An interval programming model for continuous improvement in micro-manufacturing

Continuous quality improvement in micro-manufacturing processes relies on optimization strategies that relate an output performance to a set of machining parameters. However, when determining the optimal machining parameters in a micro-manufacturing process, the economics of continuous quality improvement and decision makers’ preference information are typically neglected. This article proposes an economic continuous improvement strategy based on an interval programming model. The proposed strategy differs from previous studies in two ways. First, an interval programming model is proposed to measure the quality level, where decision makers’ preference information is considered in order to determine the weight of location and dispersion effects. Second, the proposed strategy is a more flexible approach since it considers the trade-off between the quality level and the associated costs, and leaves engineers a larger decision space through adjusting the quality level. The proposed strategy is compared with its conventional counterparts using an Nd:YLF laser beam micro-drilling process.     

High-power efficient continuous-wave TEM00 intracavity frequency-doubled diode-pumped Nd:YLF laser

We describe experimental results with a diode-pumped, intracavity-doubled cw Nd:YLF laser in multilongitudinal mode and TEM00 spatial transverse mode with a critical phase-matched lithium triborate crystal. Taking into account the thermal effects of Nd:YLF, energy-transfer upconversion, and the thermal fracture limit, we set up a power-scaling model to optimize and design a fundamental diode-pumped Nd:YLF laser. A highly efficient second-harmonic laser was achieved, based on the optimized cavity design. A second-harmonic-generation output power of 20.5 W at a wavelength of 527 nm was obtained at an incident pump power of 60 W, corresponding to an optical-to-optical efficiency of 34.2%. The TEM00 mode green laser operates at a measured M2 parameter of 1.2. The instability of the green laser power is less than +/- 1% RMS.     

A study of micro-channel size and spatter dispersion for laser beam micro-milling

During micromachining the accuracy of micro-feature is always a major concern. Using laser beam micro-milling (LBMM) the produced size is generally larger than the input design size. Another important drawback of LBMM is the formation of melt spatter around the feature geometry. The spatter adheres within the premises of the machined feature and influences the machining results, especially the geometry of previously machined features by adhering inside or around the feature. Determination of spatter size is very important when the objective is to produce an array of micro-features such as micro-channels. Thus, the minimum distance between two consecutive micro-features is necessary to be known in order to achieve safe geometry for which the size of spatter plays a significant role. The knowledge of spatter scattering and pre-calculated distance of dispersing spatter could certainly assist the machinist in estimating the safe distance between adjacent micro-channels. In this study, the micro-channel size and average spatter size are studied during the micro-milling of Ti-6AL-4V by Nd:YAG laser beam machining (LBM). Response surface methodology (RSM) has been utilized to plan a set of experiments and mathematical models are proposed to pre-calculate the channel size and spatter dispersion. Finally, the models are validated through confirmatory tests showing an acceptable range of error.     

Diode-end-pumped electro-optically Q-switched Nd:YLF slab laser

We report a very compact Nd:YLF slab laser that is end pumped by a quasi-continuous-wave diode stack. A hybrid resonator is used to generate high output power in a near-diffraction-limited beam (i.e., a beam propogation M2 factor of less than 1.2). A pulse energy of 14.3 mJ was obtained with a pulse width of 8.5 ns at a repetition rate of 500 Hz, which corresponds to a Q-switched peak power of 1.68 MW.     

基于预测模型的虚拟试验不确定性分析方法

 工程系统中不可避免地存在各种参数不确定性,利用数值计算模型对系统进行虚拟试验时应进行不确定性分析.大型耗时计算模型的不确定性分析将面临严重的的计算复杂性问题,为此,针对工程应用中耗时计算模型,提出一种基于贝叶斯预测模型的不确定性分析仿真方法,采用概率分布为参数不确定性建模,研究系统响应预测不确定性的概率特征.泰勒杆撞击实例验证了该方法的高效性.     

A Bayesian Approach to Sequential Optimization based on Computer Experiments

Computer experiments are used frequently for the study and improvement of a process under study. Optimizing such process based on a computer model is costly, so an approximation of the computer model, or metamodel, is used. Efficient global optimization (EGO) is a sequential optimization method for computer experiments based on a Gaussian process model approximation to the computer model response. A long‐standing problem in EGO is that it does not consider the uncertainty in the parameter estimates of the Gaussian process. Treating these estimates as if they are the true parameters leads to an improper assessment of the precision of the approximation, a precision that is crucial to assess not only in optimization but in metamodeling in general. One way to account for these uncertainties is to use bootstrapping, studied by previous authors. Alternatively, some other authors have mentioned how a Bayesian approach may be the best way to incorporate the parameter uncertainty in the optimization, but no fully Bayesian approach for EGO has been implemented in practice. In this paper, we present a fully Bayesian implementation of the EGO method. The proposed Bayesian EGO algorithm is validated through simulation of noisy nonlinear functions and compared with the standard EGO method and the bootstrapped EGO. We also apply the Bayesian EGO algorithm to the optimization of a stochastic computer model. It is shown how a Bayesian approach to EGO allows one to optimize any function of the posterior predictive density. Copyright © 2014 John Wiley & Sons, Ltd.     

All-solid-state, high-power, deep-UV laser system based on cascaded sum-frequency mixing in CsLiB6O10 crystals

We report on an efficient use of CsLiB(6)O(10) (CLBO) crystals employed for an all-solid-state deep-UV laser system operated at 5 kHz. We obtained greater than 3 W of UV radiation around 242 nm by mixing the 349-nm third harmonic of a Nd:YLF laser with the tunable output from a Ti:sapphire laser in a CLBO crystal. This UV radiation was subsequently mixed with the residual 1047-nm output from the Nd:YLF laser in a second CLBO crystal. The system produced 1.5 W of deep-UV radiation at 196.3 nm, which is, to our knowledge, the highest deep-UV power below 200 nm generated in a nonlinear optical crystal. Additionally, the bandwidth of both outputs was estimated to be less than 200 MHz.     

Laser beam melting of aluminum alloys with hull-core build strategy by using an initial meso-scale simulation

Laser beam melting (LBM) of aluminum alloys is gaining a wide popularity in different industrial applications as an alternative technology for the production of individual and complex parts. A long build time and the high amount of experimental work for optimizing or finding new process parameters are two of the current challenges for reaching an industrial maturity. This paper proposes an efficient way to determine new process parameters for aluminum alloy aluminum-silicon10-magnesium with highest build-up rates by using a 3D finite element model on the mesoscopic level. High laser power in combination with the hull-core build strategy was used to increase the build-up rate without impairing the part accuracy. The influences of high laser power, laser diameter and scan speed on the melt pool were studied by using a thermal simulation of single laser tracks. Based on the simulation results the process window could be derived and was tested on a laser beam melting (LBM) system. The achieved reduction of the build time of up to 31 % without loss in part accuracy proved the novel approach for the prediction of the required process window as an efficient method to reduce costly and time-consuming experimental work.     

A Bayesian framework for uncertainty quantification of perturbed gamma process based on simulated likelihood

The perturbed gamma process (PGP) has recently been widely used in modeling the noisy degradation data collected from engineering structures and components since it can simultaneously consider the temporal variability of degradation and measurement uncertainty. As a result of the sampling and inspection uncertainty in engineering practice, it is necessary to account for the resulting parameter uncertainty. Meanwhile, the flexibility of the form of measurement error motivates a potential demand for quantifying the model uncertainty and selecting the most fitting error model for the given inspection data. The Bayesian approach is well-suited to quantity the parameter uncertainty induced by imperfect inspection and limited inspection data, but its practical implementation is extremely challenging due to the intractable likelihood function of PGP. In the paper, a novel Bayesian framework for quantifying parameter and model uncertainty of PGP is presented, where the simulated likelihood that is an unbiased estimator generated by Sequential Monte Carlo (SMC) is introduced to overcome the intractable likelihood of PGP. More specifically, an Adaptive Particle Markov chain Monte Carlo (APMCMC) is proposed to perform the Bayesian sampling from the posterior distributions of parameters, achieving the requirement for the quantification of parameter uncertainty. By utilizing the posterior samples from APMCMC, a model selection method based on the Bayes factor is employed to determine the most fitting one from some alternative error models. Finally, two simulation examples are presented to illustrate the efficiency and accuracy of the proposed framework and its applicability is confirmed by a practical case involving the corrosion modeling of a group of pipelines.     

Model-based process optimization in the presence of parameter uncertainty

In model-based process optimization one uses a mathematical model to optimize a certain criterion, for example the product yield of a chemical process. Models often contain parameters that have to be estimated from data. Typically, a point estimate (e.g. the least squares estimate) is used to fix the model for the optimization stage. However, parameter estimates are uncertain due to incomplete and noisy data. In this article, it is shown how parameter uncertainty can be taken into account in process optimization. To quantify the uncertainty, Markov Chain Monte Carlo (MCMC) sampling, an emerging standard approach in Bayesian estimation, is used. In the Bayesian approach, the solution to the parameter estimation problem is given as a distribution, and the optimization criteria are functions of that distribution. The formulation and implementation of the optimization is studied, and numerical examples are used to show that parameter uncertainty can have a large effect in optimization results.     

1053-nm-wavelength selection in a diode-laser-pumped Nd:YLF laser

We report on a Nd:YLF laser that operates at 1053 nm without optical intracavity elements for the suppression of the stronger 1047-nm transition. The Nd:YLF crystal is end pumped by a fiber optically coupled 10-W diode-laser bar. The different thermal-lensing focal lengths of the two main lasing wavelengths in a plane-parallel resonator were used to achieve the selection by tilting the end mirror slightly from its optimum position for maximum output power. With 9.8-W cw diode-laser-pumping power the 1053-nm Nd:YLF laser produces a maximum output power of 1.9 W in cw operation and nearly 1 W of average power at a Q-switch repetition rate of 15 kHz. The highest slope efficiency of 47% achieved in cw operation.     

Highly stable, all-solid-state Nd:YLF regenerative amplifier

A diode-pumped Nd:YLF regenerative amplifier (regen) has been developed and is in use in the 60-beam, 30-kJ UV Omega laser system's driver line. The high stability, the compactness, and the reliability of this all-solid-state modular design are the key features of this concept. Stable, millijoule-level output-pulse energies with an overall gain of 10(9) have been demonstrated. Excellent long-term output-pulse-energy stability of better than 1% rms fluctuations has been achieved with excellent beam quality (<1% ellipticity).     

Frequency-modulation mode-locking performance for four Nd(3+)-doped laser crystals

To determine an optimum host for simultaneous short-pulse-width and large-slope-efficiency generation, the performance of Nd:YAG, Nd:YLF, Nd:YVO(4), and Nd(3+):Sr(5)(VO(4))(3)F [Nd:strontium fluorovanadate (S-VAP)] was characterized under a variety of end-pumped and frequency-modulation mode-locking conditions. The slope efficiency, threshold pump power, and pulse width were recorded for each laser and compared with theory. A figure of merit was defined, yielding Nd:YLF and Nd:YVO(4) as the experimentally determined crystals of choice.     

Optimization of the power and control of the shape of amplified trains of laser pulses

We have performed an optimization study on a train of laser pulses in a Nd:YLF master-oscillator power amplifier chain. Instead of using a flat train of input pulses and proper timing of the input pulses with respect to the pump pulse to keep the output pulse flat, we used a pulse-shaping technique. Then the maximum gain could be used, resulting in a 70% increase in output pulse energy. We constructed a special feed-forward loop to control the temporal shape of the train. We compare the results with a computational model based on the rate equations in the Nd:YLF amplifiers.     

Design of ultrahigh brightness solar-pumped disk laser

To significantly improve the solar-pumped laser beam brightness, a multi-Fresnel lens scheme is proposed for side-pumping either a single-crystal Nd:YAG or a core-doped ceramic Sm³⁺ Nd:YAG disk. Optimum laser system parameters are found through ZEMAX and LASCAD numerical analysis. An ultrahigh laser beam figure of merit B of 53?W is numerically calculated, corresponding to a significant enhancement of more than 180 times over the previous record. 17.7 W/m² collection efficiency is also numerically attained. The strong thermal effects that have hampered present-day rod-type solar-pumped lasers can also be largely alleviated.     

Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel

Proper definition of certain material properties is a paramount issue for accurate simulation. However, the values of a material parameter are commonly uncertain due to multiple factors in practice. To obtain reliable material parameters, parameter identification via Bayesian theory has become an attractive framework and received more attention recently. Based on this frame, the determination of likelihood function is critical for posterior probability. Unfortunately, it is commonly difficult to be determined directly, especially for complex engineering problems. In this study, Bayesian formulas for material parameter identification are given. To make it feasible for real engineering problems, the least square-support vector regression surrogate and Monte Carlo Simulation are integrated to obtain the maximum likelihood estimation of likelihood function. The uncertainty of parameter identification is quantified via the Bayesian method. In two benchmarks, two cases with single and multiple uncertainty sources are used to propagate and quantify uncertainties in material parameters based on Bayesian approach. Moreover, the proposed method is used to identify the material parameters of advanced high strength steel used in vehicle successfully.     

A Bayesian method for robust tolerance control and parameter design

This paper proposes a Bayesian method to set tolerance or specification limits on one or more responses and obtain optimal values for a set of controllable factors. The existence of such controllable factors (or parameters) that can be manipulated by the process engineer and that affect the responses is assumed. The dependence between the controllable factors and the responses is assumed to be captured by a regression model fit from experimental data, where the data are assumed to be available. The proposed method finds the optimal setting of the control factors (parameter design) and the corresponding specification limits for the responses (tolerance control) in order to achieve a desired posterior probability of conformance of the responses to their specifications. Contrary to standard approaches in this area, the proposed Bayesian approach uses the complete posterior predictive distribution of the responses, thus the tolerances and settings obtained consider implicitly both the mean and variance of the responses and the uncertainty in the regression model parameters.     

Automatic output-beam-collimating configuration for high-average-power multirod resonator-amplifier systems

A novel configuration for a multirod solid-state laser system is proposed. We stabilize the thermal-lens-dependent variation in the output-beam parameters by extracting the laser beam from the collimating point of periodic beam propagation. The performance is confirmed with a 100-W class diode-pumped Nd:YAG laser-amplifier system to demonstrate tenfold stabilization of the output-beam diameter.     

Effect of laser welding parameters on fusion zone morphological,mechanical and microstructural characteristics of AISI 304 stainless steel

Nowadays, the Nd:YAG laser has been a promising key tool for joining thin components. In this research, mechanical and microstructural properties of laser welded thin austenitic stainless steel sheets were investigated with experimental investigations, as a function of laser welding parameters. Butt welded joints were made using a Nd:YAG laser in the pulsed wave mode. The appropriate laser welding parameters were found in order to obtain quality and strong weld seam. The pulsed laser seam welding process is controlled by a variety of parameters. We focus on the effects of the several processing parameters on mechanical and microstructural characteristics of joint and weld quality. The aim of this research was to evaluate the influence of these processing parameters on joint strength and microstructure. And also we examined the weldability of stainless steels in butt joint configuration by a pulsed Nd:YAG laser beam.     

NPUA: A new approach for the analysis of computer experiments

An important problem in the analysis of computer experiments is the specification of the uncertainty of the prediction according to a meta-model. The Bayesian approach, developed for the uncertainty analysis of deterministic computer models, expresses uncertainty by the use of a Gaussian process. There are several versions of the Bayesian approach, which are different in many regards but all of them lead to time consuming computations for large data sets.In the present paper we introduce a new approach in which the distribution of uncertainty is obtained in a general nonparametric form. The proposed approach is called non-parametric uncertainty analysis (NPUA), which is computationally simple since it combines generic sampling and regression techniques. We compare NPUA with the Bayesian and Kriging approaches and show the advantages of NPUA for finding points for the next runs by reanalyzing the ASET model.