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Geopolymer, as a new type of solid waste-based inorganic cementitious material, exhibits outstanding behavior in terms of physical and chemical performance, macromechanical properties, long-lasting stability, and features potential application development tendency in the field of repair and reinforcement of existing concrete structures. This paper investigated the interfacial behavior of geopolymer mortar with OPC concrete substrate under different slag, fly ash and red mud mixing proportions, while cement mortar was used as a control group for the research. The interfacial bonding properties of the geopolymer mortar to the OPC concrete substrate were elaborated by carrying out split tensile test, double-sided shear test, and three-point bending test. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) were employed to further analyze the microstructural characteristics and physical phase components of the interfacial transition zone between the geopolymer mortar and the OPC concrete substrate. The results indicated that the compressive strength of slag-fly ash-red mud-based geopolymer mortar under different mixing ratio conditions was consistently superior to that of cement mortar, where the optimum mixing ratio for the geopolymer mortar was S33F33R33. Overall, the interfacial bonding properties of the geopolymer mortar to the OPC concrete substrate gradually increased with the increment of the slag content, however, an evolutionary trend of minor enhancement followed by a gradual reduction was observed with the growth of the fly ash and red mud content.

Self-compacting concrete (SCC) mixtures requires a large amount of Portland cement and thus resulting in high CO2 emissions, expanding its application has been in question to environment sustainability. Accordingly, environmental-friendly alternatives to Portland cement in SCC mixtures are deemed necessary for the progress of SCC in construction. In this investigation, treated jute fibers and mineral powders were used to replace cement in SCC mixtures. The effects of fibers and powder contents, fiber treatment, and mineral powder types on the workability and mechanical properties of SCC mixtures were investigated through slump-flow, compressive, and flexural tests. A microstructure was conducted through scanning electron microscopy. It was concluded that mineral powders and jute fibers could significantly improve the workability and mechanical properties of SCC. The microstructure observations demonstrated that the proposed methods are capable of enhancing the mechanical properties of SCC with an optimum fiber content. Specifically, a combination of 0.1% jute fibers and 75 μm zeolite powders were found to achieve the best mechanical properties. The relevant results supply fundamental reference and background for artificial intelligence and multi-function development for future infrastructure.

The glass fiber-reinforced polymer (GFRP) rebar reinforced cemented soil is widely employed to solve the weak foundation problem led by sludge particularly. The robustness of this structure is highly dependent on the interface bond strength between the GFRP tendon and cemented soils. However, its application is obstructed owing to the deficient studies on the influence factors. Therefore, this study investigates the effects of water content (Cw: 50%–90%), cement proportion (Cc: 6%–30%), and curing period (Tc: 28–90 days) on peak and residual interface bond strengths (Tp and Tt), as well as the unconfined compression strength (UCS). Results indicated that mechanical properties were positively responded to Tc and Cc, while negatively correlated to Cw. Besides, Random Forest (RF), one of the machine learning (ML) models, was developed with its hyperparameters tuned by the firefly algorithm (FA) based on the experimental dataset. The pullout strength was predicted by the ML model for the first time. High correlation coefficients and low root-mean-square errors verified the accuracy of established RF-FA models in this study. Subsequently, a coFA-based multi-objective optimisation firefly algorithm (MOFA) was introduced to optimise tri-objectives between UCS, Tp (or Tt), and cost. The Pareto fronts were successfully acquired for optimal mixture designs, which contributes to the application of GFRP tendon reinforced cemented soil in practice. In addition, the sensitivity of input variables was evaluated and ranked.

Sulfate attack is one of the non-negligible factors that induces deterioration in the performance and life cycle of soil stabilizers. In this paper, the degradation mechanism of the durability of slag–fly-ash-based geopolymer stabilized soft soil (hitherto referred to as SF-GSSS) under the sodium sulfate (Na2SO4) and magnesium sulfate (MgSO4) attack environment is comparatively investigated, and the slag/fly ash ratios are set to S10F0, S9F1, S8F2, and S7F3. The SF-GSSS was fully immersed in a 2.5% Na2SO4 solution and 2.5% MgSO4 solution, respectively, to characterize the deterioration rules via visual observations, an unconfined compressive strength (UCS) test, and by mass change. The effect of sulfate on the microstructural characteristics of the SF-GSSS were determined by different microanalytical means, such as by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results showed that the SF-GSSS immersed in a MgSO4 solution displayed significant physical deterioration, but not when in a Na2SO4 solution. The mass growth of the SF-GSSS when immersed in a Na2SO4 solution was significantly lower than when it was immersed in a MgSO4 solution at the same immersion age. The rate of strength loss was lowest for S9F1 and highest for S7F3 at the end of immersion, regardless of its immersion in Na2SO4 or MgSO4 solutions.

Identification of failure modes is a vital work in the process of failure mode and effect analysis (FMEA). Since the traditional failure mode is defined as a specific function failure or a part failure, there are uncountable failure modes for mechanical products. Then, it is too hard to list or predict all of the potential failure modes when we do FMEA on a machine. Besides, the same failure can be defined as a failure mode or a failure cause casually, which makes the analysis work stuck. To solve these problems, we propose a meta-action failure mode in this paper. A machine is decomposed into meta-action units based on the meta-action concept, and the abnormal motion performance of the unit is defined as the meta-action failure mode. All the potential failure modes of meta-actions and the expressions are given in theory. To verify the practicality and advantages of the proposed method, an illustrative example is provided. The results show that the meta-action failure mode can overcome these problems in traditional failure modes and simplify the FMEA process.

Dynamic characteristics have an important impact on the reliability of computer numerical control (CNC) machine tool. As the basis of dynamic characteristics analysis, the modal analysis should be conducted. However, the current modal analysis methods have some shortcomings. For example, the selected research object cannot reflect the dynamic characteristics of the CNC machine tool, the selection of research object lacks a scientific basis, and the influence of coupling factors is also often ignored. To address these problems, the concepts of meta-action failure mode and the latest research results about meta-action were given herein. Meta-action unit is the smallest structural unit to ensure the normal operation of the machine tool, and it can fully reflect the dynamic characteristics of a machine tool, so it is taken as the research object. In order to reduce the blindness of selecting the analytic object when implementing the reliability improvement measures, we put forward the concept of a key meta-action unit and gave the corresponding extraction method. Meanwhile, electromechanical coupling was considered when built the dynamic model of a key meta-action unit. A CNC machine tool made in China was taken as an example, and a key meta-action unit was extracted and analyzed, then the corresponding reliability improvement measures were also given. The results verify the applicability and effectiveness of this method. The proposed method can solve the shortcomings of the current methods, which lays a foundation for further research of mechanical properties and reliability based on machine tool meta-action unit.

The assembly quality of the mechanical product has a significant effect on its performance. The assembly process of the mechanical product is analyzed and optimized in this paper, then the goal of the product performance improvement is achieved by using the minimum cost. Firstly, decomposing the structure of the mechanical system by the “Function-Motion-Action” (FMA) decomposition method and the meta-actions are obtained. Then, the reason of the mechanical system performance failure is analyzed at the meta-action level and a modular fault tree model is established. Secondly, all BEs and MCSs that leading to the mechanical system performance failure are obtained through FTA, and an optimization model that considering mechanical system assembly performance index and cost is established combining cost-failure rate function relationship. Finally, using the optimization algorithm to solve the problem, and the optimal performance improvement strategy under a given performance index is obtained. The proposed method is used to optimize and analyze the performance improvement strategy of the assembly process of the computer numerical control (CNC) machine tool turntable system to obtain the optimal performance improvement strategy. The goal of improving the assembly quality of mechanical products can be achieved at the lowest cost by the optimal strategy. The method can effectively guide the production process of the mechanical product, save the produce cost and improve the assembly quality.

Cascading faults of computer numerical control machine tools will lead to frequent machine faults, but an effective fault analysis and control strategy can significantly reduce the frequency of such cascading faults. The numerous studies have focused on faulty parts using established fault diagnosis models centered on faulty parts and developed measures for controlling machine tool reliability from the parts' perspective. However, the internal parts of machine tools are strongly coupled, and when one part or assembly begins operating abnormally, it can easily initiate the abnormal operation of other parts or the entire assembly, which can lead to failures. Thus, a fault diagnosis method that considers only individual faulty parts ignores the problem of fault propagation between parts, which may lead to failure to locate the root cause. To address this problem, this paper proposes a cascading fault analysis and control strategy based on meta action. First, the functional components are decomposed into meta actions using the “function-motion-action” principle. Second, the directed edges and hierarchical digraph of meta action fault propagation are obtained. Third, the PageRank algorithm is used to calculate the fault propagation impact degree of each directed edge, and the key paths of meta action fault propagation are extracted based on the hierarchical digraph and the impact degree. Finally, the fault tree analysis method is applied to analyze the meta action on key paths, and feasible machine control measures are developed. The feasibility and effectiveness of the method are demonstrated using an example machining center.

For many enterprises, great importance is attached to the quality control of the mechatronic product, which spends a lot of effort. Quality Function Deployment (QFD) is widely used to ensure the quality of mechatronic product and the first step is transforming customer demands to engineering characteristics. However, there are numerous engineering characteristics for a complex mechatronic product. To analyze all the characteristics will lead to unnecessary waste and inefficiency. Also, the quality is mainly ensured by the key characteristics, then these characteristics should be concentrated on. There is a lack of objective and effective technology to recognize the key engineering characteristics of the mechatronic product. This paper focuses on determining the key engineering characteristics of mechatronic product. A novel method based on the theory of QFD is therefore proposed to guarantee the quality of mechatronic product efficiently. First, it is necessary to obtain an accurate weight of customer demands. In this paper, the Kano model and vague set are combined to process customer demands for mechatronic product, which not only considers user evaluating information, but also the influencing degree to user satisfaction. Second, in order to get the objective and the precise weight of engineering characteristic, the Radial Basis Function (RBF) neural network is used to establish the relationship between customer demands and the engineering characteristics of the mechatronic product. Finally, the Pareto diagram of importance degree of engineering characteristic is constructed to obtain the key engineering characteristics of the mechatronic product. Numerical control (NC) machine tool is taken as an example and the validity and scientificity of the method are verified. The method provides a new mentality for the quality control of the mechatronic product and has important theoretical significance and broad application prospect.

The fastener failures at different parts have diverse influences on the outputs of rotor systems. However, little research about this has been reported yet. Thus, the influences of various fastener failures on rotor outputs are investigated in this paper. According to the different failure behaviors, we divide the fasteners into three types. Considering that the immediate result of a fastener failure is to cause the fastened parts loosening, the looseness is considered as the fastener failure mode. The failure models of the three kinds of fasteners are established by the lumped mass method. By the numerical simulation, the outputs of the system under different failures are acquired. Moreover, to reveal the influences of the failure degree on the rotor system, the outputs of the system with different looseness are studied. The investigation in this paper disclosed the characteristics of different fastener failures, which is helpful for failure locating and failure diagnosing. Furthermore, this work also provides the theoretical foundations for the mechanism researches of the machine failures caused by fastener faults.

Precision manufacturing engineering requires not only excellent materials but also precise machining methods. The point contact cutting is widely applied to precision turning, milling, grinding, and so on. However, the machining principle and the surface topography formation process of the point contact cutting are not well described mathematically. To solve the problem, we proposed a new surface topography analysis method considering vibration in this paper. First, based on the geometry topology theory, we reduced the dimension of the point contact cutting process. Moreover, the surface topography displacement formula was derived. Finally, the relationships between the influence factors (the shape of the cutting tools, the cutting parameters, and vibration) and the surface topography displacement were analyzed and explored. The surface topography analysis method proposed in this paper provides the theoretical foundation for designing, controlling, and optimizing the point contact cutting process.

Fault analysis activity is very important for a flexible manufacturing cell (FMC) in the development phase. Fault tree analysis (FTA) and fault mode and effects analysis (FMEA) are widely used for fault analysis. However, they are time-consuming and expensive when fully implemented. In this paper, we propose an improving hybrid multilevel FTA-FMEA method that is the combination of the above two methods. The proposed method has a clear three-layer analysis structure. In the first layer, a system FTA of the FMC is performed to determine the functional fault modes. Then, FMEA is conducted to examine them and the key functional fault modes are selected by criticality analysis. In the second layer, we perform the FTA of the determined key functional fault modes to find out the meta-action/component fault modes. The bottom events of fault trees in this layer show differences due to the subsystems with different features. Same as the first layer, FMEA is conducted subsequently and criticality analysis is also used to determine the key meta-action/component fault modes. In the last layer, we perform the FTA of the determined key meta-action/component fault modes to find out fault causes. Then, the key fault causes are determined by criticality analysis. Risk priority number (RPN) is usually used to determine the priority ranking in criticality analysis, while its calculation way is slightly naïve. In this paper, we use the technique for order preference by similarity to ideal solution (TOPSIS) method to examine the priority ranking of fault modes/causes. Moreover, we consider the correction cost as the fourth indicator to assess the priority. The improving fault analysis method can not only help designers better understand the new FMC but also help decision makers make better decisions. At last, a real FMC as a case is presented to illustrate the proposed method.

Reliability of the CNC machine tool (abbreviated as machine tool) relates to the product's reliability and has great influence on the manufacturing process. The traditional reliability statistical evaluation method of the machine tool neglects the influence of the mission profile and load profile (M&L), it causes that the evaluation result is not accurate enough to provide accurate references for the reliability-related works under specific M&Ls such as the preventive maintenance, product improvement, etc. To address these defects, this paper proposes an improved reliability statistical evaluation method considering the M&L. Firstly, the machine tool is decomposed by the meta-action decomposition method, and the mission profile of the machine tool is represented by the meta-action chain (MC). Secondly, load profile representation indicators of the machine tool are extracted based on the load composition and cutting force calculation. Then, the mapping model between the M&L and the machine tool's reliability is established using the radial basis function (RBF) neural network. Finally, the improved reliability statistical evaluation method is illustrated and validated by the engineering practical application. Comparing the evaluation results of the two statistical evaluation methods, it shows that the improved reliability statistical evaluation method is more accurate than the traditional reliability statistical evaluation method, under specific M&Ls, so that it can provide more accurate references for the reliability-related works.

A reasonable structural decomposition method is the foundation of studying the error propagation mechanism precisely. In this paper, a new Function-Motion-Action-Part (FMAP) structural decomposition method is proposed to analyze the mapping model between the geometric dimension of part and output errors of a mechanical product. First, the meta-action unit (MAU) theory is extended to the part layer, and the MAU structure and the MAU chain are redefined. Second, according to the coordination characteristics of the parts in MAU, the error expression model of the parts and the error propagation model of the MAU assembly process are established. The state-space model of the MAU output error is further established so as to complete the mapping model from the geometric dimension of manufactured parts to the precision of the MAU. Then, according to the characteristics of the kinematic error propagation between the MAUs, the input and output error indexes of the MAU chain are determined, and the error propagation model in the MAU chain is established based on the radial basis function network. Finally, the methodology is applied to a numerical control rotary table to verify the effectiveness of the FMAP structural decomposition method for the error propagation mechanism analysis.

Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

Construction and demolition (C&D) waste has become a research hotspot due to the need for environmental sustainability and strength enhancement of cementitious materials. However, wider applications of C&D waste are limited, as its non-homogeneous surface nature limits its workability. This research evaluated the feasible utilization of C&D waste as aggregates in polypropylene-fiber-reinforced cement-stabilized soil (CSS) under sulfate-alkali activation. CSS specimens incorporated Portland cement and C&D waste in 10%, 20%, and 30% proportions. Also, polypropylene fiber after alkali activation by sodium sulfate (at 0.2%, 0.4%, and 0.8% dosing level) was defined as 1%, 2%, and 4%. Strength enhancement was examined through unconfined compressive strength (UCS) and flexural strength tests at 7, 14 and 28 days. Test results indicated that mechanical properties showed significant improvement with increasing levels of Portland cement and sodium sulfate, while the improvement dropped after excessive addition of C&D waste and polypropylene fiber. Optimal proportioning was determined as 30%, 4%, 20%, and 0.8% for Portland cement, polypropylene fiber, C&D waste, and sodium sulfate, respectively. Scanning electron microscope (SEM) analysis attributed the enhancement to hydration product (ettringite) formation, bridging effect and increased particle friction. Additionally, the decrease in amplification was ascribed to the destruction of interface transition-zone (ITZ) strength, resulting in premature failure.

An analytical three-dimensional slope reliability evaluation framework was developed in this work independent of use of numerical simulations. The slope stability analysis was necessarily carried out by utilizing an extended three-dimensional Morgenstern–Price method, which was characterized by analytical formulations and competitive computational efficiency. Incorporation of the presented stability analysis method into response surface methodology led to an effective slope reliability evaluation framework. The applicability and superiority of this framework was examined and validated using a real complicated landslide case reported in practice, and a hypothetical slope example widely adopted in the literature. The impact of correlation coefficients and probability distribution patterns on the slope reliability assessment results was further addressed to derive additional benefits of this framework.

The usability of CNC machine tools is a comprehensive reflection of reliability, maintainability and supportability, which is also a feature that users concern about mostly. It is affected not only by the function failures of CNC machine tools, but also by the performance failures, such as precision degradation, performance fluctuation, oil leakage and so on. So separated research on reliability is not enough to maintain the "usable and easy-to-use state" of domestic CNC machine tools. The present situation and research significance of domestic CNC machine tools' usability are analyzed. The research status of usability modeling method and maintenance decision method at home and abroad are expounded. Then the problems and developments existing in usability research of CNC machine tools are summarized. Finally, the understanding of two key scientific problems in urgent need of research are put forward, including systematic modeling for the usability based on meta-action unit and active maintenance decision centered on usability of CNC machine tools, which might provide ideas for the usability research of CNC machine tools.

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively.

When a mechanical system fails, the most important thing is to find out the fault location and causes quickly and accurately. Based on the meta-action unit, this article proposes a comprehensive, qualitative, and quantitative fault diagnosis method composed of fault tree analysis and evidential network to analyze faults of the mechanical system. First, we expand the function–motion–action decomposition method, revise the definitions of meta-action and meta-action unit derived from the function–motion–action decomposition of the complex mechanical system, use the fault tree analysis approach to analyze the faults of the meta-action unit instead of the system directly, and establish the meta-action unit fault tree analysis (MU-FTA) model with simple levels. These activities can tremendously simplify the qualitative diagnostic model. Second, the MU-FTA model is mapped to the evidential network model according to their similarities. Finally, we fuse the after-sales data and expert experience information by Dempster’s combination rule to calculate the basic probability assignments of the parent nodes in the bottom layer of the evidential network model. Thus, the basic probability assignments of meta-action units are obtained by network transmission and the diagnostic conclusion is acquired. The fault diagnosis of a certain type of numerical control turntable is conducted as an example to demonstrate and verify the feasibility and effectiveness of the proposed approach.

A comprehensive fault analysis of CNC machine tool is conducive to improving its reliability. Due to the highly complex structure of CNC machine tool, there are different degrees of coupling relationship between faults. However, the traditional fault analysis methods (FMEA, FTA, etc.) for CNC machine tool do not solve this problem perfectly. Therefore, we propose a coupling fault propagation model based on meta-action. First, in order to simplify the structural complexity of CNC machine tool, the “Function-Motion-Action (FMA)” decomposition structure is used to decompose the product function into simple meta-action, and the numerical matrix is used to quantify the coupling relationship between the meta-actions. Then, based on the fault transfer characteristics of meta-action, the fault propagation model is established, and the global risk effect (GRE) is combined to realize the comprehensive evaluation of the risk criticality of meta-actions. Finally, the rationality and validity of the method are verified by the case analysis of the automatic pallet changer (APC) of computerized numerical control (CNC) machining center.

Nowadays, modern manufacturing enterprises pay more and more attention to reliability in order to ensure the mechatronic product quality. Since the reliability modeling and evaluation of mechatronic product are the bases of reliability engineering, the novel system full-state model and assessment method based on meta-action are proposed in this article. First, the “Function-Motion-Action” decomposition method and meta-action definition are introduced, and based on the thought of “Top-Down,” the mechatronic product is decomposed to the most basic motion unit—meta-action unit. Second, on the basis of the fact that the reliability of mechatronic product is determined by the reliability of meta-action unit, the complete new classification method is presented. This method divides the product operation states into the meta-action unit fault state and normal state. Third, the collaborative full-state model is established for mechatronic product, the Markov process with all states is combined, the model is solved by analytical method, and the reliability performance was evaluated. Finally, an example of the numerical control rotary table is analyzed to verify the effectiveness and feasibility of the methodology.

Nowadays, although more and more companies focus on improving the quality of computer numerical control machine tools, its reliability control still remains as an unsolved problem. Since assembly reliability control is very important in product reliability assurance in China, a new key assembly processes extraction method based on the integration of quality function deployment; failure mode, effects, and criticality analysis; and fuzzy theory for computer numerical control machine tools is proposed. Firstly, assembly faults and assembly reliability control flow of computer numerical control machine tools are studied. Secondly, quality function deployment; failure mode, effects, and criticality analysis; and fuzzy theory are integrated to build a scientific extraction model, by which the key assembly processes meeting both customer functional demands and failure data distribution can be extracted, also an example is given to illustrate the correctness and effectiveness of the method. Finally, the assembly reliability monitoring system is established based on key assembly processes to realize and simplify this method.

(1) The alloy material 20CrMnTiH is widely used in gear manufacturing, but difficult to process, and its quantity (efficiency) and quality (surface quality) are generally negative correlation indicators. As a difficult but realistic problem, it is of important practical significance to explore how to efficiently grind high-precision low-carbon alloy gear workpieces. (2) Firstly, the pixel method was applied to analyze the grinding principles and explore the grinding parameters—the grinding wheel speed and grinding wheel frame moving speed—as well as the feed rate, which impacts the grinding indicators. Secondly, based on the ceramic microcrystalline corundum grinding wheel and the 20CrMnTiH gear workpiece, controlled experiments with 28 groups of grinding parameters were conducted. Moreover, the impact curves of the grinding parameters on the grinding indicators—the grinding efficiency, grinding wheel life, and surface roughness—were obtained by the multiple linear regression method. Finally, the multi-objective optimization method was used to comprehensively optimize the grinding process. (3) Compared with the traditional grinding process, under optimized grinding parameters, the 20CrMnTiH gear workpieces have a lower surface roughness and a longer grinding wheel life, and require a shorter time to achieve grinding accuracy. (4) The grinding experiments showed that the grinding parameters are linearly related to the grinding indicators. The optimization results show that the precision, efficiency, and economy of the 20CrMnTiH gear grinding process have been improved via the comprehensive optimization of the grinding parameters.

As everyone knows, assembly quality plays a very important role in final product quality. Since computer numerical control machine tool is a large system with complicated structure and function, and there are complex association relationships among quality characteristics in assembly process, then it is difficult and inaccurate to analyze the whole computer numerical control machine tool quality characteristic association at one time. In this article, meta-action assembly unit is proposed as the basic analysis unit, of which quality characteristic association is studied to guarantee the whole computer numerical control machine tool assembly quality. First, based on “Function-Motion-Action” decomposition structure, the definitions of meta-action and meta-action assembly unit are introduced. Second, manufacturing process association and meta-action assembly unit quality characteristic association are discussed. Third, after understanding the definitions of information entropy and relative entropy, the concrete meta-action assembly unit quality characteristic association analysis steps based on relative entropy are described in detail. And finally, the lifting piston translation assembly unit of automatic pallet changer is taken as an example, the association degree between internal leakage and the influence factors of part quality characteristics and mate-relationships among them are calculated to figure out the most influential factors, showing the correctness and feasibility of this method.