Laser metal deposition (LMD) is an additive manufacturing method for metal parts by using focused thermal energy to fuse materials as they are deposited. During LMD, transient thermal signatures such as the in-situ thermal images of melt pool, contain rich information about process performance. Early prediction of such transient thermal signatures provides opportunities for process monitoring and defect prevention. While physics-based models of LMD have been conventionally used for thermal signature prediction, they have limitations and are computationally expensive for real-time prediction. A scalable, efficient data-science-based model is therefore needed. This paper develops a deep-learning-based surrogate model, called LMD-cGAN, to predict and emulate the transient thermal signatures in LMD. The model generates images for the thermal dynamics of melt pool conditionally on the deposition layer. It enables early prediction of future-layer thermal signatures for an in-process part based on its early-layer thermal signatures. To respect the physics in LMD, a physics-guided image selection (PGIS) mechanism is integrated with LMD-cGAN to calibrate the predictions against physical benchmarks of transient melt pool for the process. The effectiveness and efficiency of the proposed method are demonstrated in a case study on the LMD of Ti-4Al-6V thin-walled structures. Note to Practitioners—With online sensing, many LMD applications have real-time process data that convey valuable information about the process status and part quality. The proposed method leverages these data for thermal signature prediction. LMD-cGAN is a deep-learning-based surrogate model that learns the population profile of real thermal signatures and generates thermal signatures from there. The proposed PGIS mechanism in LMD-cGAN ensures the physical validity of these predictions by benchmarking them against physical insights about the process. LMD-cGAN can be applied to predict thermal signatures in future layers based on early-layer thermal signatures of an in-process part (an implicit assumption here is that the in-process part to be predicted for is the same type). LMD-cGAN can also be applied to emulate thermal signatures in specific layers. To generate thermal signatures for generic, non-defect parts, the training data should be selected with caution – the part where the data were collected should have no obvious defects, so the thermal signatures generated by LMD-cGAN show the regular thermal dynamics. Compared with pure physical models, the proposed method incorporates process uncertainties captured from the early-layer data, hence “on-the-fly” emulation of the melt pool, while characterizing the inherent relationship between the LMD process and thermal signatures.

Multivariate time series (MTS) arise due to multisensor data collection in manufacturing. These data are complex in the sense that attributes have a varying scale, volitivity, continuity, and so on, and interattribute dependence also appears, which can mask the inherent information about system health status. Conventional machine learning-based process monitoring techniques are inefficient in predicting faults with MTS—their detection capability heavily relies on the input features, yet the classification power of MTS attributes is weakened by the complexity of multisensor data. Effective feature extraction is, therefore, necessary to facilitate fault prediction with MTS. This study proposes a fault prediction framework for MTS based on bag-of-words (BOW) feature extraction, statistical feature selection, and classification analysis. BOW models are for the first time adopted in a manufacturing context. Their superior capability in information preservation, local pattern recognition, and temporal effect accommodation has overcome the major limitations in current manufacturing practices with MTS. A comprehensive case study demonstrates the desirable performance of this framework on two MTS data sets from paper manufacturing and automotive manufacturing, as well as its superiority over conventional machine learning-based fault prediction.Note to Practitioners—Process monitoring in a multisensor environment has been a vital interest in manufacturing. The difficulty lies in the lack of detection power in many conventional techniques, e.g., control chart and logistic regression. A critical reason for such failure is the neglect of time effect in MTS—patterns associated with system faults tend to stretch a period, but a conventional control chart or classifier inspects each time stamp separately. Fault detection based on time series sequences is, therefore, essential. However, how to effectively extract features from MTS becomes a challenge. The framework proposed in this study adopts BOW models, specifically symbolic aggregate approximation (SAX), to extract features from MTS sequences, thus substantially improves the detection power against local patterns over time. Many manufacturing multisensor data are subject to such local patterns that point to the root cause of fault, so the proposed framework has a wide application in manufacturing.

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. The architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method. Note to Practitioners—This article was motivated by the problem of detecting objects such as weld nugget or metal piece in infrared (IR) imaging of thermomechanical processes (TMPs) in automotive manufacturing. The method is applicable to in situ IR images or videos that contain one or more objects to be detected. It only requires that the data are in image form and come from TMPs. Currently, there is no existing deep learning (DL)-based method for generic object detection (OD) in unlabeled IR images from TMPs. The proposed method takes advantages of the recent advancement in DL. This article suggests a systematic approach to build a DL-based OD model, named Unsupervised IR Image Net (UIR-Net), to extract objects from raw IR images collected for TMPs. A step-by-step procedure is given in this article to guide users through label generation, data quality evaluation, and model training to establish the proposed UIR-Net model. Results from resistance spot welding and hot stamping suggest that this approach is feasible and effective. It is one of the few generic OD works designed for manufacturing applications. Simple implementation, feasibility, and effectiveness make this method a suitable candidate for online data analytics and process monitoring in a wide range of manufacturing applications.

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations.

This article develops a novel in situ nondestructive quality evaluation for friction stir blind riveting in joining lightweight materials. This method is able to solve the small sample size problem that is commonly occurred in manufacturing experiments. The proposed method achieves an optimal integration of the tensor decomposition and ensemble learning by utilizing the mutual benefits. On the one hand, diversified feature matrices are extracted via tensor decomposition to maximize the ensemble learning performance. On the other hand, regularized tensor decomposition results deviate with different regularization parameter values and ensemble learning is able to determine the optimal parameter value via a heuristic algorithm, which stabilizes the tensor decomposition results. This optimal integration is built by developing a novel diversity-based feature generation and selection approach: 1) a diversity measure is defined to evaluate the extracted features; 2) a heuristic adaptive algorithm is developed with ensemble learning to determine the optimal regularization parameter for integration; and 3) the optimal features are selected via clustering to maximize the diversity measure, which is expected to strengthen ensemble learning performance for better evaluation results. Numerical studies and case studies are performed to demonstrate the effectiveness of the proposed method as well as its superiority over the existing methods.