Liang Guo

To achieve automatic online monitoring of foam content in wellhead pipelines during the foam drainage gas production of shale gas, a cloud-based monitoring system for the foam content at the wellhead of foam drainage gas production is proposed. The system mainly comprises a terminal foam content detection sensor and a cloud platform, which relies on a wireless network for data communication. In this system, the foam content detection sensor uses the absorption of infrared light by the foam in the pipeline as a basic detection principle. Furthermore, the sensor uses signal-processing circuits to convert infrared light of different intensities into model voltage signals of different sizes and uses LoRa wireless networks to transfer the measurement data to a server for storage in a time-series database while displaying real-time foam content data on a web page. The monitoring system was tested in a field, and the relationship model between the foam content and the detection voltage signal was fitted based on the test data. Based on this model, and then tested and compared with the manual sampling results, the results of the 40 data samples tested showed that the monitoring system was effective and met the field working conditions. The foam content calculated by the fitted model compared with the manually measured foam content showed an error range of 0.11%–15.37%. The test results show that the system has functional requirements to monitor foam content in the field, contributing to the automation, networking, and cloud-based monitoring of the foam content at the wellhead.

Cloud manufacturing (CMfg) is a new manufacturing mode formed by the integration of information technology and communication technology with manufacturing. As a core role in CMfg, the CMfg platform is responsible for decomposing a large number of tasks from demander and allocating them to available services. The scheduling requires comprehensive consideration of the relevance, complexity and dynamics of task and service. When the decomposable task is multi-composite, how to allocate the optimum services to multi-composite tasks is a tricky and important problem. To solve the issue, a hierarchical scheduling model for multi-composite tasks is proposed, which is divided into user-level scheduling and sublevel scheduling to reduce the scale and difficulty of scheduling. User-level scheduling achieves two-way matching between demander and provider based on various attributes. For the sublevel scheduling, an improved firefly genetic algorithm is created for multi-objective optimisation. A detailed analysis of the hierarchical scheduling strategy is performed by testing several different instances. Experimental results indicate that this strategy reduces the complexity than collective scheduling; and has a better comprehensive balance effect on multiple optimisation goals than sequential scheduling.

Automatic process decision-making is a key module in intelligent process design(IPD), which determines the intelligence degree of IPD and affects the quality of product design. The traditional process decision-making method fails to solve the problem of knowledge expression, especially the integration of enterprise manufacturing resources and process knowledge. What’s more, heterogeneous knowledge also leads to the application of traditional knowledge mainly in keyword retrieval. So the process reasoning is mainly applied to the feature level, but the reasoning ability for the part level is weak. To overcome the above problems, the Knowledge Graph(KG) is introduced into the automatic machining process decision-making system. Firstly, a three-level information model is built to reorganize part information, process knowledge, and equipment resources based on KG. Secondly, the process reasoning framework based on KG is established, which is composed of process knowledge graph(PKG) information and process reasoning algorithm. Thirdly, to integrate process reasoning based on PKG, a hybrid reasoning algorithm based on semantic analysis(SA) and attributes weighting(AW) is built, which solved the problem of heterogeneity among process knowledge when making decisions. Finally, a prototype system was developed, and the aero-engine cone gear axis was tested to verify the effectiveness of the proposed system.

The implementation of automatic feature recognition (AFR) techniques is considered an indispensable concept in transferring product data between computer-aided design (CAD) and computer-aided process planning (CAPP). Different AFR techniques and systems have been developed to serve this aim; however, each of them have limitations. The main research gap is that each system is restricted to a specific set of predefined manufacturing features, which makes the universality of these methods difficult to extended. To solve this problem, a new hybrid 3D feature recognition method (graph and rule based) is proposed for recognizing machining features, and shaft parts are taken as an example in this paper. First, the reverse modeling method is used to classify the machining features in the part design process. Second, the 3D model is represented by B-Rep, and the weighted attribute adjacency matrix (WAAM) is proposed to represent the data structure of the B-Rep model. Third, the recognition and suppression rules are defined. Finally, three typical shaft parts are used as the test cases in MATLAB. The test results show hybrid feature recognition method can recognize all features. The comparative test shows that the practicability and efficiency of the method are satisfactory.

Motorized spindle system is one of the crucial components affecting the machine tools energy performance. Many previous studies have examined its energy optimization problems, however, most such studies focused mainly on parameters optimization to improve material removal energy efficiency or reduce total energy consumption. A missing research area is energy optimization problem considering thermal stability and productivity constraints simultaneously. Against this background, an energy optimization approach of motorized spindle system is presented with consideration of thermal stability and productivity adequately, with the goal of maximization of energy efficiency and material removal rate, and minimization of spindle average temperature which is closely associated with thermal stability. Firstly, the energy characteristics of motorized spindle and its cooling system are mathematically modelled. Then, a multi-objective optimization model is established to take the maximum energy efficiency, minimum spindle average temperature, and maximum material removal rate as objectives. The optimal solution is obtained by solving the proposed optimization model with the Non-dominated Sorted Genetic Algorithm-II (NSGA-II). Finally, a case study is introduced to validate the proposed method and the results indicate that the proposed method is more effective to find optimal decision variables for balancing the considered objectives compared with the existing optimization method.

Cloud manufacturing (CM) is a new service-oriented networked manufacturing mode. The optimal configuration of manufacturing services is one of most challenging topics in CM. Most research focuses on service composition optimization algorithms. However, for different manufacturing tasks, the configuration mode of the manufacturing services is different. For complex parts, effectively using the appropriate optimization strategy to solve the optimization of machining services is still rare in CM. To solve the above problem, a new machining task decomposition and service optimization strategy is proposed. Under this mode, the features of the complex part are defined as the basic task granularity. Four machining service optimization modes are constructed, and a mathematical model of machining service optimization under the four modes is established. Subsequently, a particle swarm optimization algorithm based on simulated annealing (PSOBSA) is designed by combining the particle swarm optimization (PSO) and simulated annealing (SA). Finally, three groups of simulation experiments are conducted to simulate the optimization mode of complex parts machining services based on feature decomposition. The simulation results demonstrate the feasibility of the service optimization mode and the effectiveness of the PSOBSA. The research results presented in this paper provide an machining service outsourcing method for complex parts.