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Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

This paper aims to provide a new dynamic modeling approach for root cause detection of the auto-body assembly variation.

The dynamic characteristics, such as fixture element wear and quality of incoming parts, are considered in assembly variation modeling with the dynamic Bayesian network. Based on the network structure mapping, the parameter learning of different types of nodes is conducted by integrating process knowledge and Monte Carlo simulation. The inference was that both the measurement data and maintenance actions are evidence for the improvement of diagnosis accuracy.

The proposed assembly variation model which has incorporated dynamic manufacturing features could be used to detect multiple process faults effectively.

A dynamic variation modeling method is proposed. This method could be used to provide more accurate diagnosis results and preventive maintenance guidelines for the assembly process.

The purpose of this paper is to focus on optimal sensor placement for the fixture variation diagnosis of compliant sheet metal assembly process. Fixture variations are the main sources for complex automotive body dimensional failures. An effective measurement strategy can help exactly and timely diagnose these fixture variations. Research on sensor placement strategy of compliant sheet metal assembly process is not much stated formerly.

The impact principle of fixture variations is analyzed to set up the relationship between the assembly variation and fixture variations applying the method of influence coefficients and the effective independence (EI) method is used to find the optimal sensor positions based on the impact principle analysis of fixture variations.

The obtained fixture variation sensitivity matrix describes the influence of fixture variations to compliant sheet metal assembly variation and can be used for diagnosing fixture variations. The EI method can effectively solve the optimal sensor positions for compliant sheet metal assembly by a case demonstration.

The proposed method can solve the sensor placement of online assembly station for diagnosing fixture variations. It takes the compliant characteristics of sheet metal parts into account and the sensor information has much greater diagnosability than that from applying other methods.

Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of compliant sheet metal parts for the fixture fault diagnosis.

Based on the initial sensor locations and measurement data in launch time of the assembly process, the Bayesian network approach for fixture fault diagnosis is proposed to construct the diagnostic model. Furthermore, given the desired number of sensors, the diagnostic ability of the sensor set is evaluated based on the mutual information of the nodes. Thereby, a new sensor placement method is put forward and validated with a real automotive sheet metal part.

The new proposed method can be used to perform the fixture fault diagnosis and sensor placement optimization effectively, especially in a data‐rich environment. And it is robust in the presence of measurement noise.

This paper presents a novel approach for fixture fault diagnosis and optimal sensor placement in the assembly process.

The purpose of this paper is to summarize the status and characteristics of rail technology of high-speed railway in China, and point out the development direction of rail technology of high-speed railway.

This study reviews the evolution of high-speed rail standards in China, comparing their chemical composition, mechanical attributes and geometric specifications with EN standards. It delves into the status of rail production technology, shifts in key performance indicators and the quality characteristics of rails. The analysis further examines the interplay between wheels and rails, the implementation of grinding technology and the techniques for inspecting rail service conditions. It encapsulates the salient features of rail operation and maintenance within the high-speed railway ecosystem. The paper concludes with an insightful prognosis of high-speed railway technology development in China.

The rail standards of high-speed railway in China are scientific and advanced, highly operational and in line with international standards. The quality and performance of rail in China have reached the world’s advanced level. The 60N profile guarantees the operation quality of wheel–rail interaction effectively. The rail grinding technology system scientifically guarantees the long-term good service performance of the rail. The rail service state detection technology is scientific and efficient. The rail technology will take “more intelligent” and “higher speed” as the development direction to meet the future needs of high-speed railway in China.

The development direction of rail technology for high-speed railway in China is defined, which will promote the continuous innovation and breakthrough of rail technology.

This paper aims to explore the mechanism of influence on the subordinate's sense of gain at work (SGW) in terms of the coaching leadership behavior (CL), supervisor-subordinate guanxi (SSG) and commitment-based practice of human resource management (CHRM).

Based on the survey of 584 employees from 50 firms operating in China, this study explores the effect of CL on employees’ SGW, which concerns two dimensions: sense of material gain and sense of spiritual gain.

Results show that the CL has a significant positive influence on both the subordinate’s sense of material gain and his/her sense of spiritual gain, in which there exists a mediating effect of SSG and moderating effects of CHRM for the influence.

This study not only develops the theory of SGW, but also provides a scientific basis and policy suggestions for employers to implement in order to enhance their employees’ SGW.

Few integrative studies have examined the impact of CL on employees’ SGW. Based on the Need-to-Belong Theory, this study adds new empirical evidence and constructs a theoretical model for the mechanism of influence on the SGW, examines the influence of CL on the subordinate’s SGW and finds a mechanism of transmission (SSG) and a boundary condition (CHRM) for the influence.

The purpose of this study is to explore the influence of media use on environmental protection behaviors among college students.

A total of 182 college students from H universities, including science, engineering, liberal arts and art, were surveyed. This study used structural equation modeling (SEM) to analyze the data based on the proposed model, including media use, environmental protection awareness, environmental protection intentions and environmental protection behaviors, and then analyzed the impact of different gender, grade and disciplines on college students' environmental protection behavior by T-test and ANOVA.

First, it can be seen from the structural equation model analysis that media use has a significant impact on environmental protection awareness and environmental protection intention of college students, while media use has no significant direct impact on their environmental protection behaviors, but environmental protection intention has a mediating effect between media use and environmental protection behaviors. Second, the analysis of control variables explained that gender and disciplines have no significant impact on college students' environmental protection behaviors, while grades do.

Research on media use on environmental protection behavior was limited. This study proposed a new model of the impact of media use on environmental protection behavior and deeply analyzed the effect of media use from the perspective of environmental protection behavior. In addition, this study put forward research suggestions from the perspective of media publicity and environmental protection, hoping that the research conclusions could provide a basis and reference for ecological environment construction.