as conventional vises are not considered yet. In this paper, the SoV model is expanded to include the fixture- and datum-induced variations on workholding devices such as bench vises. The model derivation is validated through assembly and machining simulations on Computer Aided Design software. The case study analyzed shows an average error of part quality prediction between the SoV model and the CAD simulations of 0.26%.