In this article, a method of hybrid convolutional neural networks (CNNs) is proposed for powder-bed fusion (PBF) process monitoring. The proposed method can learn both the spatial and temporal representative features from the raw images automatically based on the advantages of the CNN architecture. The results demonstrate the superior performance of the proposed method compared with the traditional methods with handcrafted features. The overall detection accuracy of four process conditions, e.g., overheating, normal, irregularity, and balling, can be up to 0.997. In addition, it is found that the temporal information for PBF process monitoring by the vision detection of the process zone (including melt pool, plume, and spatters) is significant. As the proposed method can save image processing steps, it simplifies the procedure on feature extraction. This makes it more suitable for online monitoring applications.

In advanced manufacturing, tool condition monitoring is essential to improve the productivity of the manufacturing process. However, single model prediction is not satisfactory due to the complicated machining process that resulted in varying degradation of the tools. A multi-stage tool condition monitoring framework using gated recurrent unit network (Multi-GRU) is proposed in this paper to address the issue. A G RU classifier is first used to estimate the tool condition stage. Then different models are trained based on each stage to estimate the tool condition. A gun-drilling experiment under different cutting conditions is carried out to verify the efficiency of the proposed framework. The Multi-GRU framework is tested using extracted features for both tool state classification and tool wear estimation tasks. The experimental result shows that the Multi-GRU framework outperforms conventional single GRU model in the terms of overall accuracy in both classification and regression tasks. Besides, the experimental result also demonstrated that, with large enough classes, the classification results can approximate the tool wear with good accuracy.

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on the training data that presents an effective approach to incorporating the evaluation measure (i.e., G-mean) into the objective function. We first optimize the misclassification costs, and then apply them to DBN. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state of the art on both benchmark data sets and real-world data set for fault diagnosis in tool condition monitoring.

Tool condition monitoring (TCM) is a prerequisite to ensure high finishing quality of workpiece in manufacturing automation. One of the most important components in TCM system is tool wear estimation. How to achieve estimation with high accuracy is still an open question. In the past few decades, recurrent neural network (RNN) has shown a great success in learning long-term dependence of the sequential data. However, traditional RNNs (e.g., vanilla RNN, etc.) suffer gradient vanishing or exploding problem as well as long computational training time when the model is trained through back propagation through time (BPTT). To address these issues, we propose a gated recurrent units (GRU) based neural network to estimate the tool wear for tool condition monitoring. The GRU neural network can analyze time-series data on multiple time scales and can avoid gradient vanishing during training. A real-world gun drilling experimental dataset is used as a case study for tool condition monitoring in this paper. The performance of the proposed GRU based TCM approach is compared with other well-known models including support vector regression (SVR) and multi-layer perceptron (MLP). The experimental results show that the proposed GRU based TCM approach outperforms other competing models on this real-world gun drilling dataset.

Tool Condition Monitoring (TCM) is an important topic in manufacturing industry, which improves product quality, production efficiency, reduces costs and downtime. This paper develops a new data-driven framework for estimating tool remaining useful life (RUL) in TCM. The framework includes the following modular components: data preprocessing with a proposed adaptive Baysian change point detection (ABCPD) for automatic data alignment, time window process, feature extraction, feature selection and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on a real-world gun drilling experimental dataset with multiple sensor measurements (i.e. thrust force, torque, 12 vibration signals). Different model selection, sensor selection, feature selection methods have been investigated in this paper. The simulation performance of the proposed framework is studied with the gun drilling dataset and it has been shown that the proposed framework has good performance.

Biological inspired control approaches based on central pattern generator (CPG) have been used to generate human-like rhythmic locomotion for bipedal robots. CPG consists of several oscillators with coupled mutual inhibition. In the application of CPG to bipedal walking, one of the important problem is how to coordinate oscillators so as to achieve stable walking, since without proper coordination the rhythmic trajectory generated by CPG may fail to control the walking. To solve this problem, this paper presents a method of coordination between two oscillators using phase information. In the method, approximated phase values of the oscillators are derived and used as the feedback to coordinate two oscillators. Furthermore, coordination between multiple oscillators with different frequencies and phases has also been explored. This method is verified with a 2D robust walking controlled by four oscillators. Several walking scenarios are tested: adding external force, change walking frequency and step length during walking. Robust walking is achieved in our simulation.

Due to the extensive utilization in engineering designs, various vibration controller designs have been investigated to meet the design specifications. However, not all of them exactly meet the design requirements. In this paper, mechatronic design quotient (MDQ) approach and genetic algorithm are coupled together to perform this evaluation and optimization task. MDQ is presented to formulate an evaluation function of passive vibration controller design for flexible beam structures, and GA is then used to optimize this function so as to achieve a design solution with the highest MDQ value. Experimental results from one damper and two dampers are presented and compared. It showed that the linear dampers design with the proposed method can achieve the desired performance.

In this paper, an approach using the Taylor series expansion is explored for error compensation in bi-axis CNC machines. The objective is to improve the contouring accuracy in these machines. The contour errors that will occur if the desired reference inputs are directly applied to the control loops are first predicted using the known axial dynamic models. The Taylor series expansion is then used to obtain an expression of the contour error around the current tool position. A robust numerical method is devised which is then used to compute the modified reference components along individual axes for cancellation of the predicted contour errors. Simulation results show that, with perfect knowledge of the machine's dynamic models, the proposed strategy can perfectly cancel out the contour errors for both linear and circular contours despite the mismatches in the axes dynamics.

This paper presents a model-based monitoring and failure detection approach in ball-nose end milling process. A mechanistic force model has been established for high speed milling on hardened stavax steel with 6 mm micro-grain tungsten carbide 2 flute ball-nose end mill. The threshold curve can be obtained off-line based on the process model as the cutting engagement conditions along the tool path are determined at the simulation stage. The measured cutting forces are monitored on-line to detect the faults by comparing them with the threshold curve at machining stage. If a fault is detected at certain position along the tool path, an intelligent predictive method is utilized to predict whether this fault will result in catastrophic failure. Experimental results are provided to demonstrate the feasibility of this approach.

This paper presents an attractive position-based visual servoing approach for camera-in-hand robotic systems. The major contribution of this work is in devising an elegant and pragmatic approach for 3D visual tracking. The proposed modified Smith predictor (MSP)-DeMenthon-Horaud (DH) visual servoing system has shown to be reliable and yielded good target tracking performance. It differ from the other techniques found in the vast visual servoing literature, in its approach for image interpretation and the introduction of a Smith-like predictor control structure to overcome the inherent vision delay. A complete description will be made on the MSP-DH visual servoing system. Experiments on the target tracking performance on AY planar motion using an AdeptOne robotic system are presented to illustrate the controller performance. Also, experimental results clearly showed the capability of the MSP-DH visual servoing system in performing 3D-dynamic visual servoing.

This paper proposes an enhanced sliding mode controller for the set point tracking control, which combine the pre-shape sliding mode control method with motion auxiliary design technique. Using the proposed pre-shape sliding mode controller, not only the robust property of sliding mode controller can be improved, but also the system trajectory can be designed. When comparing with the traditional control method, the proposed algorithm can give a better response with improved robust property. Simulation results for a single link robot manipulator also verify that the proposed controller gives a more attractive performance than the traditional PD computed torque controller.

The nonlinear and cross-coupled dynamics of motor-manipulator systems is studied, and a control strategy is developed for achieving ease of controller design and high control performance. By using the algorithm developed in this paper, not only can the highly complicated motor-manipulator dynamics be effectively controlled, but the motors are also made "sensible" for sensing and compensating for the uncertain dynamics acting at manipulator joints. Furthermore, as a significant result, the modeling difficulty and control complexity of the manipulator dynamics can be significantly reduced.