4.7 Article

A context-aware concept evaluation approach based on user experiences for smart product-service systems design iteration

Journal

ADVANCED ENGINEERING INFORMATICS
Volume 50, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2021.101394

Keywords

Smart product-service systems; Concept evaluation; User experience; Information axiom; Context-awareness

Funding

  1. National Research Foundation (NRF) Singapore
  2. Delta Electronics International (Singapore) Pte Ltd., under the Corporate Laboratory@ University Scheme at Nanyang Technological University, Singapore [RCA-16/434]

Ask authors/readers for more resources

This paper proposes an automatic Smart PSS evaluation method to meet user requirements more timely and automatically by collecting user experience information, demonstrated through a case study.
With the trend of 'digitalization' and 'servitization' in the manufacturing industry, numerous product-service systems fail to seize a market share and encounter an imbalance between digital investments and expected revenues. This phenomenon is probably caused by the insufficient evaluation on user experience and by the lag between user requirement changes and the offered solutions. Both limitations can be mitigated via automatic Smart PSS evaluation based on broader concerns on user experience information that was collected from either product-service bundles or user behavior. In this paper, a context-aware concept evaluation approach is proposed for Smart PSS design iteration, aiming to satisfy users in a more timely and automatic manner. Derived from the conventional information axiom method, the proposed approach introduces a context-aware evaluation indicator identification module and an automatic system range identification procedure based on natural language processing techniques, and eventually return the most robust concepts during the usage phase. With less human intervention in the design process, it relieves the lag between user requirement changes and the solutions, and reduces the prescriptive instructions in the conventional information axiom method. A case study of a 3D printer company's design iteration is conducted, which proves the proposed approach's feasibility. It is hoped that this work provides practical guidance for achieving a more context-aware Smart PSS development.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Review Engineering, Industrial

Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review

Chao Liu, Pai Zheng, Xun Xu

Summary: This paper presents a systematic literature review on the digitalisation and servitisation of machine tools in the context of Industry 4.0. The review provides a comprehensive understanding of recent advancements in this field, including key technologies, methods, standards, architectures, and applications. Additionally, a novel conceptual framework called Cyber-Physical Machine Tool (CPMT) is proposed as a systematic approach to achieving digitalisation and servitisation of next-generation machine tools. The paper also discusses major research issues, challenges, and future research directions.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2023)

Article Engineering, Industrial

Hybrid sensing-based approach for the monitoring and maintenance of shared manufacturing resources

Geng Zhang, Chun-Hsien Chen, Bufan Liu, Xinyu Li, Zuoxu Wang

Summary: With the rapid development of information technologies, shared manufacturing is facing a growing need for monitoring and maintenance. Existing research primarily focuses on a resource-centric strategy for management, overlooking the experience data from users/customers. To fill this gap, a hybrid sensing-based approach is proposed for monitoring and maintenance of shared manufacturing resources.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2023)

Editorial Material Engineering, Multidisciplinary

Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

ENGINEERING (2023)

Article Computer Science, Artificial Intelligence

A visual reasoning-based approach for driving experience improvement in the AR-assisted head-up displays

Yongshi Liang, Pai Zheng, Liqiao Xia

Summary: Enabled by advanced data analytics and intelligent computing, this study proposes a visual reasoning-based approach to present drivers with perceptual, predictive, and reasoning information onto augmented reality head-up displays (AR-HUDs) toward cognitive intelligence. A driving scenario knowledge graph is established, and the driving scene is comprehensively perceived through analyzing video streams and images collected by an in-car visual camera. A graph-based driving scenario reasoning model is built for driving strategy recommendations. The analyzed information is intuitively shown on the HUDs via pre-defined AR graphics. A case study is provided to prove its feasibility.

ADVANCED ENGINEERING INFORMATICS (2023)

Article Engineering, Industrial

Adaptive optimal process control with actor-critic design for energy-efficient batch machining subject to time-varying tool wear

Qinge Xiao, Zhile Yang, Yingfeng Zhang, Pai Zheng

Summary: Batch machining systems are crucial for productivity and quality improvement but consume significant energy due to continuous interaction with machine tools, workpieces, and cutting tools. This study focuses on adaptive process control considering time-varying tool wear using reinforcement learning (RL). An energy-efficient decision model is developed using the Markov decision process, and an actor-critic RL framework is proposed for dynamic process control. Experimental results show that the RL method can reduce energy consumption by over 20% compared to optimization ignoring tool wear effects and has three times faster learning efficiency than metaheuristic methods.

JOURNAL OF MANUFACTURING SYSTEMS (2023)

Review Engineering, Industrial

A review of digital twin-driven machining: From digitization to intellectualization

Shimin Liu, Jinsong Bao, Pai Zheng

Summary: Digital twin (DT) technology is becoming a hot topic in intelligent machining as it enables quality control of the dynamic cutting process through the establishment of high-fidelity DT models. However, there is a lack of clear and systematic analysis of DT-driven machining. This study conducted a state-of-the-art survey, classifying the concepts, analyzing the characteristics and processes, and proposing future research directions to promote further discussions and research in this field.

JOURNAL OF MANUFACTURING SYSTEMS (2023)

Article Engineering, Multidisciplinary

A state-of-the-art survey of welding radiographic image analysis: Challenges, technologies and applications

Tianyuan Liu, Pai Zheng, Jinsong Bao, Huabin Chen

Summary: This paper provides a comprehensive review and discussion of WRIA, including the challenges faced, the evolutionary paths, and specific application tasks. It also explores potential future perspectives for WRIA, offering useful insights to both academic researchers and industrial practitioners.

MEASUREMENT (2023)

Article Computer Science, Interdisciplinary Applications

An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction

Chengxi Li, Pai Zheng, Yue Yin, Yat Ming Pang, Shengzeng Huo

Summary: With the emergence of Industry 5.0, there is a need for human workers and manufacturing equipment, such as robots, to interact in dealing with dynamic and complex production tasks. This study proposes a mutual-cognitive safe human-robot interaction approach to address the safety requirements. The approach includes worker visual augmentation, robot velocity control, Digital Twin-enabled motion preview and collision detection, and Deep Reinforcement Learning-based robot collision avoidance motion planning in an Augmented Reality-assisted manner.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2023)

Article Computer Science, Interdisciplinary Applications

Neural reactive path planning with Riemannian motion policies for robotic silicone sealing?

Peng Zhou, Pai Zheng, Jiaming Qi, Chengxi Li, Anqing Duan, Maggie Xu, Victor Wu, David Navarro-Alarcon

Summary: Silicone sealing, with its excellent chemical and mechanical properties, is commonly used in various industries. Due to labor shortages, there is a need for automated solutions for sealing tasks. This paper presents a new method that uses vision-guided robotic systems to automate silicone sealing, utilizing a neural path planning framework and a Riemannian motion policy.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2023)

Review Computer Science, Interdisciplinary Applications

Proactive human-robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives

Shufei Li, Pai Zheng, Sichao Liu, Zuoxu Wang, Xi Vincent Wang, Lianyu Zheng, Lihui Wang

Summary: This paper discusses the importance of human-robot collaboration in smart manufacturing. Existing development in this area is either human-dominant or robot-dominant, lacking efficient integration of robotic automation and human cognition. Proactive human-robot collaboration is proposed as a solution, allowing multiple agents to collaboratively operate based on each other's needs and capabilities. The paper also highlights current challenges and future research directions in this field.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2023)

Article Materials Science, Characterization & Testing

A multiple scale spaces empowered approach for welding radiographic image defect segmentation

Tianyuan Liu, Pai Zheng, Xiaojia Liu

Summary: This study proposes a multiple scale spaces segmentation method to address the complexity of scale variability and contextual relationships in welding defect segmentation. By constructing multi-scale feature space, semantic space, and relational space, it effectively segments complex welding defects.

NDT & E INTERNATIONAL (2023)

Article Automation & Control Systems

Histogram-Based Gradient Boosting Tree: A Federated Learning Approach for Collaborative Fault Diagnosis

Liqiao Xia, Pai Zheng, Jinjie Li, Xiao Huang, Robert X. Gao

Summary: Data-driven approaches are widely used in fault diagnosis, but the lack of sufficient labels and data privacy protection remain challenges in real-world manufacturing scenarios. This study proposes a federated learning model using histogram-based gradient boosting tree, which protects data privacy by only communicating relevant local model parameter updates and introduces a data compensation mechanism to address data volume disparity.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2023)

Article Engineering, Industrial

Graph-enabled cognitive digital twins for causal inference in maintenance processes

Kendrik Yan Hong Lim, Theresia Stefanny Yosal, Chun-Hsien Chen, Pai Zheng, Lihui Wang, Xun Xu

Summary: The increasing complexity of industrial systems requires more effective and intelligent maintenance approaches to address manufacturing defects. This paper introduces a cognitive digital twin system that leverages industrial knowledge graphs to support maintenance planning and operations. The system can manage interconnected systems, facilitate cross-domain analysis, and generate feasible solutions validated through simulation. It can also identify potential disruptions in new product designs.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2023)

Review Engineering, Industrial

Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm

Jiewu Leng, Yuanwei Zhong, Zisheng Lin, Kailin Xu, Dimitris Mourtzis, Xueliang Zhou, Pai Zheng, Qiang Liu, J. Leon Zhao, Weiming Shen

Summary: Manufacturers are realizing the importance of system resilience and considering the use of Decentralized Autonomous Organization (DAO) to achieve decentralized autonomous manufacturing and resilient Industry 5.0 vision. This paper reviews the literature on Decentralized Manufacturing (DM) and Autonomous Manufacturing (AM), and proposes a manufacturing paradigm called Decentralized Autonomous Manufacturing (DAM). Future research directions and challenges of DAM are highlighted.

JOURNAL OF MANUFACTURING SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

A multiplex network based analytical framework for safety management standardization in construction engineering

Fangyu Chen, Yongchang Wei, Hongchang Ji, Gangyan Xu

Summary: This paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management and validates its effectiveness through a case study. The research findings suggest that key standards often encompass a wider array of risks, providing suggestions for revising construction standards.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Digital twin-enabled collision early warning system for marine piling: Application to a wharf project in China

Minghao Li, Qiubing Ren, Mingchao Li, Ting Kong, Heng Li, Huijing Tian, Shiyuan Liu

Summary: This study proposes a method using digital twin technology to construct a collision early warning system for marine piling. The system utilizes a five-dimensional model and four independently maintainable development modules to maximize its effectiveness. The pile positioning algorithm and collision early warning algorithm are capable of providing warnings for complex pile groups.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Development of a technology tree using patent information

Seokhyun Ryu, Sungjoo Lee

Summary: This study proposes the use of patent information to develop a robust technology tree and applies it to the furniture manufacturing process. Through methods such as clustering analysis, semantic analysis, and association-rule mining, technological attributes and their relationships are extracted and analyzed. This approach provides meaningful information to improve the understanding of a target technology and supports research and development planning.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Feature-based domain disentanglement and randomization: A generalized framework for rail surface defect segmentation in unseen scenarios

Shuai Ma, Kechen Song, Menghui Niu, Hongkun Tian, Yanyan Wang, Yunhui Yan

Summary: This paper proposes a feature-based domain disentanglement and randomization (FDDR) framework to improve the generalization of deep models in unseen datasets. The framework successfully addresses the appearance difference issue between training and test images by decomposing the defect image into domain-invariant structural features and domain-specific style features. It also utilizes randomly generated samples for training to further expand the training sample.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Improving indoor wayfinding with AR-enabled egocentric cues: A comparative study

Fang Xu, Tianyu Zhou, Hengxu You, Jing Du

Summary: This study explores the impact of AR-based egocentric perspectives on indoor wayfinding performance. The results reveal that participants using the egocentric perspective demonstrate improved efficiency, reduced cognitive load, and enhanced spatial awareness in indoor navigation tasks.

ADVANCED ENGINEERING INFORMATICS (2024)

Review Computer Science, Artificial Intelligence

Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

Yujie Lu, Shuo Wang, Sensen Fan, Jiahui Lu, Peixian Li, Pingbo Tang

Summary: Image-based 3D reconstruction plays a crucial role in civil engineering by bridging the gap between physical objects and as-built models. This study provides a comprehensive summary of the field over the past decade, highlighting its interdisciplinary nature and integration of various technologies such as photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. The proposed 3D reconstruction knowledge framework outlines the essential elements, use phases, and reconstruction scales, and identifies eight future research directions. This review is valuable for scholars interested in the current state and future trends of image-based 3D reconstruction in civil engineering, particularly in relation to deep learning methods.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

A novel method for intersecting machining feature segmentation via deep reinforcement learning

Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang

Summary: This paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework enhances the effectiveness of intersecting machining feature segmentation by leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning. Experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Imbalanced domain generalization via Semantic-Discriminative augmentation for intelligent fault diagnosis

Chao Zhao, Weiming Shen

Summary: This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Discovering semantic and visual hints with machine learning of real design templates to support insight exploration in informatics

Ching-Chih Chang, Teng-Wen Chang, Hsin-Yi Huang, Shih-Ting Tsai

Summary: Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Machining feature process route planning based on a graph convolutional neural network

Zhen Wang, Shusheng Zhang, Hang Zhang, Yajun Zhang, Jiachen Liang, Rui Huang, Bo Huang

Summary: This research proposes a novel approach for machining feature process planning using graph convolutional neural networks. By representing part information with attribute graphs and constructing a learning model, the proposed method achieves higher accuracy and resolves current limitations in machining feature process planning.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

A Copula network deconvolution-based direct correlation disentangling framework for explainable fault detection in semiconductor wafer fabrication

Hong-Wei Xu, Wei Qin, Jin-Hua Hu, Yan-Ning Sun, You -Long Lv, Jie Zhang

Summary: Wafer fabrication is a complex manufacturing system, where understanding the correlation between parameters is crucial for identifying the cause of wafer defects. This study proposes a Copula network deconvolution-based framework for separating direct correlations, which involves constructing a complex network correlation diagram and designing a nonlinear correlation metric model. The proposed method enables explainable fault detection by identifying direct correlations.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Adaptive knowledge push method of product intelligent design based on feature transfer

Yida Hong, Wenqiang Li, Chuanxiao Li, Hai Xiang, Sitong Ling

Summary: An adaptive push method based on feature transfer is proposed to address sparsity and cold start issues in product intelligent design. By constructing a collaborative filtering algorithm model and transforming the rating model, the method successfully alleviates data sparsity and cold start problems.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Empowering intelligent manufacturing with edge computing: A portable diagnosis and distance localization approach for bearing faults

Hairui Fang, Jialin An, Bo Sun, Dongsheng Chen, Jingyu Bai, Han Liu, Jiawei Xiang, Wenjie Bai, Dong Wang, Siyuan Fan, Chuanfei Hu, Fir Dunkin, Yingjie Wu

Summary: This work proposes a model for real-time fault diagnosis and distance localization on edge computing devices, achieving lightweight design and high accuracy in complex environments. It also demonstrates a high frame rate on edge computing devices, providing a novel solution for industrial practice.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

A digital twin-based motion forecasting framework for preemptive risk monitoring

Yujun Jiao, Xukai Zhai, Luyajing Peng, Junkai Liu, Yang Liang, Zhishuai Yin

Summary: This paper proposes a digital twin-based motion forecasting framework that predicts the future trajectories of workers on construction sites, accurately predicting workers' motions in potential risk scenarios.

ADVANCED ENGINEERING INFORMATICS (2024)

Article Computer Science, Artificial Intelligence

Time-tired compaction: An elastic compaction scheme for LSM-tree based time-series database

Ling-Zhe Zhang, Xiang-Dong Huang, Yan-Kai Wang, Jia-Lin Qiao, Shao-Xu Song, Jian-Min Wang

Summary: Time-series DBMSs based on the LSM-tree have been widely applied in various scenarios. The characteristics of time-series data workload pose challenges to efficient queries. To address issues like query latency and inaccurate range, we propose a novel compaction algorithm called Time-Tiered Compaction.

ADVANCED ENGINEERING INFORMATICS (2024)