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“V-bending” is the most commonly used bending process in which the sheet metal is pressed into a “V-shaped” die using a “V-shaped” punch to form a required angular bend. When the punch is removed after the operation, because of elastic recovery, the bent angle varies. This shape discrepancy is known as spring back which causes problems in the assembly of the component in the modern aerospace industry. Regarding the optimization of spring-back accuracy, this research will illustrate the laws of the transition area (TA) of the nondeformation area (NDA) during the 90° “V-shape” bending process.

According to the traditional “V-bending” process to optimize the spring-back accuracy, the bent sheets are divided into deformation area (DA) and NDA. For this reason, the traditional “V-bending” process may prolong error to optimize the spring-back accuracy because NDA has a certain amount of deformation, which the researcher always avoids. Firstly, bent sheets are divided into three parts in this research: DA, TA and NDA to avoid the distortion error in TA that are not considered in the NDA in traditional theory. Then, the stress and strain in the DA and TA were discussed during theoretical derivation and some hypotheses were proposed. In this research, the interval, position and distortion degree of the TA of the bending sheet are used by finite element analysis. Finally, V-shape bending tests for aluminum alloy at room temperature are used and labeled all the work pieces' TAs to realize the deformation amount in the TA.

The bending radius does not affect the range of the TA, it only changes the position of TA in the bending sheet. It is evident that the laws of TA were explored in the width direction and gradually changed from the inner layer to the outer layer based on the ratio of width and thickness of the bending plate/sheet.

In the modern aerospace industry, aircraft manufacturing technology must maintain high accuracy. This research has practical value in the 90° “V-shape” bending of metal sheets and the development of its spring-back accuracy.

Regarding the assembly of the fuselage panel, this paper aims to illustrate a design of pre-assembly tooling of the fuselage panel for the automatic drilling riveting machine. This new prototype of pre-assembly tooling can be used for different types and sizes of fuselage panels. Also, apply to the automated drilling and riveting machine of the fuselage panels.

Based on the different structures of the fuselage panel, the position of the preassembly tooling components, location of the clamp and position of the fuselage panel are determined. After that, the overall structure of the preassembly tooling is designed, including the movable frame and the cardboard. The cardboard positioning module and the clamping module formulate a detailed design scheme of preassembly tooling for the fuselage panel. The structure of the pre-assembled tooling is optimized by static analysis. The result of the overall design is optimized by using MATLAB and CATIA-V5 software, and the results meet the condition of the design requirements.

The traditional assembly process of the fuselage is to install the fuselage panel on the preassembly tooling for positioning the hole and then install it on the automated drilling and riveting tooling for secondary tooling. Secondary tooling can consume assembly errors of the fuselage panel. The new prototype of flexible tooling design for the fuselage panel not only avoids the secondary tooling error of the fuselage panel but also meets the preassembly of different types of fuselage panels.

The further development of the flexible tooling design of the fuselage panel is to reduce the error of sliding tooling due to friction of the sliding components. Because if the assembly cycle is increased, the sliding parts will lose material due to corrosion. As a result, the repeated friction force is the root cause of the positioning error of sliding parts. Therefore, it is necessary to engage less corrosive material. Also, the lubricant may be used to reduce the corrosion in minimizing the positioning error of the sliding tool components. In addition, it is important to calculate the number of assembly cycles for efficient fuselage panel assembly.

According to the structure and assembly process characteristics of the fuselage panel, the fuselage panel preassembly tooling can optimize the assembly process of the fuselage panel and have certain practical application values.

This paper aims to illustrate the tolerance optimization method based on the assembly accuracy constrain, precession constrain and the cost of production of the assembly product.

A tolerance optimization method is an excellent way to perform product assembly performance. The tolerance optimization method is adapted to the process analysis of the hatch and skin of an aircraft. In this paper, the tolerance optimization techniques are applied to the tolerance allocation for step difference analysis (example: step difference between aircraft cabin door and fuselage outer skin). First, a mathematical model is described to understand the relationship between manufacturing cost and tolerance cost. Second, the penalty function method is applied to form a new equation for tolerance optimization. Finally, MATLAB software is used to calculate 170 loops iteration to understand the efficiency of the new equation for tolerance optimization.

The tolerance optimization method is based on the assembly accuracy constrain, machinery constrain and the cost of production of the assembly product. The main finding of this paper is the lowest assembly and lowest production costs that met the product tolerance specification.

This paper illustrated an efficient method of tolerance allocation for products assembly. After 170 loops iterations, it founds that the results very close to the original required tolerance. But it can easily say that the different number of loops iterations may have a different result. But optimization result must be approximate to the original tolerance requirements.

It is evident from Table 4 that the tolerance of the closed loop is 1.3999 after the tolerance distribution is completed, which is less than and very close to the original tolerance of 1.40; the machining precision constraint of the outer skin of the cabin door and the fuselage is satisfied, and the assembly precision constraint of the closed loop is satisfied.

The research may support further research studies to minimize cost tolerance allocation using tolerance cost optimization techniques, which must meet the given constrain accuracy for assembly products.

Continuous knowledge contribution behaviors (CKCB) are critical for the healthy development of online medical communities (OMCs). However, it is unclear that if and how contributors' prior actions and the responses they received from the community influence the nature of their future contributions. Drawing upon the Information Systems Continuance theory and Service Feedback theory, the purpose of the study is to examine the impact of knowledge contribution performance (KCP) on doctors' CKCB. Evaluation of social motivation, financial incentive and the moderating influence of expertise level (EL) provided further insight into the pathways that motivate various forms of CKCB.

In order to better understand the CKCB of physicians in OMCs, the authors divided it into two categories: A_CKCB (active CKCB) and P_CKCB (passive CKCB). Information Systems Continuance theory and Service Feedback theory are adapted and integrated with empirical findings from previous research on OMCs to develop a model of CKCB. This study used ordinary least squares (OLS) regression to test hypotheses in the preexisting research model based on data collected from a Chinese OMC platform.

The results show that KCP helps develop several facets of CKCB. According to the findings, doctors' CKCB improved dramatically after receiving feedback from A_CKCB and P_CKCB, but feedback from peers did not promote CKCB. This study found that financial rewards only have a significant positive effect on P_CKCB, and that the level of expertise has a negative effect on the effect. The findings also demonstrated that doctors' level of expertise moderates the relationship between fA_CKCB (a comprehensive evaluation of doctors' A_CKCB) and A_CKCB.

Future studies should look at the role of self-efficacy as a mediator and attitudes as a moderator in the link between KCP and various forms of CKCB. This will help authors figure out how important KCP is for physicians' CKCB. And future research should use more than one way to gather data to prove the above roles.

This study makes a significant contribution to understanding the association between CKCB and KCP by highlighting the significance of distinguishing between the various forms of CKCB and their underlying causes.

This research has advanced both the theory and practice of OMCs' user management by illuminating the central role of KCP in this context.

The primary purpose of the study is to examine the role of market power in driving innovation and productivity of intangible intensive firms of eight emerging economies of the Association of Southeast Asian Nations (ASEAN-8).

There is hardly any study on emerging economies that explored the causal chain of R&D–innovation–productivity, considering the role of market power in a structural model. Taking advantage of the availability of firm-level data and following the extended version of the Crépon, Duguet and Mairesse (CDM) model, we intend to fill the gap. The CDM model first explores the link between R&D and innovation, then the latter's impact on productivity. Besides, it captures sectoral heterogeneity and the differing roles of technological and institutional innovation on productivity.

The manufacturing firms that held a higher markup had a more significant contribution to driving innovation than services one. While institutional innovation affected productivity positively, technological innovation had the opposite impact. Nevertheless, firms' higher degree of monopoly, in general, worsened productivity outcomes. The estimated results are robust to a range of alterations.

The study offers implications for the competition policy of ASEAN.

The sample of this study accounts for almost half of the world's best-performing emerging economies. Thus, the findings are likely to contribute to the thin literature on market power's role in driving innovation and productivity in the intangible economy of emerging markets.

Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map.

The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data.

On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines.

A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

Given innovation's significance, this research examines the link between empowered leadership and frugal innovation. The research also explores how collaborative cultures and organizational commitment mediate empowered leadership's effect on frugal innovation.

Quantitative method is used with the approach of hierarchical regression to test the hypotheses with data obtained from Pakistani small- and medium-sized enterprises (SMEs) through the questionnaire from 288 participants.

The results of this study show that empowered leadership has a considerable impact on the firm's capacity for frugal innovation. Additionally, this study shows that organizational commitment and collaborative culture significantly moderate the association between empowering leadership and frugal innovation.

Future studies should examine mediating factors, including employment experience, education and perceived organizational support, and moderating variables like employee psychological empowerment and leadership styles.

This research advises SMEs in developing nations to utilize frugal innovation since they cannot afford to spend extensively on technologies that add creativity and innovation to goods and services.

This study advances how leadership both directly and indirectly helps organizations strengthen their capacity for frugal innovation through the mediating roles of collaborative culture and organizational commitment.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.