Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated ...framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications.
Slicing silicon wafers for solar cells and micro-electronic applications by diamond wire sawing has emerged as a sustainable manufacturing process with higher productivity, reduced kerf-loss, thinner ...substrates that save material, and reduced environmental impact through the use of water-based cutting fluids, compared to the conventional loose abrasive slurry sawing process. This paper reviews recent research on diamond wire sawing of photovoltaic silicon wafers and compares it with the loose abrasive wire sawing process from a standpoint of sustainable manufacturing. Various aspects of the diamond wire sawing process including surface morphology, total thickness variation (TTV), surface and subsurface damage, fracture strength, residual stress, stress induced phase transformation, effect of microstructure and abrasive grit shape are critically reviewed and areas of future need are identified.
In this work, we present a deep neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining and finishing from design and manufacturing data. By ...concatenating a 3D Convolutional Neural Network (3D CNN) with a simple Multilayer Perceptron (MLP), we show that the model can learn the capabilities of a manufacturing process described in terms of the part features and quality it can generate, and the materials it can process. Specifically, the proposed method takes the part feature geometry, material properties, and quality information contained in a part design as inputs and trains the deep neural network model to predict the manufacturing process label as output. We present an example implementation of the proposed method using a synthesized dataset to illustrate automatic manufacturing process selection. The performance of the proposed model is compared with the performance of interpretable data-driven classification methods such as decision trees and random forests. By comparing the performance with different combinations of input information to be included during training, it is evident that part quality information is necessary for characterizing the capabilities of finishing processes while material information further improves the model’s ability to discriminate between the different process capabilities. The superior prediction accuracy of the proposed deep neural network model demonstrates its potential for use in future data-driven Computer Aided Process Planning (CAPP) systems.
Finite element based machining process models are used in research and industry for process design and optimization. These models require a constitutive description of the material behavior to ...accurately model and predict process responses such as cutting forces, temperatures, and residual stress. Calibration of these models to low-strain uniaxial dynamic compression experiments can be troublesome since the machining process generally imposes much larger strains than uniaxial compression. Calibration of finite element models directly to machining data is generally difficult since the models are computationally expensive and nonlinear optimization methods for estimating the unknown calibration parameters yield non-unique solutions and require many iterations. In this work we utilize a nonstationary Gaussian Process surrogate model to emulate the finite element response and calibrate to experimental orthogonal cutting tests using a Bayesian inference framework. We assume that the material yield behavior can be described by the Johnson-Cook material flow model. We find that the nonstationary Gaussian Process model is an good surrogate for the complex finite element model. Cutting forces measured from orthogonal tube turning experiments were used for calibration. Validation is performed using a separate response variable - the cut chip thickness. Calibration results illustrate a preference for material models with low hardening rates, which alleviates issues such as over-prediction of strain hardening behavior when using the Johnson-Cook material flow model. The Bayesian formulation also captures the uncertainty in the Johnson-Cook parameters, which can be used to quantify the uncertainty in the machining process responses. The methods presented here are general and can be used for more complex constitutive and tribological models for machining and other complex manufacturing processes.
•Bayesian calibration used to infer flow stress directly from machining experiments.•Multi-output Gaussian process model used to emulate expensive finite element model.•Framework quantifies constitutive and tribological parameter uncertainties.•Approach is general and may be used beyond machining for other complex processes.
•The main source of error resulting from real-time correctional commands to an industrial robot is identified to be the structural dynamics of the manipulator.•A way of modeling robot dynamics which ...allows them to be directly incorporated into a closed-loop system model is presented.•This model represents real-time corrections by their “equivalent force” by assuming they are achieved with a constant acceleration.•A method of using the model to predict closed-loop stability of control systems involving industrial manipulators is presented.•The new method alleviates the need to manually tune feedback controllers for industrial manipulators.
With the demand for higher position accuracy from industrial robots used for precision manufacturing tasks, a common solution approach is to implement closed-loop feedback control using external sensors. Because most industrial robot controllers only allow real-time commands to be specified in the form of Cartesian or joint position offsets, the plant models of these closed-loop systems tend to be very simple in that they assume that the robot executes each input command with minimal or no error. However, real-time motion error can be of the order or larger than the corresponding input commands. Due to the shortcomings of these simplistic models, closed-loop controller gains need to inevitably be tuned manually through trial and error. If the missing components of the simplistic plant models can be identified, closed-loop controller gains can be readily determined efficiently through simulation. In this paper, robot controller delay and robot dynamics are identified as the key missing components, and a new data-driven method for capturing the robot dynamics and a model for closed-loop stability prediction are established. The new model-based method is experimentally evaluated on a six degree-of-freedom (6-DoF) industrial manipulator. It is confirmed that the new method can be used to determine via simulation robot controller gains that ensure closed-loop stability without the need for iterative trial and error experimental gain-tuning.
Machining is a severe plastic deformation process that subjects materials to high rates of deformation and elevated temperatures. Dynamic recrystallization during severe plastic deformation drives ...grain refinement into the sub-micron range but the ductility and thermal stability of these structures is poor. In contrast, bimodal microstructures consisting of both fine and coarse grains have been shown to exhibit attractive strength and ductility properties at these temperatures. In this paper, we study the process–structure–property relationships for pure copper subject to a machining process where the cutting speeds are varied. Further, we investigate the role of thermal effects by varying the post-deformation cooling rates. Microstructures generated under these deformation conditions are quantified using angularly resolved chord length statistics. The derived metrics are found to be robust descriptors of the studied microstructures as they automatically capture complex features such as unimodal and bimodal distributions of grain structure, grain constituent length scales, and morphological anisotropy induced by shear deformation. The uniaxial equivalent yield strength of the generated structures are estimated using spherical nanoindentation and inverse modeling techniques. Finally, we present a methodology for identifying the constrained property-process inverse mapping for machining using a Bayesian framework and the established forward process–structure–property mapping.
A hybrid post-processing method that synergistically combines cavitation peening and electrochemical polishing to achieve superior surface quality of solid and lattice structured additively ...manufactured (AM) metal parts is analysed. The method enables surface strengthening of AM parts through plastic deformation caused by cavitation while simultaneously improving the surface finish through electrochemical dissolution of surface asperities. Compared to sequential processing, the hybrid process produces higher microhardness and comparable surface roughness in a single step. Results show that coupling of the physical-chemical effects accompanying cavitation and electrochemical reaction can enhance the cavitation intensity and dissolution efficiency in hybrid processing.
Fundamental understanding of the fixed abrasive slicing of photovoltaic silicon wafers is crucial for producing low‐cost wafers with superior surface quality and mechanical strength. With the goal of ...understanding the diamond wire sawing process, this paper investigates the scribing of mono‐ and multi‐crystalline silicon by the abrasive grits on an actual diamond wire. Specifically, the effects of grit shape and silicon crystal structure on the resulting surface morphology, subsurface damage, and the critical depth of cut at which ductile‐to‐brittle transition occurs are investigated. Results show that surface cracking depends on the grit shape. Scribing across the grain and twin boundaries in multi‐crystalline silicon impacts the resulting surface morphology, with grit shape producing a greater effect than crystallographic orientation in the grain interior relative to the grain boundary. Subsurface damage depends on the grit shape and crystal structure. Differences in the critical depth of cut for ductile‐to‐brittle transition in scribing of mono‐crystalline silicon are explained via analysis of the stress state produced by idealized grit shapes.
This paper reviews recent advances in constitutive and friction data and models for simulation of metal machining. Phenomenological and physically-based constitutive models commonly used in machining ...simulations are presented and discussed. Other topics include experimental techniques for acquiring data necessary to identify the constitutive model parameters, and recent advances in modelling of tool-workpiece friction and experimental techniques to acquire friction data under machining conditions. Additionally, thermo-physical properties for thermal modelling of the machining process, and microstructure data for the chip and workpiece together with relevant experimental methods are discussed. Future research needs in each of the focused areas are highlighted.
This paper presents a finite element model for white layer formation in orthogonal machining of hardened AISI 52100 steel under thermally dominant cutting conditions that promote martensitic phase ...transformations. The model explicitly accounts for the effects of stress and strain, transformation plasticity and the effect of volume expansion accompanying phase transformation on the transformation temperature. Model predictions of white layer depth are found to be in agreement with experimental values. The paper also analyzes the effect of white layer formation on residual stress evolution in orthogonal cutting of AISI 52100 hardened steel. Model simulations show that white layer formation does have a significant impact on the magnitude of surface residual stress and on the location of the peak compressive residual stress.
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