Xian Zhang, Changming Zhang, Peng Wang, Fan Yang and Chunlei Peng
The purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.
Abstract
Purpose
The purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.
Design/methodology/approach
In terms of theoretical calculation, the contact pairs wear of HD are calculated based on Archard wear formula and the relative motion characteristics of contact pairs. According to the motion trajectory of flexspline teeth, the teeth backlash and the number of meshing teeth, the meshing stiffness and overall stiffness of HD are analyzed considering the wear and its randomness of contact pairs. Combined with Monte Carlo Simulation, the stiffness reliability evaluation method considering contact pairs wear is proposed, and the result of this method is verified by the stiffness reliability result deduced from the stiffness degradation measurement data.
Findings
Considering contact pairs wear, during operation, the teeth backlash increases, the number of meshing teeth decreases, the meshing stiffness decreases, ultimately leading to a gradual decrease in the overall stiffness of HD. When only one type of contact pair wear is considered, the influence of flexspline and circular spline contact pair wear on HD stiffness reliability is greater. Compared with the stiffness reliability evaluation results obtained from the stiffness degradation data in the literature, the mathematic expectation of stiffness degradation failure life distribution obtained from the proposed method is relatively bigger.
Originality/value
The stiffness reliability evaluation method of HD considering contact pairs wear is firstly proposed. The stiffness reliability evaluation result from theoretical calculation is verified by the stiffness reliability results deduced from HD stiffness degradation measurement.
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Zhoufeng Liu, Menghan Wang, Chunlei Li, Shumin Ding and Bicao Li
The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and…
Abstract
Purpose
The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.
Design/methodology/approach
This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.
Findings
To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.
Originality/value
The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.
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Wenjie Cheng, Boqin Gu and Chunlei Shao
This paper aims to figure out the steady flow status in the molten salt pump under various temperatures and blade number conditions, and give good insight on the structure and…
Abstract
Purpose
This paper aims to figure out the steady flow status in the molten salt pump under various temperatures and blade number conditions, and give good insight on the structure and temperature-dependent efficiencies of all pump cases. Finally, the main objective of present work is to get best working condition and blade numbers for optimized hydraulic performance.
Design/methodology/approach
The steady flow in the molten salt pump was studied numerically based on the three-dimensional Reynolds-Averaged Navier–Stokes equations and the standard k-ε turbulence model. Under different temperature conditions, the internal flow fields in the pumps with different blade number were systematically simulated. Besides, a quantitative backflow analysis method was proposed for further investigation.
Findings
With the molten salt fluid temperature, sharply increasing from 160°C to 480°C, the static pressure decreases gently in all pump cases, and seven-blades pump has the least backflow under low flow rate condition. The efficiencies of all pump cases increase slowly at low temperature (about 160 to 320°C), but there is almost no variation at high temperature, and obviously seven-blades pump has the best efficiency and head in all pump cases over the wide range of temperatures. The seven-blades pump has the best performance in all selected pump cases.
Originality/value
The steady flow in molten salt pumps was systematically studied under various temperature and blade number conditions for the first time. A quantitative backflow analysis method was proposed first for further investigation on the local flow status in the molten salt pump. A definition about the low velocity region in molten salt pumps was built up to account for whether the studied pump gains most energy. This method can help us to know how to improve the efficiencies of molten salt pumps.
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Chunlei Li, Chaodie Liu, Zhoufeng Liu, Ruimin Yang and Yun Huang
The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile…
Abstract
Purpose
The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.
Design/methodology/approach
This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.
Findings
The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.
Originality/value
The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.
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ChunLei Yang, Robert W. Scapens and Christopher Humphrey
The paper proposes a place-space duality, rather than a dualism, for accounting research.
Abstract
Purpose
The paper proposes a place-space duality, rather than a dualism, for accounting research.
Design/methodology/approach
The discussion is informed by the literature in human geography, which, while developing the concept of space, has made an important distinction between abstract space and place as a site of experiential learning and memory.
Findings
The lack of a concept of place is a serious omission in the accounting literature and perpetuates an abstract sense of space, which can restrict the scope of accounting research.
Research limitations/implications
The paper calls for further research to study accounting in place and to explore both the collective and individual senses of place, as well as conscious and unconscious place associations. We recognise that there is limited prior accounting research on this topic and that there are challenges in conducting such interdisciplinary research, especially as there is a lack of common ground between research in human geography and accounting and little integration of the two literatures.
Practical implications
The paper proposes an accounting research agenda based on a place-space duality, which reflects the strength of people-place relationships, including place identities, place attachment and place dependence.
Originality/value
The paper provides a critique of the conceptualisation of space in accounting research, identifies place-space as a duality (rather than a dualism) and suggests a novel distinction between studying accounting in context and in place.
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Saira Faisal, Shenela Naqvi, Muhammad Ali and Long Lin
Among various metal oxide nano particles, MgO NPs and ZnO nanoparticles (NPs) in particular are gaining increasing attention due to their multifunctional characteristics, low cost…
Abstract
Purpose
Among various metal oxide nano particles, MgO NPs and ZnO nanoparticles (NPs) in particular are gaining increasing attention due to their multifunctional characteristics, low cost and compatibility with textile materials. Each type of nanoparticle excels over others in certain properties. As such, it is often crucial to carry out comparative studies of NPs to identify the one showing higher efficiency/output for particular applications of textile products.
Design/methodology/approach
In the investigation reported in this paper, ZnO NPs and MgO NPs were synthesised via sol-gel technique and characterised. For comparative analysis, the synthesised NPs were evaluated for multiple properties using standard procedures before and after being applied on cotton fabrics by a dip-pad-dry-cure method.
Findings
XRD and FTIR analyses confirmed the successful synthesis of ZnO and MgO NPs. Homogeneous formation of desired NPs and their dense and uniform deposition on the cotton fibre surface were observed using SEM. ZnO NPs and MgO NPs coatings on cotton were observed to significantly enhance self-cleaning/stain removal properties achieving Grade 5 and Grade 4 categories, respectively. In terms of ultraviolet (UV) protection, ZnO or MgO NP coated fabrics showed UPF values of greater than 50, i.e. excellent in blocking UV rays. MgO NPs exhibited 20% cleaning efficiency in treating reactive dye wastewater against ZnO NPs which were 4% efficient in the same treatment, so MgO was more suitable for such type of treatments at low cost. Both NPs were able to impart multifunctionality to cotton fabrics as per requirement of the end products. However, ZnO NPs were better for stain removal from the fabrics while MgO NPs were appropriate for UV blocking.
Originality/value
It was therefore clear that multifunctional textile products could be developed by employing a single type of cost effective and efficient nano particles.
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Zhoufeng Liu, Shanliang Liu, Chunlei Li and Bicao Li
This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep…
Abstract
Purpose
This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.
Design/methodology/approach
To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.
Findings
The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).
Research limitations/implications
Our proposed algorithm can provide a promising tool for fabric defect detection.
Practical implications
The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.
Social implications
This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial
Originality/value
Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.
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Although collaborative research is believed to be an important means of accessing external knowledge, research on whether taking a strategic network position benefits new product…
Abstract
Purpose
Although collaborative research is believed to be an important means of accessing external knowledge, research on whether taking a strategic network position benefits new product development (NPD) is inconclusive. This study aims to unravel the conditions under which taking a strategic position within a collaborative research network is conducive for a firm’s NPD.
Design/methodology/approach
Drawing on social network theory, absorptive capacity theory and knowledge recombinant studies, this study examines how strategic network positions (i.e. degree centrality and structural holes) and knowledge base cohesion (i.e. local and global cohesion) in tandem affect a firm’s NPD. A panel data set of 366 firms in the Chinese automobile sector (2002–2010) is empirically analyzed, using the panel negative binomial approach with random effects and several alternate estimation approaches.
Findings
This study reveals that, rather than the volume of a firm’s knowledge base, its cohesion determines how it absorbs and uses knowledge accrued from collaborative research for NPD. Specifically, this paper finds that centrally positioned firms have greater NPD when their knowledge bases are locally cohesive, while firms spanning structural holes have more NPD when their knowledge bases are globally cohesive.
Originality/value
Successfully transferring collaborative research outcomes into product innovation is difficult. This study contributes to the literature on strategic network positions and NPD. The findings advance the understanding of knowledge base cohesion’s moderating role in explaining how firms absorb and exploit external knowledge for internal innovation. The findings also have important implications for managers who wish to promote product innovation by engaging in collaborative research with external partners.
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The aim of this study was to investigate the impact of the transactive memory system (TMS) on green innovation and examine the mediation role of the social network at all…
Abstract
Purpose
The aim of this study was to investigate the impact of the transactive memory system (TMS) on green innovation and examine the mediation role of the social network at all hierarchical levels.
Design/methodology/approach
Three hypotheses were examined by performing regression analyses on survey data from manufacturing firms in China. Especially, the nested sets of data from 389 individual observations nested in 53 work teams, including individual level and collective level have been investigated.
Findings
The study results show that the TMS has a positive effect on green innovation. Furthermore, the results indicate that at the team level, structure holes' mediation in this relationship is stronger than degree centrality; at the individual level, weak ties mediation in the relationship of specialization and green innovation is stronger than strong ties, conversely, strong ties mediation in the relationship of credibility and green innovation is stronger than weak ties.
Originality/value
This study expands previous research by highlighting the significance of multilevel social network elements in the context of the TMS and sustainable development and enriches the present research on green innovation.