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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.

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.

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.

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.

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.

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.

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.

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.

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.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

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.

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.

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.

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.

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.

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.

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.

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.

This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process.

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.

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.

This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering.

The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In contrast to existing studies, this paper aims to propose that digital transformation does not depend on a single condition; rather, it depends on the interaction between internal and external factors of a firm. Therefore, the aim of this paper is to examine the effect of a combination of internal and external factors on a firm's digital transformation intention.

An empirical analysis on a sample of 112 Chinese small- and medium-sized firms was conducted by applying smart-PLS and fuzzy set qualitative comparative analysis (fsQCA).

The results of smart PLS show that external pressures (institutional and market pressures) and human capital have a positive impact on corporate digital transformation intentions. From a combination perspective, the results of the fsQCA show that there are five causal conditions that lead to high digital transformation intention. In contrast to the net effect, the results of fsQCA show that different combinations of states of internal (human capital, organizational culture and technological capital) and external elements (institutional and market pressures) of the firm are likely to stimulate digital transformation intention.

This study provides empirically based insights into firms' digital transformation intentions and advances the current understanding of the drivers and inhibitors of digital transformation. Unlike most current research, which tends to focus on the net effect of factors influencing the digital transformation of enterprises, this study focuses on identifying the core elements influencing enterprises' digital transformation intention, especially the joint effect of different factors, both internal and external to the enterprise. The combined SEM and fsQCA findings of this paper not only enrich the existing theories on digital transformation but also have high value in guiding the digital transformation of firms.

This study aims to examine the impact of digital transformation on firms’ value and explore the mediating impact of ESG performance and moderating impact of information interaction.

Data was collected from companies listed on the Shanghai and Shenzhen stock exchange between 2012 and 2020 with 21,488 observational samples, featuring a selection of 3,348 companies. Panel data regression techniques were used to test the mediating role of ESG performance and the moderating role of information interaction.

The study found that digital transformation can improve firms’ ESG performance, which in turn positively affects their value. The firms that engage in more interaction with outsiders benefit more from digital transformation and have a higher value.

This study provides new theoretical insight into improving firms’ value through digital transformation and ESG performance. It is the first to discuss and study the moderating role of information interaction in the relationship between digital transformation and firms’ value.