The increasing adoption of advanced three-dimensional (3D) scanning technologies has made large-scale point clouds containing millions of 3D measurement points standard in applications like manufacturing. However, processing immense amounts of 3D data imposes significant computational loads, often resulting in discarded critical information and suboptimal outcomes for downstream tasks. This paper introduces Point-ITR, a task-oriented sampling method tailored for regression tasks, which selectively retains the most informative points within large-scale point clouds. Specifically, we propose a gradient-based importance sampling framework for intra-sample selection (selecting points within a 3D point cloud) and a feature-based weighting scheme for inter-sample selection (selecting among different 3D point cloud sub-samples). Additionally, we introduce an iterative random sampling (ItrRS) module for preprocessing and an Offset Residual Block that utilizes a reference design model to learn structural features and accelerate both training and testing, which allows a simple fully connected network to process large-scale point clouds. Our approach improves prediction accuracy across downstream tasks while ensuring that the rich details captured are fully utilized for interpretation, offering a more effective and efficient solution. We validate our methodology through simulation studies and real-world case applications in additive manufacturing, demonstrating its robustness and practical applicability.

In manufacturing processes, a multitude of sensors are typically deployed to collect data of process parameters. While this provides an opportunity to better predict and control the quality of the final product, the relationship between the process variables and the desired final quality is often not well-understood. To establish this relationship, machine learning (ML) models can be used. However, collecting large, labeled datasets to train the ML model can be difficult, as creating such datasets typically involves costly or destructive end-of-line quality testing of the products. To overcome this challenge, we propose a novel framework called Physics-informed Weakly-supervised Learning (PWL) that integrates physics-based models with data-driven ML models. By leveraging physical knowledge and using the outputs of physics-based models as weak labels, PWL offers an alternative to traditional methods that require large, labeled datasets. Our approach simultaneously optimizes the data-driven ML model, as well as the discrepancy and calibration parameters of the physics-based model, resulting in superior predictive performance compared to either model used alone. We demonstrate the effectiveness of PWL through simulation experiments, comparisons with existing methods, and two real-world case studies, highlighting its potential for improving quality prediction in various manufacturing systems.Note to Practitioners—The proposed method in this paper, Physics-informed Weakly-supervised Learning (PWL), addresses a common problem in manufacturing processes, where obtaining large, labeled datasets can be costly or not always possible. By integrating physics-based models with data-driven ML models, PWL leverages available physical knowledge to improve the predictive performance of product quality in manufacturing systems. This enables practitioners to better understand and optimize their manufacturing processes even with limited labeled datasets, leading to improved product quality and reduced production costs.

Multistage Manufacturing Systems (MMS) are common in industries involving complex processes with multiple stages, each impacting the final product quality. Traditional product quality modeling approaches struggle with the intricate interdependencies and variable structures within these systems, further complicated by the extensive sensor-generated data. In this paper, we introduce Graph-based Variation Propagation Network (GVPNet), an innovative approach for end-to-end learning of product quality representations and their propagation through MMS stages. GVPNet utilizes a heterogeneous Graph Attention Network (hGAT) architecture that uses a graph representation specifically tailored for MMS, facilitating the effective application of graph neural networks (GNN) in this context. The network’s ability to aggregate information and learn node embeddings enables it to predict quality variables several stages ahead, thus offering a proactive tool for quality control. GVPNet was applied in two real world case studies and demonstrated its superiority in predictive accuracy and robustness over benchmarks.

Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. However, many current methods simply generate defects following a fixed set of rules, which may not directly relate to downstream task performance. This can lead to suboptimal performance and may even hinder the downstream task. To solve this problem, we leverage a novel bi-level optimization-based synthetic defect data generation framework. We use an online synthetic defect generation module grounded in the commonly-used Cut&Paste framework, and adopt an efficient gradient-based optimization algorithm to solve the bi-level optimization problem. We achieve simultaneous training of the defect segmentation network, and learn various parameters of the data synthesis module by maximizing the validation performance of the trained defect segmentation network. Our experimental results on benchmark datasets under limited data settings show that the proposed bi-level optimization method can be used for learning the most effective locations for pasting synthetic defects thereby improving the segmentation performance by up to 21.3% when compared to pasting defects at random locations. We also demonstrate up to 2.7% performance gain by learning the importance weights for different augmentation-specific defect data sources when compared to giving equal importance to all the data sources.

Prognostics for electronic packages is an evolving field critical to predicting the reliability and lifespan of electronic systems. This article proposes a novel “structure-aware system-level (SASL)” approach, addressing the limitations of traditional methods that treat components or subsystems as isolated black boxes. SASL examines how individual component degradation propagates, interacts within the package structure, and collectively determines the system's lifetime. The article reviews three key areas: component-level prognostics, package structure, and system-level analysis, offering guidance for future research. It advocates interdisciplinary collaboration to develop practical and interpretable prognostics methods, driving innovation in industries reliant on complex electronic systems.

Unlocking the power of 3D point cloud machine learning models can be a challenge due to the need for extensive labeled datasets, which presents a challenge when applying these models to new domains. Transfer learning can help overcome this challenge by utilizing data from related tasks to enhance model performance. However, traditional (2D) transfer learning methods struggle with 3D point cloud domain adaptation, due to differences in physical environments and sensor configurations. To address this issue, we propose PLURAL, a novel 3D point cloud transfer learning methodology based on contrastive learning with augmentations. Our approach is inspired by the notion that high-level shape features are more transferable than low-level geometry features. We propose a co-training architecture that includes separate 3D point cloud models with domain-specific parameters, as well as a module for learning domain-invariant features. Additionally, PLURAL extends the approach of contrastive instance alignment to 3D point cloud modeling by considering physics-informed hard sample mining. Our experiments on simulation and real-world datasets demonstrate that PLURAL outperforms state-of-the-art transfer learning methods by a significant margin, effectively reducing the domain gap.Note to Practitioners—The usage of 3D point cloud machine learning models is currently limited by the need for extensive labeled data. With our proposed framework, data from related tasks can be utilized to enhance the model performance on new applications or domains. PLURAL explicitly considers the acquisition of 3D point clouds by diverse sensors and in diverse environments. The method is highly adaptable and includes separate models with domain-specific parameters, making it applicable to a wide range of applications and domains.

Unstructured point clouds of varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a vector response based on unstructured point clouds is a common problem that arises in a wide variety of applications. The current literature relies on several pre-processing steps such as structured subsampling and feature extraction to analyze the point cloud data. Those techniques lead to quantization artifacts and do not consider the relationship between the regression response and the point cloud during pre-processing. Therefore, we propose a general and holistic “Bayesian Nonlinear Tensor Learning and Modeler” (ANTLER) to model the relationship of unstructured, varying-size point cloud data with a vector response. The proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality reduction and a nonlinear regression model with a 3D point cloud input and a regression response. ANTLER can consider the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data. Note to Practitioners—This paper is motivated by a real-world case study concerning the prediction of the transmission error and eccentricity based on unstructured point clouds of varying sizes in gear manufacturing. In the current state-of-the-art method, those characteristics can only be obtained via expensive and time-consuming Finite Element Analysis (FEA) or test benches. The proposed ANTLER framework can directly link the measurement point clouds with a vector response and serves as a guiding example for the immense potential of the ANTLER.

With advancements in sensor technology, high dimensional signals such as functional curves and images are typically collected from multiple sensors to characterize the degradation of a system. Data fusion methods are employed to integrate multisensor signals generated from the system into a scalar health index (HI) to understand the degradation status of the system. This paper develops sparse group LASSO-principal component analysis (SGL-PCA), a method that constructs HIs for image and profile data. First, we remove the smooth background from each sensor signal. Then, we solve the degradation patterns and the degradation paths through a rank-one matrix approximation problem, with the consideration of the sparsity of the measurements related to the degradation process and the monotonicity of the degradation paths. Results from a simulation study and a case study illustrate that the HI constructed by the proposed method outperforms the benchmark methods in identifying the measurements subject to the degradation process and predicting the remaining useful life of the system. Note to Practitioners—In practice, sensors generating multiple high-dimensional curves and images are often installed in systems to characterize their degradation status. Compared with scalar sensor signals, the information that associates with the degradation process often appears in sparse regions from the sensor signals. Therefore, identifying the degradation information accurately is important in the health index (HI) construction for degradation modeling and prognostic analysis. This article proposes a method that simultaneously selects the degradation information and estimates the optimal weights for integrating multi-sensor signals in constructing the HI. The proposed method is applicable in the case where the systems degrade under a single failure mode, and multiple sensors are used to monitor the degradation processes. Practitioners can implement our method to predict the remaining useful lives of in-service systems through three steps: (1) estimate the backgrounds of each sensor and derive data-fusion model using a historical dataset; (2) construct the HIs of in-service systems; (3) predict the remaining useful lives of these systems based on the developed HIs.

Tensor data with rich structural information become increasingly important in process modeling, monitoring, and diagnosis in manufacturing medical and other applications. Here structural information is referred to the information of tensor components such as sparsity, smoothness, low-rank, and piecewise constancy. To reveal useful information from tensor data, we propose to decompose the tensor into the summation of multiple components based on their different structural information. In this article, we provide a new definition of structural information in tensor data. We then propose an additive tensor decomposition (ATD) framework to extract useful information from tensor data. This framework specifies a high dimensional optimization problem to obtain the components with distinct structural information. An alternating direction method of multipliers (ADMM) algorithm is proposed to solve it, which is highly parallelable and thus suitable for the proposed optimization problem. Two simulation examples and a real case study in medical image analysis illustrate the versatility and effectiveness of the ATD framework. Note to Practitioners—This article was motivated by a real case in medical imaging: extracting aortic valve calcification (AVC) regions from the tensor data obtained from computed tomography (CT) image series of the aortic region. The main objective is to decompose image series into multiple components corresponding to tissues, calcium deposition, and error. Similar needs are pervasive in other medical image analysis applications as well as the image-based modeling, monitoring, and diagnosis of industrial processes and systems. Existing methods fail to incorporate a detailed description of the properties of image series that reflect the physical understanding of the system in both the spatial and temporal domains. In this article, we provide a systematic description of the properties of image series and use them to develop a decomposition framework. It is applicable to various applications and can generate more accurate and interpretable results.

Fourier-transform infrared spectroscopy (FTIR) is a widely adopted technique for characterizing the chemical composition in many physical and chemical analyses. However, FTIR spectra are subject to multiple sources of uncertainty, and thus the analysis of them relies on domain experts and can only lead to qualitative conclusions. This study aims to analyze the effect of a certain treatment on FTIR spectra subject to two commonly observed uncertainties, the offset shift and the multiplicative error. Due to these uncertainties, the pre-exposure FTIR spectra are modeled according to the physical understanding of the uncertainty—observed spectra can be viewed as translating and stretchering an underlying template signal, and the post-exposure FTIR spectra are modeled as the translated and stretchered template signal plus an extra functional treatment effect. To provide engineering interpretation, the treatment effect is modeled as the product of the pattern of modification and its corresponding magnitude. A two-step parameter estimation algorithm is developed to estimate the underlying template signal, the pattern of modification, and the magnitude of modification at various treatment strengths. The effectiveness of the proposed method is validated in a simulation study. Furtherly, in a real case study, the proposed method is used to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment.Note to Practitioners—FTIR spectrometer is often used to characterize the surface chemical composition of a material. Due to the large uncertainties associated with the nature of spectrometer and the measurement environment, the FTIR signals are usually examined visually by experienced engineers and technicians in industrial applications, which can be both time-consuming and inaccurate. To understand the effect of plasma exposure on the surface property of carbon fiber reinforced polymer (CFRP) material, the elimination of uncertainties associated with FTIR signals is investigated, and a systematic method is proposed to quantify the effect of surface treatments on FTIR signals. A two-step analytic procedure is proposed, which provides information on how the plasma exposure distorts the FTIR signals, and how the plasma distance relates to the magnitude of the distortion. The methodology in this article can be used to analyze the treatment effect on a variety of spectroscopic measurements that are subject to uncertainties such as offset and scaling errors, which expands the applications of in situ handheld spectrometer metrology in manufacturing industries.

Heterogeneous but complementary sources of data provide an unprecedented opportunity for developing accurate statistical models of systems. Although the existing methods have shown promising results, they are mostly applicable to situations where the system output is measured in its complete form. In reality, however, it may not be feasible to obtain the complete output measurement of a system, which results in observations that contain missing values. This article introduces a general framework that integrates tensor regression with tensor completion and proposes an efficient optimization framework that alternates between two steps for parameter estimation. Through multiple simulations and a case study, we evaluate the performance of the proposed method. The results indicate the superiority of the proposed method in comparison to a benchmark. Note to Practitioners—The proposed method aims to obtain an accurate estimation of the regression model when certain entries of the response are inaccessible. By considering both the information from multiple inputs and the structure of the response, our proposed method can achieve more accurate estimation of the output tensor. In order to apply the proposed method in practice, two assumptions should hold. First, the response tensor should be low-rank, meaning that fewer variation patterns should exist in the response than its dimensions. Second, the relationship between the input tensors and the response should be linear or approximately linear. The presented method in this article uses tensor decomposition techniques to exploit the correlation structures of the high-dimensional data and prevent overfitting. Another benefit of our integrated framework is that the rank of the response tensor converges automatically, which can be used directly in the parameter estimation.

In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression, such as linear regression or Gaussian process (GP). Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for GP. In this article, we propose two new active learning algorithms for the GP with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact of uncertainties and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage.

Degradation modeling is a critical and challenging problem as it serves as the basis for system prognostics and evolution mechanism analysis. In practice, multiple sensors are used to monitor the status of a system. Thus, multisensor data fusion techniques have been proposed to capture comprehensive information for prognostic modeling and analysis, which aims at developing a composite health index (HI) through the fusion of multiple sensor signals. In the literature, most existing methods use a linear data-fusion model for integration of multisensor data to construct the HI, which is insufficient to model nonlinear relations between sensing signals and HI in a complicated system. This article proposes a novel data fusion method based on deep learning for HI construction for prognostic analysis. A pair of adversarial networks is proposed to enable the training procedure of neural networks. To guarantee the stability of the algorithm, we propose a root mean square propagation (i.e., RMSprop)-based sampling algorithm to estimate model parameters. A set of simulation studies and a case study on a set of degradation signals of aircraft engines are conducted. The results demonstrate that the proposed method has a significant improvement on remaining useful life prediction compared to existing data fusion methods.

Learning the relationship between a response variable (e.g., a quality characteristic) and a set of predictors (e.g., process variables) is of special importance in process modeling, prediction, and optimization. In many applications, not only is the number of these variables large but these variables are also high-dimensional (HD) (e.g., they are represented by waveform signals). This high dimensionality requires a systematic approach to both modeling the relationship between the variables and removing the noninformative input variables. This article proposes a functional regression method in which an HD response is estimated and predicted through a set of informative and noninformative HD covariates. For this purpose, the functional regression coefficients are expanded through a set of low-dimensional smooth basis functions. In order to estimate the low-dimensional set of parameters, a penalized loss function with both smoothing and group lasso penalties is defined. The block coordinate decent (BCD) method is employed to develop a computationally tractable algorithm for minimizing the loss function. Through simulations and case studies, the performance of the proposed method is evaluated and compared with benchmarks. The results illustrate the advantage of the proposed method over the benchmarks.

Change point detection in torque signals has widely been adopted for quality inspection during pipe tightening processes. Previous studies on the change point detection in this process generally focus on directly detecting the change points throughout torques without considering the underlying mechanism that generates various quasi-periodic nonlinear profiles, thereby introducing a series of false change points and increasing the risk of releasing defective pipes. To overcome this problem, we propose a novel change-point detection approach by fully considering the profile generating mechanism, and introduce a similarity-weighted matrix with an integration of dynamic time warping and tightening physics. Thus, the probability of false detection of change points is reduced. A weighted regression model is developed to determine the authentic change points by introducing the tightening process constraints. The performance of the proposed approach is demonstrated by both numerical and real case studies, and results show that the proposed method achieves a more effective detection power than the other existing methods in the pipe tightening processes.

Modeling and analysis of profiles, especially high-dimensional nonlinear profiles, is an important and challenging topic in statistical process control. Conventional mixed-effects models have several limitations in solving the multichannel profile detection problems for in-line Raman spectroscopy, such as the inability to separate defective information from random effects, computational inefficiency, and inability to handle high-dimensional extracted coefficients. In this paper, a new wavelet-based penalized mixed-effects decomposition (PMD) method is proposed to solve the multichannel profile detection problem in Raman spectroscopy. The proposed PMD exploits a regularized high-dimensional regression with linear constraints to decompose the profiles into four parts: fixed effects, normal effects, defective effects, and signal-dependent noise. An optimization algorithm based on the accelerated proximal gradient (APG) is developed to do parameter estimation efficiently for the proposed model. Finally, the separated fixed effects coefficients, normal effects coefficients, and defective effects coefficients can be used to extract the quality features of fabrication consistency, with in sample uniformity, and defect information, respectively. Using a surrogated data analysis and a case study, we evaluated the performance of the proposed PMD method and demonstrated a better detection power with less computational time.

Multistage manufacturing processes (MMPs) usually exhibit an interactive relationship between tool wear and product quality degradation. On one hand, the tool wear in a stage may result in the quality degradation of the products fabricated on that stage. On the other hand, the quality degradation at a preceding stage may lead to the change of the operational condition and thus affect the tool wear in subsequent stages. This interaction needs to be considered to accurately predict the residual life distribution (RLD) of MMPs, which will benefit condition-based maintenance and tool inventory management. In this paper, we propose an interaction model that utilizes a linear model to represent the impact of tool wear on quality degradation and a stochastic differential equation model to capture the impact of quality degradation on the instantaneous rate of tool wear. We then propose a Bayesian framework that incorporates real-time quality measurements to online update the RLD of MMPs. Our methodology is a generalization of an existing “QR-chain model,” which is dedicated into a similar research and application area. We conduct numerical studies to test the performance of our methodology and compare with the QR-chain model. The results show that our methodology outperforms the QR-chain model through capturing the impact of quality degradation on the process of tool wear and incorporating real-time quality measurements.

Complex systems often consist of multiple units that are required to work together in parallel to satisfy a specific engineering objective. As an example, in manufacturing processes, several identical machines may need to operate together to simultaneously fabricate the same products in order to meet the high production demand. This parallel configuration is often designed with some level of redundancy to compensate for unexpected events. In this way, when only a small portion of units fail to operate due to either unexpected machine downtime or scheduled maintenance, the remaining units can still achieve the engineering objective by increasing their workloads up to the designed capacities. However, the workload of a unit apparently impacts the unit's degradation rate as well as its failure time. Specifically, this paper considers the case that a higher workload assignment accelerates the unit's degradation and vice versa. Based on this assumption, we develop a method to actively control the degradation as well as the predicted failure time of each unit by dynamically adjusting its workloads. Our goal is to prevent the overlap of unit failures within a certain time period through taking advantage of the natural redundancy of the parallel structure, which may potentially lead to a better utilization of maintenance resources as well as a consistently ensured system throughput. A numerical study is used to evaluate the performance of the proposed method under different scenarios.

Process monitoring and quality control is essential for continuous manufacturing processes of carbon nano- tube (CNT) thin sheets or buckypaper. Raman spectroscopy is an attractive inline quality characterization and quantification tool for nanomanufacturing because of its nondestructive nature, fast data acquisition speed, and ability to provide detailed material information. However, there is signal-dependent noise buried in the Raman spectra, which reduces the signal-to-noise (S/N) ratio and affects the accuracy, efficiency, and sensitivity for Raman spectrum-based quality control approaches. In this paper, a signal analysis model with signal-dependent noise for Raman spectroscopy is developed and validated based on experimental data. The wavelet shrinkage method is used for denoising and improving the S/N ratio of raw Raman spectra. Based on the validated signal-noise relationship, a novel generalized wavelet shrinkage approach is introduced to remove noise in all wavelet coefficients by applying individual adaptive wavelet thresholds. The effectiveness of this method is demonstrated using both simulation and experimental case studies of inline Raman monitoring of continuous buckypaper manufacturing. The proposed method allows for a significant reduction of Raman data acquisition time without much loss of S/N ratio, which inherently enables Raman spectroscopy for inline monitoring and control for continuous nanomanufacturing processes.

In progressive stamping processes, condition monitoring based on tonnage signals is of great practical significance. One typical fault in progressive stamping processes is a missing part in one of the die stations due to malfunction of part transfer in the press. One challenging question is how to detect the fault due to the missing part in certain die stations as such a fault often results in die or press damage, but only provides a small change in the tonnage signals. To address this issue, this article proposes a novel automatic process monitoring method using the recurrence plot (RP) method. Along with the developed method, we also provide a detailed interpretation of the representative patterns in the recurrence plot. Then, the corresponding relationship between the RPs and the tonnage signals under different process conditions is fully investigated. To differentiate the tonnage signals under normal and faulty conditions, we adopt the recurrence quantification analysis (RQA) to characterize the critical patterns in the RPs. A parameter learning algorithm is developed to set up the appropriate parameter of the RP method for progressive stamping processes. A real case study is provided to validate our approach, and the results are compared with the existing literature to demonstrate the outperformance of this proposed monitoring method.

Due to the rapid advances in sensing and computing technology, multiple sensors have been widely used to simultaneously monitor the health status of an operation unit. This creates a data-rich environment, enabling an unprecedented opportunity to make better understanding and inference about the current and future behavior of the unit in real time. Depending on specific task requirements, a unit is often required to run under multiple operational conditions, each of which may affect the degradation path of the unit differently. Thus, two fundamental challenges remain to be solved for effective degradation modeling and prognostic analysis: 1) how to leverage the dependent information among multiple sensor signals to better understand the health condition of the unit; and 2) how to model the effects of multiple conditions on the degradation characteristics of the unit. To address these two issues, this paper develops a data fusion methodology that integrates the information from multiple sensors to construct a health index when the monitored unit runs under multiple operational conditions. Our goal is that the developed health index provides a much better characterization of the health condition of the degraded unit, and, thus, leads to a better prediction of the remaining lifetime. Unlike other existing approaches, the developed data fusion model combines the fusion procedure and the degradation modeling under different operational conditions in a unified manner. The effectiveness of the proposed method is demonstrated in a case study, which involves a degradation dataset of aircraft gas turbine engines collected from 21 sensors under six different operational conditions.

Image and video sensors are increasingly being deployed in complex systems due to the rich process information that these sensors can capture. As a result, image data play an important role in process monitoring and control in different application domains such as manufacturing processes, food industries, medical decision-making, and structural health monitoring. Existing process monitoring techniques fail to fully utilize the information of color images due to their complex data characteristics including the high-dimensionality and correlation structure (i.e., temporal, spatial and spectral correlation). This paper proposes a new image-based process monitoring approach that is capable of handling both grayscale and color images. The proposed approach models the high-dimensional structure of the image data with tensors and employs low-rank tensor decomposition techniques to extract important monitoring features monitored using multivariate control charts. In addition, this paper shows the analytical relationships between different low-rank tensor decomposition methods. The performance of the proposed method in quick detection of process changes is evaluated and compared with existing methods through extensive simulations and a case study in a steel tube manufacturing process.

Multivariate process control in Distributed Sensor Networks (DSNs) is an important and challenging topic. Although a fully deployed sensor network will minimize information loss, the associated sensing cost can be overwhelming. Many efforts have been made to investigate the optimal sensor allocation strategy for different process control applications; however, most of them assume that the sensor layout is fixed once sensors are deployed in the system. This paper proposes a novel approach to adaptively reallocate sensor resources based on online observations, which can enhance both monitoring and diagnosis capabilities. The proposed adaptive sensor allocation strategy addresses two fundamental issues: when to reallocate sensors and how to update sensor layout. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. To investigate the adaptive strategy, a Bayesian Network (BN) model is assumed available to represent the causal relationships among a set of variables. Case studies are performed on a hot forming process and a cap alignment process to illustrate the procedure and evaluate the performance of the proposed method under different fault scenarios.

Prognostics involves the effective utilization of condition or performance-based sensor signals to accurately estimate the remaining lifetime of partially degraded systems and components. The rapid development of sensor technology, has led to the use of multiple sensors to monitor the condition of an engineering system. It is therefore important to develop methodologies capable of integrating data from multiple sensors with the goal of improving the accuracy of predicting remaining lifetime. Although numerous efforts have focused on developing feature-level and decision-level fusion methodologies for prognostics, little research has targeted the development of “data-level” fusion models. In this paper, we present a methodology for constructing a composite health index for characterizing the performance of a system through the fusion of multiple degradation-based sensor data. This methodology includes data selection, data processing, and data fusion steps that lead to an improved degradation-based prognostic model. Our goal is that the composite health index provides a much better characterization of the condition of a system compared to relying solely on data from an individual sensor. Our methodology was evaluated through a case study involving a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

The relative failure frequency among different failure modes of a production system is referred to as failure profile in this paper. Identification of failure profile based on failure-time data collected in the production phase of a system can help pinpoint the bottleneck problems, and provide valuable information for system design evaluation and maintenance management. Major challenges of effective failure profile identification come from time-varying and limited failure-time data. In this paper, the failure profile is estimated by using the maximum likelihood method. In addition, statistical hypothesis testing procedures are proposed to inspect the existence of a dominating failure mode, and possible changes of failure profiles during a production period. The developed methods are illustrated with an automation system of a high throughput screening (HTS) process, and a production process for cylinder heads.

Dimensional variation propagation modeling is a critical enabling technique for product quality variation reduction in a multistage assembly process (MAP). However, the complex inter-stage correlations make the modeling extremely difficult. This paper aims to improve the existing techniques by developing a generic state space approach to modeling 3-D variation propagation induced by various types of variation sources in general MAPs. A concept of differential motion vector (DMV) is adopted to represent deviations with respect to four types of coordinate systems and to formulate the variation propagation as a series of homogeneous transformation among different coordinate systems. Based on this representation and formulation strategy, a novel generic mechanism is proposed to model the effect of variations induced by part fabrication processes and a MAP. A case study on 3-D variation propagation in a panel fitting process is presented to demonstrate the modeling and analysis capability of the proposed methodology.

Active control for dimensional variation reduction in multistage assembly processes (MAPs) is a challenging issue for quality assurance. It is desirable to implement a system-level control strategy to minimize the end-of-line product variance, which is propagated from upstream manufacturing stages. Research has been conducted to realize such objective, based on the variation propagation models derived from the nominal parameters of product and process design. However, due to the uncertainties induced by the significant changes of process parameters, such designated model will be different from that of the actual process, and will not precisely represent the actual physics of the process. This model discrepancy may lead to the performance deterioration of the controllers. This paper proposed a feed-forward MAP control strategy that explicitly takes into account the uncertainties of model coefficients. The case study demonstrates that, when the model uncertainties are significant, the controller derived from the proposed approach outperforms that derived without considering the model uncertainty.

Robust parameter design (RPD) has been widely used as a cost-effective tool in quality control to reduce variability, in which the controllable factors are set to minimize the variability of response variables due to noise factors, assuming their distributions are known. It is essentially an offline tool without considering that some noise factors can be measured online. Recently, the concept of design of experiment (DOE)-based automatic process control (APC) has been proposed for online process control based on regression models obtained from DOE and with consideration of the online measurement of noise factors. The existing literature investigates the DOE-based APC with assumption that both regression models and the online noise measurement are precisely known, which limits the applicability of the technique. This paper develops the DOE-based APC scheme that considers both the observation and the modeling uncertainties. The controller is implemented under two APC strategies, i.e., cautious control strategy and certainty equivalence control strategy. The comparison among online APC and robust design approaches demonstrates that automatic controller with consideration of both uncertainties can achieve better process performance than conventional design, and is more stable than normal DOE-based APC controllers. The proposed approach is illustrated using an industrial process.

Distributed sensing, a system-wide deployment of sensing devices, has resulted in both temporally and spatially dense data-rich environments. This new technology provides unprecedented opportunities for quality and productivity improvement. This paper discusses the state-of-the-art practice, research challenges, and future directions related to distributed sensing. The discussion includes the optimal design of distributed sensor systems, information criteria, and processing for distributed sensing and optimal decision making in distributed sensing. The discussion also provides applications based on the authors' research experiences. Note to Practitioners—This paper is based on a panel discussion on the topic of the emerging technology of distributed sensing and the associated challenges and opportunities. The panel, constituted by a group of leading researchers and practitioners with expertise in operations and statistics, convened during the Institute for Operations Research and the Management Sciences (INFORMS) 2003 annual meeting in Atlanta, GA. This panel focused its discussion on the information layer technology of distributed sensing for quality and productivity improvements, which differentiates this panel from other similar panels that were formed in a different society. The panelists provided their visions about the state-of-the-art practice, research challenges, and future research directions, and also discussed potential applications based on their own experiences.

Due to the rapid development of computer and sensing technology, many measurements of process variables are readily available in manufacturing processes. These measurements carry a large amount of information about process conditions. It is highly desirable to develop a process monitoring and diagnosis methodology that can utilize this information. In this paper, a statistical process control monitoring system is developed for a class of commonly available process measurements-cycle-based waveform signals. This system integrates the statistical process control technology and the Haar wavelet transform. With it, one can not only detect a process change, but also identify the location and estimate the magnitude of the process mean shift within the signal. A case study involving a stamping process demonstrates the effectiveness of the proposed methodology on the monitoring of the profile-type data. Note to Practitioners-Cycle-based signal refers to an analog or digital signal that is obtained through automatic sensing during each operation cycle of a manufacturing process. The cycle-based signal is very common in various manufacturing processes (e.g., forming force in stamping processes, the holding force, and the current signals in spot welding processes, the insertion force in the engine assembly process). In general, cycle-based signals contain rich process information. In this paper, cycle-based signal monitoring will be accomplished by monitoring the wavelet transformation of the signal, instead of monitoring the raw observations themselves. Further, a decision-making technique is developed using the SPC monitoring system to locate where the mean shift occurred and to estimate magnitudes of mean shifts. Thus, this paper presents a generic framework for the enhanced statistical process control technique of cycle-based signals.

Root-cause identification for quality-related problems is a key issue in quality and productivity improvement for a manufacturing process. Unfortunately, root-cause identification is also a very challenging engineering problem, particularly for a multistage manufacturing process. In this paper, root-cause identification is formulated as a problem of estimation and hypothesis testing of a general linear mixed model. First, a linear mixed fault-quality model is built to describe the cause-effect relationship between the process faults and product quality. Then, the estimation algorithms developed for a general linear mixed model are adapted to estimate the process mean and variance. Finally, a hypothesis testing method is developed to determine if process faults exist in terms of statistical significance. A detailed experimental study illustrated the effectiveness of the proposed methodology.Note to Practitioners-Economic globalization brings intense competition among manufacturing enterprises. The key to succeed in this competitive climate is to rapidly respond to fast-changing market demands with high-quality and competitively priced products. To achieve this, we need to quickly identify root causes of quality-related problems in a complicated manufacturing system. However, the current widely adopted quality-control techniques focus more on monitoring than on root-cause identification. These techniques can efficiently detect the changes in the process but the root cause identification is often left to the plant engineers or operators. In this paper, a systematic estimation and testing method is proposed to identify the variational root causes in multistage manufacturing processes. First, a linear model is built based on the design information to describe the cause-effect relationship between the process faults and product quality. Then, an algorithm is developed to estimate the mean and variance of the process faults from the quality measurements of products. Finally, a statistical testing method is developed to determine if process faults (i.e. root causes) exist in terms of statistical significance. A detailed experimental study illustrates the effectiveness of this method. The method presented in this paper is a new quality-control technique and can be used for quality improvement of multistage manufacturing processes.

In this paper, a state space model is developed to describe the dimensional variation propagation of multistage machining processes. A complicated machining system usually contains multiple stages. When the workpiece passes through multiple stages, machining errors at each stage will be accumulated and transformed onto the workpiece. Differential motion vector, a concept from the robotics field, is used in this model as the state vector to represent the geometric deviation of the workpiece. The deviation accumulation and transformation are quantitatively described by the state transition in the state space model. A systematic procedure that builds the model is presented and an experimental validation is also conducted. The validation result is satisfactory. This model has great potential to be applied to fault diagnosis and process design evaluation for complicated machining processes.

The relationship between product quality and manufacturing system component reliability is very complex in a multi-station manufacturing process (MMP). In this paper, a general system reliability model is presented to integrate the product quality and manufacturing system component reliability information. Considering the unique complex characteristics of MMPs, a new QR-Chain model is proposed to study the propagation of the interaction between manufacturing system component reliability and product quality throughout all stations. An analytical solution for the system reliability of a general MMP can be obtained based on the proposed QR-Chain model. The upper bound of the system reliability is derived, which is much easier to obtain than the exact solution.

In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, the system diagnostic performance is continuously improved as the knowledge of process faults is automatically accumulated during production. As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology.