Xinrong Hu, Shuangshuang Li, Tao Peng, Shi Dong, Junjie Zhang, Changnian Chen, Zlli Zhang, Shuqin Cui, Ruhan He, Min Li and Junping Liu
Fabric has complicated anisotropic mechanical behavior because of the woven pattern and complex physical properties. However, most current fabric simulation models are not…
Abstract
Purpose
Fabric has complicated anisotropic mechanical behavior because of the woven pattern and complex physical properties. However, most current fabric simulation models are not satisfied because the models are usually geometrical models with stiffness parameters.
Design/methodology/approach
In this paper, the authors present a modeling technique to simulate fabric with Riemann manifold. The proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area. It decomposes the m × n matrix constituting the fabric into several batches and processes the fabric dots in batches. In this model, the distance between any two adjacent particles of the fabric's is assumed to be equal, and the area of the curved surface is always constant, and the inclination and decay of the ridge wave-curved surface are also considered.
Findings
As the result, the simulated shape is lifelike. In time cost performance, the model improves the efficiency of the fabric styling and meets the requirements of real-time simulation.
Originality/value
The proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area.
Details
Keywords
Tao Peng, Shuangmei Xu, Hong Zhang and Yi Zhu
Many process parameters in selective laser melting (SLM) can be configured to optimize build time, which directly relates to energy consumption, and to achieve acceptable part…
Abstract
Purpose
Many process parameters in selective laser melting (SLM) can be configured to optimize build time, which directly relates to energy consumption, and to achieve acceptable part quality. This study aims to investigate whether energy can be effectively reduced with acceptable mechanical properties. The influence of exposure time is primarily focused to correlate energy consumption to mechanical properties.
Design/methodology/approach
Through single-factor design and experiment result analysis, three levels of exposure time were examined in fabricating two sets of sample parts, for energy analysis and mechanical property tests. Manufacturing power profile was measured online, and four mechanical properties, tensile, flexural, torsional strengths and part density, were investigated. A graphical growth rate tendency (GRT) plot is proposed to jointly analyze multiple variables.
Findings
Energy consumption increases in fabricating a same part with the increase of exposure time in the tested range, but exposure time was found to influence build power rather than build time in the given SLM system. Mechanical properties do not increase linearly, and grow at different rates. It is found that within the tested range, increased energy consumption brought to a small improvement of part density, but a notable improvement of tensile strength and maximum torque.
Practical implications
Producing quality SLM parts can be energy-effective through quantitative study. The proposed GRT plot is an intuitive visual aid to compare the growth rates of different variables, which offers more information to additive manufacturing practitioners.
Originality/value
In this research, energy consumption and mechanical property are jointly analyzed for the first time to advance the knowledge of energy-effective SLM fabrication. This helps additive manufacturing technology to be truly energy-efficient and environmental-friendly.
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Tao Peng, Xingliang Liu, Rui Fang, Ronghui Zhang, Yanwei Pang, Tao Wang and Yike Tong
This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.
Abstract
Purpose
This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.
Design/methodology/approach
The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.
Findings
The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.
Originality/value
This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.
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Binghai Zhou and Tao Peng
This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated…
Abstract
Purpose
This paper aims to investigate the just-in-time (JIT) in-house logistics problem for automotive assembly lines. A point-to-point (P2P) JIT distribution model has been formulated to specify the destination station and parts quantity of each delivery for minimizing line-side inventory levels.
Design/methodology/approach
An exact backtracking procedure integrating with dominance properties is presented to cope with small-scale instances. As for real-world instances, this study develops a modified discrete artificial bee colony (MDABC) metaheuristic. The neighbor search of MDABC is redefined by a novel differential evolution loop and a breadth-first search.
Findings
The backtracking method has efficaciously cut unpromising branches and solved small-scale instances to optimality. Meanwhile, the modifications have enhanced exploitation abilities of the original metaheuristic, and good approximate solutions are obtained for real-world instances. Furthermore, inventory peaks are avoided according to the simulation results which validates the effectiveness of this mathematical model to facilitate an efficient JIT parts supply.
Research limitations/implications
This study is applicable only if the breakdown of transport devices is not considered. The current work has effectively facilitated the P2P JIT logistics scheduling in automotive assembly lines, and it could be modified to tackle similar distribution problems featuring time-varying demands.
Originality/value
Both limited vehicle capacities and no stock-outs constraints are considered, and the combined routing and loading problem is solved satisfactorily for an efficient JIT supply of material in automotive assembly lines.
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Arfan Majeed, Jingxiang Lv and Tao Peng
This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.
Abstract
Purpose
This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.
Design/methodology/approach
Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM.
Findings
The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase.
Research limitations/implications
This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.
Practical implications
The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.
Originality/value
This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.
Details
Keywords
Tao Peng and Binghai Zhou
With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate…
Abstract
Purpose
With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels.
Design/methodology/approach
A mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method.
Findings
Both CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances.
Originality/value
No stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.
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Varimna Singh, Preyal Sanghavi and Nishant Agrawal
Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain…
Abstract
Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain (SC) activities and achieve sustainable growth through dynamic capabilities (DCs). This approach equips businesses with the necessary tools to optimise their operations and remain competitive in a dynamic business environment. The value proposition of a business encompasses a wide range of activities that add value at each stage. By leveraging DCs, a firm can achieve innovation, gain a competitive advantage and enhance its adaptability. Conversely, effective value chain management can amplify the influence of a firm's DCs on SC sustainability, by reducing waste, optimising resource utilisation and fostering strategic partnerships. This mutually beneficial connection takes the form of a dynamic interaction in which I4.0 technologies act as a catalyst to help organisations become more resilient, adaptive and responsive. The adoption of these technologies denotes a comprehensive approach to business shift, not merely technical integration. I4.0 has an impact on several organisational disciplines outside of manufacturing, from automation and efficiency advantages to quality enhancements. This chapter offers an extensive literature review to explore the level of SC sustainability that a business can achieve by combining its DCs and implementing strategic I4.0 adoption. The function of value chain management in moderating the effects of I4.0 and DCs on SC sustainability is also assessed. This study proposes a theoretical model that is grounded in the insights extracted from the literature review.
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Blown up theory is very important in modern forecasting science, and will result in revolution not only in forecasting theories but also in applied theories and applied methods…
Abstract
Blown up theory is very important in modern forecasting science, and will result in revolution not only in forecasting theories but also in applied theories and applied methods. Moreover, the blown‐up theory will involve re‐thinking and re‐formulation of some concepts in traditional theories. This article is a record of dialogue between Professor OuYang and the author on some important issues. It is believed that this record will not only benefit us greatly, but also be inductive for young generations in developing their way of thinking and research directions.
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Digital transformation provides a new impetus for the development of declining firms. However, there is currently a lack of sufficient research on whether digital transformation…
Abstract
Purpose
Digital transformation provides a new impetus for the development of declining firms. However, there is currently a lack of sufficient research on whether digital transformation is beneficial for the turnaround of declining firms. This paper aims to explore the relationship between digital transformation and the turnaround of declining firms.
Design/methodology/approach
Drawing on the theoretical foundations of the resource-based view and dynamic capabilities theory, this research uses a comprehensive dataset of Chinese A-share listed companies from 2010–2021 to explore the influence of digital transformation on the turnaround of declining firms.
Findings
The research findings show that digital transformation contributes to the turnaround of declining firms. Mechanism analyses demonstrate that digital transformation enhances dynamic capabilities and attracts more analysts, thereby facilitating the turnaround process. Moreover, the moderation analysis reveals that CEO equity incentives strengthen the positive correlation between digital transformation and the turnaround of declining firms. Heterogeneity analysis indicates that the association between digital transformation and the turnaround of declining firms is particularly significant for firms with low financing constraints and high-tech firms. Moreover, this research reveals that digital transformation can facilitate the turnaround of firms in deep and long-term decline.
Originality/value
This research contributes to the literature on the digital transformation of enterprises and provides important insights for the turnaround of declining firms.
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Bin Li, Jiayi Tao, Domenico Graziano and Marco Pironti
Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the…
Abstract
Purpose
Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the operational performance of Chinese traditional retail enterprises. Such improvements have crucial theoretical value and practical implications for Chinese traditional retail enterprises to achieve transformation and sustainable development.
Design/methodology/approach
This study applied the typical analysis method, selected China’s leading mobile social platform, WeChat, as a typical case, and observed and analyzed the public data of the traditional retail industry and social platforms and interviews with relevant enterprises. On this basis, this study used the inductive and deductive methods of qualitative research to conduct an in-depth analysis of the mechanism by which WeChat’s digital empowerment improves the operational performance of Chinese traditional retail enterprises. It also discussed the critical role and path knowledge management capabilities play in this mechanism.
Findings
This research demonstrated that mobile social platforms empower Chinese traditional retail enterprises to build diversified digital channels, enhance the knowledge acquisition capability of enterprises and thus improve their performance; empower Chinese traditional retail enterprises to build digital community networks, enhance the knowledge diffusion capability of enterprises and thus improve their performance; and empower Chinese traditional retail enterprises to integrate online and offline businesses, enhance the knowledge integration capability of enterprises and thus improve their performance.
Research limitations/implications
This study clarifies the internal mechanism of how the digital empowerment of mobile social platforms can improve the performance of Chinese traditional retail enterprises. This mechanism implies that knowledge management capabilities (knowledge acquisition, diffusion and integration capability) are the underlying logic for Chinese traditional retail enterprises to achieve higher performance levels. This has important practical implications for managers of Chinese traditional retail enterprises to leverage the digital infrastructure of mobile social platforms to achieve the sustainable development of enterprises.
Originality/value
This study provides an in-depth analysis of how the traditional retail industry uses digital social platforms to improve operational performance from the perspective of knowledge management capabilities, which can further promote the theoretical research and practical development of digitalization and knowledge management. At the same time, this study explored the research on the operational performance of Chinese traditional retail enterprises from the perspective of knowledge management capabilities and expanded the research on knowledge management in related fields. The authors have initially sorted out the impact of knowledge management capabilities on the operational performance of Chinese traditional retail enterprises in the digital era. This will help better understand the role and function of knowledge management in strategic transformation and expand the application of knowledge management theory.