Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.

In Industry 4.0, the production planning and execution of smart factories (SFs) full of continuously delivered small-batch orders become dynamic and complicated. Traditional centralised manufacture planning is difficult to handle unexpected disturbances. With the aid of new information technologies, resources in SFs become smart and connected to make autonomous decisions. This paper tries to release intelligence of smart connected resources to allocate production tasks and logistics tasks in SFs coordinately and autonomously. The architecture is modelled as an autonomous decision-making manufacturing system with IIoT support, which aims to synchronously allocate manufacturing tasks by the bidding of resources in SFs. Then, a dynamic production-logistics-integrated tasks allocation model is built. The orders makespan and resources utilisation are considered as the objective function, and production resources and logistics resources are integrated to autonomously communicate and interact with each other to bid for dynamic production-logistics integrated operations. To figure out, a reinforcement learning (RL) algorithm is studied, which makes operations decisions for each job step by step based on in-situ data during manufacturing process. Finally, a demonstrative case showed that compared to centralised scheduling system, the RL-based model performs better in handling production-logistics-integrated tasks allocation problem in SFs full of dynamic and small-batch individualised orders.

Five-axis flank milling is prevalent in complex surfaces manufacturing, and it typically consumes high electricity energy. To save energy and improve energy efficiency, this paper proposes a tool path optimisation of five-axis flank milling by meta-reinforcement learning. Firstly, considering flank milling features, a feed angle is defined that guides tool spatial motion and identifies an ideal principal path. Then, machining energy consumption and time are modelled by tool path variables, i.e., feed angle, cutting strip width and path length. Secondly, an energy-efficient tool path dynamic optimisation model is constructed, which is then described by multiple Markov Decision Processes (MDPs). Thirdly, meta-learning integrating with the Soft Actor-Critic (MSAC) framework is utilised to address the MDPs. In an MDP with one principal path randomly generated by a feed angle, cutting strip width is dynamically optimised under a maximum scallop height limit to realise energy-efficient multi-expansions. By quick traversal of MDPs with various feed angles, MSAC enables an energy-efficient path generation and expansion integrated scheme. Experiments show that, regarding machining energy consumption and time, the proposed method achieves a reduction of 69.96% and 68.44% over the end milling with an iso-scallop height, and of 41.50% and 39.80% over the flank milling with an iso-scallop height, with a minimum amount of machining carbon emission, which highlights its contribution to the arena of energy-oriented and sustainable intelligent manufacturing.