[This abstract is based on the author's abstract.] Improving the quality of a product/process using a computer simulator is a much less expensive option than the real physical testing. However, simulation using computationally intensive computer models ..

Space-filling designs for computer experiments: A review

Computer experiments may involve not only continuous input factors but also nominal factors, discrete numeric factors, and ordinal factors. Most existing literature in designing computer experiment...

Designing computer experiments with multiple types of factors: The MaxPro approach

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

Statistical calibration and uncertainty quantification of complex machining computer models

Design and Modeling

During the development of CNC machining, the computer-based NC machining simulation has played an important role in verifying the NC programs economically and safely so as to avoid scraps or even accidents. In order to balance the accuracy, efficiency, and fidelity, various simulation methods have been proposed in historical researches, including wireframe, solid, object space-based, and image space-based simulation. All the mentioned methods use G-code as input and take the workpiece blank as a whole part, which causes the difficulty for achieving real-time simulation with acceptable accuracy under limited computing resources. This paper proposed a STEP-NC feature-oriented machining simulation method, which takes the workpiece blank as the combination of several machining features plus the workpiece, and implements customized algorithms for individual features. Using the divide and conquer strategy, high efficiency with guaranteed accuracy and fidelity can be obtained during the real-time simulation. Two demo workpieces with 2.5D features from the STEP-NC standard were tested using the proposed method, and the efficiency was validated by comparing results with the conventional methods.

STEP-NC feature-oriented high-efficient CNC machining simulation

Understanding the uncertainty in simulation outputs is important for careful decision-making regarding a machining process However, Monte Carlo–based methods cannot be used for evaluating the uncertainty when the simulations are computationally expensive An alternative approach is to build an easy-to-evaluate emulator to approximate the computer model and run the Monte Carlo simulations on the emulator Although this approach is very promising, it becomes inefficient when the computer model is highly nonlinear and the region of interest is large Most machining simulations are of this kind because the output is affected by several quantitative factors—such as the workpiece material properties, cutting tool parameters, and process parameters whose effects can change depending on other qualitative factors such as the type of materials, tool designs, and tool paths Because the number of levels of the qualitative factors can range from tens to thousands, building an accurate emulator is not an easy task This article proposes a new approach, called an in situ emulator, to overcome this problem The idea is to build an emulator for the user-specified levels of the qualitative factors and inside the local region defined by the input uncertainty distribution of the quantitative factors Efficient experimental design and statistical modeling techniques are used for constructing the in situ emulator The approach is illustrated using the simulations of two solid end milling processes

Uncertainty quantification of machining simulations using an in situ emulator

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Uncertainty quantification of machining simulations using an in situ emulator