Lecture Notes in Electrical Engineering 484

Kesheng Wang · Yi Wang  Jan Ola Strandhagen · Tao Yu Editors

Advanced Manufacturing and Automation VIII

Lecture Notes in Electrical Engineering

Volume 484

Board of Series editors

Leopoldo Angrisani, Napoli, ItalyMarco Arteaga, Coyoacán, MéxicoBijaya Ketan Panigrahi, New Delhi, IndiaSamarjit Chakraborty, München, GermanyJiming Chen, Hangzhou, P.R. ChinaShanben Chen, Shanghai, ChinaTan Kay Chen, Singapore, SingaporeRuediger Dillmann, Karlsruhe, GermanyHaibin Duan, Beijing, ChinaGianluigi Ferrari, Parma, ItalyManuel Ferre, Madrid, SpainSandra Hirche, München, GermanyFaryar Jabbari, Irvine, USALimin Jia, Beijing, ChinaJanusz Kacprzyk, Warsaw, PolandAlaa Khamis, New Cairo City, EgyptTorsten Kroeger, Stanford, USAQilian Liang, Arlington, USATan Cher Ming, Singapore, SingaporeWolfgang Minker, Ulm, GermanyPradeep Misra, Dayton, USASebastian Möller, Berlin, GermanySubhas Mukhopadhyay, Palmerston North, New ZealandCun-Zheng Ning, Tempe, USAToyoaki Nishida, Kyoto, JapanFederica Pascucci, Roma, ItalyYong Qin, Beijing, ChinaGan Woon Seng, Singapore, SingaporeGermano Veiga, Porto, PortugalHaitao Wu, Beijing, ChinaJunjie James Zhang, Charlotte, USA

Lecture Notes in Electrical Engineering (LNEE) is a book series which reports thelatest research and developments in Electrical Engineering, namely:

• Communication, Networks, and Information Theory• Computer Engineering• Signal, Image, Speech and Information Processing• Circuits and Systems• Bioengineering• Engineering

The audience for the books in LNEE consists of advanced level students,researchers, and industry professionals working at the forefront of their fields. Muchlike Springer’s other Lecture Notes series, LNEE will be distributed throughSpringer’s print and electronic publishing channels.

More information about this series at http://www.springer.com/series/7818

Kesheng Wang • Yi WangJan Ola Strandhagen • Tao YuEditors

Advanced Manufacturingand Automation VIII

123

EditorsKesheng WangDepartment of Mechanical and IndustrialEngineeringNorwegian University of Scienceand TechnologyTrondheim, Sør-Trøndelag Fylke, Norway

Yi WangSchool of BusinessPlymouth UniversityPlymouth, UK

Jan Ola StrandhagenDepartment of Mechanical and IndustrialEngineeringNorwegian University of Scienceand TechnologyTrondheim, Sør-Trøndelag Fylke, Norway

Tao YuShanghai Second Polytechnic UniversityShanghai, China

ISSN 1876-1100 ISSN 1876-1119 (electronic)Lecture Notes in Electrical EngineeringISBN 978-981-13-2374-4 ISBN 978-981-13-2375-1 (eBook)https://doi.org/10.1007/978-981-13-2375-1

Library of Congress Control Number: 2015413778

© Springer Nature Singapore Pte Ltd. 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,Singapore

Preface

IWAMA—International Workshop of Advanced Manufacturing and Automation—aims at providing a common platform for academics, researchers, practicing pro-fessionals, and experts from industries to interact, discuss trends and advances, andshare ideas and perspectives in the areas of manufacturing and automation.

IWAMA began in Shanghai University in 2010. In 2012 and 2013, it was held atthe Norwegian University of Science and Technology; in 2014 at ShanghaiUniversity again; in 2015 at Shanghai Polytechnic University; in 2016 at Universityof Manchester; and in 2017 at Changshu Institute of Technology. The sponsorsorganizing the IWAMA series have expanded to many universities throughout theworld, including University of Plymouth, Changzhou University, NorwegianUniversity of Science and Technology, SINTEF, University of Manchester,Changshu Institute of Technology, Shanghai University, Shanghai PolytechnicUniversity, Xiamen University of Science and Technology, Tongji University,University of Malaga, University of Firenze, University of Stavanger, The ArcticUniversity of Norway, Shandong Agricultural University, China University ofMining and Technology, Indian National Institute of Technology, DonghuaUniversity, Shanghai Jiao Tong University, Dalian University, St. PetersburgPolytechnic University, Hong Kong Polytechnic University, and China Instrumentand Control Society. As IWAMA becomes an annual event, we are expecting thatmore sponsors from universities and industries will participate in the internationalworkshop as co-organizers.

Manufacturing and automation have assumed paramount importance and arevital factors for the economy of a nation and the quality of life. The field ofmanufacturing and automation is advancing at a rapid pace, and new technologiesare also emerging in the field. The challenges faced by today’s engineers are forcingthem to keep on top of the emerging trends through continuous research anddevelopment.

IWAMA2018 took place inChangzhouUniversity, China, September 25–26, 2018,organized by Changzhou University, University of Plymouth, Norwegian Universityof Science and Technology, and SINTEF. The program is designed to improvemanufacturing and automation technologies for the next generation through

v

discussion of the most recent advances and future perspectives, and to engage theworldwide community in a collective effort to solve problems in manufacturing andautomation.

Manufacturing research includes a focus on the transformation of present fac-tories, toward reusable, flexible, modular, intelligent, digital, virtual, affordable,easy-to-adapt, easy-to-operate, easy-to-maintain, and highly reliable “smart facto-ries.” Therefore, IWAMA 2018 has mainly covered four topics in manufacturingengineering:

1. Industry 4.02. Manufacturing systems and technologies3. Production management4. Design and optimization.

All papers submitted to the workshop have been subjected to strict peer reviewby at least two expert referees. Finally, 89 papers have been selected to be includedin the proceedings after a revision process. We hope that the proceedings will notonly give the readers a broad overview of the latest advances, and a summary of theevent, but also provide researchers with a valuable reference in this field.

Especially, we work together with Changzhou Science and Technology Bureauto organize an industry session, where more than 150 companies worldwide join thesession to discuss how AI and robotics support Industry 4.0 and ChinaManufacturing 2025.

On behalf of the organizing committee and the international scientific committeeof IWAMA 2018, I would like to take this opportunity to express my appreciationfor all the kind support, from the contributors of high-quality keynotes and papers,and all the participants. My thanks are extended to all the workshop organizers andpaper reviewers, to Changzhou University and University of Plymouth for thefinancial support, and to all co-sponsors for their generous contribution. Thanks arealso given to Jin Yuan, Quan Yu, Wanping Wu, Lin Liu, Lin Zou, Guohong Dai,and Ziqiang Zhou, for their hard editorial work of the proceedings and arrangementof the workshop.

Yi WangChair of IWAMA 2018

vi Preface

Organization

Organized and Sponsored By

China Changzhou University (CCZU), ChinaUniversity of Plymouth (PLYU), UKNorwegian University of Science and Technology (NTNU), NorwayShanghai Second Polytechnic University (SSPU), ChinaFoundation for Industrial and Technical Research (SINTEF), Norway

Co-organized by

Changshu Institute of Technology (CSLG), ChinaTongji University (TU), ChinaShandong Agriculture University (SDAU), ChinaUniversity of Stavanger (UiS), Norway

Honorary Chairs

Minglun FangKesheng Wang

General Chairs

Yi WangJan Ola StrandhagenTao Yu

vii

Local Organizing Committee

Guohong Dai (Chair)Lin LiuLin Zou

Ziqiang ZhouWanping WuLi Yang

International Program Committee

Jan Ola Strandhagen, NorwayKesheng Wang, NorwayOdd Myklebust, NorwayPer Schjølberg, NorwayKnut Sørby, NorwayErlend Alfnes, NorwayHeidi Dreyer, NorwayTorgeir Welo, NorwayKristian Martinsen, NorwayHirpa L. Gelgele, NorwayWei D. Solvang, NorwayYi Wang, UKChris Parker, UKJorge M. Fajardo, SpainTorsten Kjellberg, SwedenFumihiko Kimura, JapanGustav J. Olling, USAMichael Wozny, USAByoung K. Choi, KoreaWladimir Bodrow, GermanyGuy Doumeingts, FranceVan Houten, the NetherlandsPeter Bernus, AustraliaJanis Grundspenkis, LatviaGeorge L. Kovacs, HungaryRinaldo Rinaldi, ItalyGaetano Aiello, ItalyRomeo Bandinelli, ItalyYafei He, ChinaJawei Bai, China

Jinhui Yang, ChinaDawei Tu, ChinaMinglun Fang, ChinaBinheng Lu, ChinaXiaoqien Tang, ChinaMing Chen, ChinaXinguo Ming, ChinaKeith C. Chan, ChinaMeiping Wu, ChinaLanzhoung Guo, ChinaXiaojing Wang, ChinaJin Yuan, ChinaYongyi He, ChinaChaodong Li, ChinaCuilian Zhao, ChinaChuanhong Zhou, ChinaJianqing Cao, ChinaYayu Huang, ChinaShirong Ge, ChinaGuijuan Lin, ChinaShanming Luo, ChinaDong Yang, ChinaZumin Wang, ChinaGuohong Dai, ChinaSarbjit Singh, IndiaVishal S. Sharma, IndiaHongjun Ni, ChinaZiqian Zhou, ChinaJianqien Chao, ChinaXifang Zhu, China

viii Organization

Organizing Committee

Guohong Dai (Chair)Xifang Zhu (Chair)Yue ZhangXuedong LiuLin LiuLin Zou

Ziqiang ZhouOdd MyklebustQuan YuJin YuanWanping WuJianqing Cao

Secretariat

Wangping WuJin YuanZiqiang Zhou

Organization ix

x Organization

Contents

Industry 4.0

Industry 4.0 Closed Loop Tolerance EngineeringMaturity Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Kristian Martinsen

A DEMO of Smart Manufacturing for Mass Customization in a Lab . . . 12Jinghui Yang and Timmie Abrahamsson

A Fault Diagnosis Method Based on Mathematical Morphologyfor Bearing Under Multiple Load Conditions . . . . . . . . . . . . . . . . . . . . . 19Yang Ge, Lanzhong Guo, and Yan Dou

An Industry 4.0 Technologies-Driven Warehouse ResourceManagement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Haishu Ma

Collaboration with High-Payload Industrial Robots: Simulationfor Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Beibei Shu and Gabor Sziebig

Depth Image Restoration Using Non-negative MatrixFactorization Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Suolan Liu and Hongyuan Wang

Design and Manufacture of Elevator Model Control System Basedon PLC and HMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Yan Dou, Lanzhong Guo, Yang Ge, and Yuchao Wang

Design of Integrated Information Platform for Smart Ship . . . . . . . . . . 53Guiqin Li, Jinfeng Shi, Qiuyu Zhu, Jian Lan, and Peter Mitrouchev

Development of the Prediction Software for Mechanical Propertiesof Automotive Plastic Materials at High and Low Temperatures . . . . . . 59Guiqin Li, Peng Pan, and Peter Mitrouchev

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Gear Fault Diagnosis Method Based on Feature Fusion and SVM . . . . 65Dashuai Zhu, Lizheng Pan, Shigang She, Xianchuan Shi,and Suolin Duan

HDPS-BPSO Based Predictive Maintenance Scheduling for BacklashError Compensation in a Machining Center . . . . . . . . . . . . . . . . . . . . . 71Zhe Li, Yi Wang, Kesheng Wang, and Jingyue Li

Influence of the Length-Diameter Ratio and the Depth of Liquid Poolin a Bowl on Separation Performance of a Decanter Centrifuge . . . . . . 78Huixin Yuan, Yuheng Zhang, Shuangcheng Fu, and Yusheng Jiang

LSTM Based Prediction and Time-Temperature Varying Rate Fusionfor Hydropower Plant Anomaly Detection: A Case Study . . . . . . . . . . . 86Jin Yuan, Yi Wang, and Kesheng Wang

Wind Turbine System Modelling Using Bond Graph Method . . . . . . . . 95Abdulbasit Mohammed and Hirpa G. Lemu

On Opportunities and Limitations of Additive ManufacturingTechnology for Industry 4.0 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Hirpa G. Lemu

Operator 4.0 – Emerging Job Categories in Manufacturing . . . . . . . . . 114Harald Rødseth, Ragnhild Eleftheriadis, Eirin Lodgaard,and Jon Martin Fordal

Reliability Analysis of Centrifugal Pump Based on SmallSample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Hongfei Zhu, Junfeng Pei, Siyu Wang, Jianjie Di, and Xianru Huang

Research on Horizontal Vibration of Traction Elevator . . . . . . . . . . . . . 131Lanzhong Guo and Xiaomei Jiang

Research on Real-Time Monitoring Technology of EquipmentBased on Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Lilan Liu, Chen Jiang, Zenggui Gao, and Yi Wang

Research on the Relationship Between Sound and Speedof a DC Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Xiliang Zhang, Sujuan Wang, Zhenyu Chen, Zhiwei Shen, Yuxin Zhong,and Jingguan Yang

Review and Analysis of Processing Principles and Applicationsof Self-healing Composite Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Yohannes Regassa, Belete Sirabizuh, and Hirpa G. Lemu

Scattered Parts for Robot Bin-Picking Based on the UniversalV-REP Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168Lin Zhang and Xu Zhang

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Brain Network Analysis Based on Resting State FunctionalMagnetic Resonance Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176Xin Pan, Zhongyi Jiang, Suhong Wang, and Ling Zou

Development of Bicycle Smart Factory and Explorationof Intelligent Manufacturing Talents Cultivation . . . . . . . . . . . . . . . . . . 181Yu’an He

The Journey Towards World Class Maintenance with ProfitLoss Indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192Harald Rødseth, Jon Martin Fordal, and Per Schjølberg

Initiating Industrie 4.0 by Implementing SensorManagement – Improving Operational Availability . . . . . . . . . . . . . . . . 200Jon Martin Fordal, Harald Rødseth, and Per Schjølberg

Manufacturing System and Technologies

A Prediction Method for the Ship Rust Removal Effectof Pre-mixed Abrasive Jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Qing Guo, Shuzhen Yang, Minghui Fang, and Tao Yu

A Review of Dynamic Control of the Rigid-Flexible Macro-MicroManipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218Xuan Gao, Zhenyu Hong, and Dongsheng Zhang

Analysis of Speech Enhancement Algorithm in IndustrialNoise Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226Lilan Liu, Gan Sun, Zenggui Gao, and Yi Wang

Application of Machine Learning Methods for Prediction of PartsQuality in Thermoplastics Injection Molding . . . . . . . . . . . . . . . . . . . . . 237Olga Ogorodnyk, Ole Vidar Lyngstad, Mats Larsen, Kesheng Wang,and Kristian Martinsen

Application of Machine Learning Methods to ImproveDimensional Accuracy in Additive Manufacturing . . . . . . . . . . . . . . . . . 245Ivanna Baturynska, Oleksandr Semeniuta, and Kesheng Wang

Design and Implementation of PCB Detection and ClassificationSystem Based on Machine Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253Zhiwei Shen, Sujuan Wang, Jianfang Dou, and Zimei Tu

Diagnosis of Out-of-Control Signals in Multivariate ManufacturingProcesses with Random Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262Zheng Jian, Beixin Xia, Chen Wang, and Zhaoyang Li

Effect of Processing Parameters on the Relative Densityof AlSi10Mg Processed by Laser Powder Bed Fusion . . . . . . . . . . . . . . 268Even Wilberg Hovig, Håkon Dehli Holm, and Knut Sørby

Contents xiii

Experimental Study on Measuring the Internal Porosity of PlantCanopy by Laser Distance Measuring . . . . . . . . . . . . . . . . . . . . . . . . . . 277Huan Li, Xinghua Liu, Xuemei Liu, and Yang Li

Laser Stripe Matching Based on Multi-layer Refraction Modelin Underwater Laser Scanning System . . . . . . . . . . . . . . . . . . . . . . . . . . 286Jinbo Li, Xu Zhang, Can Zhang, Pingping He, and Dawei Tu

Numerical Simulation of Internal Flow Field in Needle Valve BodyExtrusion Grinding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296Yong Zeng, Shuzhen Yang, Minghui Fang, and Tao Yu

One Dimensional Camera of Line Structured LightProbe Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305Qian Zhan and Xu Zhang

Optimization of Sample Size for Two-Point Diameter Verificationin Coordinate Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Petr Chelishchev and Knut Sørby

Paper Currency Sorting Equipment Based on Rotary Structure . . . . . . 322Lizheng Pan, Dashuai Zhu, Shigang She, Jing Ding, and Zeming Yin

Precision Analysis of the Underwater Laser Scanning Systemto Measure Benthic Organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328Pingping He, Xu Zhang, Jinbo Li, Liangliang Xie, and Dawei Tu

Recognition Algorithm Based on Convolution Neural Networkfor the Mechanical Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Duan Suolin, Yin Congcong, and Liu Maomao

The Research of Three-Dimensional Morphology Recoveryof Image Sequence Based on Focusing Method . . . . . . . . . . . . . . . . . . . 348Qian Zhan

Research on Motion Planning of Seven Degree of FreedomManipulator Based on DDPG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356Li-lan Liu, En-lai Chen, Zeng-gui Gao, and Yi Wang

Research on Straightness Error Evaluation Method Basedon Search Algorithm of Beetle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368Chen Wang, Cong Ren, Baorui Li, Yi Wang, and Kesheng Wang

Production Management

Analysis of Machine Failure Sorting Based on Directed Graphand DEMATEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377Min Ji

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Applying Decision Tree in Food Industry – A Case Study . . . . . . . . . . . 383James Mugridge and Yi Wang

Applying Decision Tree in National Health Service . . . . . . . . . . . . . . . . 389Freddy Youd and Yi Wang

Cognitive Maintenance for High-End Equipment and Manufacturing . . . 394Yi Wang, Kesheng Wang, and Guohong Dai

Decision-Making and Supplier Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . 401Abbie Buchan and Yi Wang

Groups Decision Making Under Uncertain Conditionsin Relation—A Volkswagen Case Study . . . . . . . . . . . . . . . . . . . . . . . . . 406Arran Roddy and Yi Wang

Health Detection System for Skyscrapers . . . . . . . . . . . . . . . . . . . . . . . . 411Lili Kou, Xiaojun Jiang, and Qin Qin

Integrated Production Plan Scheduling for SteelMaking-Continuous Casting-Hot Strip Based on SCMA . . . . . . . . . . . . 418Lilan Liu, Pengfei Sun, Zenggui Gao, and Yi Wang

Knowledge Sharing in Product Development Teams . . . . . . . . . . . . . . . 432Eirin Lodgaard and Kjersti Øverbø Schulte

Multi-site Production Planning in a Fresh FishProduction Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Quan Yu and Jan Ola Strandhagen

Product Design in Food Industry - A McDonald’s Case . . . . . . . . . . . . . 448Polly Dugmore and Yi Wang

Research and Practice of Bilingual Teaching in Fundamentalof Control Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453Wangping Wu and Tongshu Hua

Research on Assembly Line Planning and Simulation Technologyof Vacuum Circuit Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457Wenhua Zhu and Xuqian Zhang

Shop Floor Teams and Motivating Factors forContinuous Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467Eirin Lodgaard and Linda Perez Johannessen

Structural Modelling and Automation of Technological ProcessesWithin Net-Centric Industrial Workshop Based on NetworkMethods of Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Vsevolod Kotlyarov, Igor Chernorutsky, Pavel Drobintsev, Nikita Voinov,and Alexey Tolstoles

Contents xv

Student Learning Information Collection and Analysis SystemBased on Mobile Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Chuanhong Zhou, Chong Zhang, and Chao Dai

Task Modulated Cortical Response During Emotion Regulation:A TMS Evoked Potential Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498Wenjie Li, Yingjie Li, and Dan Cao

The Research on the Framework of Machine Fault Diagnosisin Intelligent Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Min Ji

Utilization of MES System, Enablers and Disablers . . . . . . . . . . . . . . . . 509Inger Gamme and Ragnhild J. Eleftheriadis

Design and Optimization

Application of CNN Deep Learning in Product Design Evaluation . . . . 517Baorui Li, Yi Wang, Kesheng Wang, and Jinghui Yang

An FFT-Based Technique for Underwater Image Stitching . . . . . . . . . . 527Dawei Li, Xu Zhang, and Dawei Tu

An Experimental Study on Dynamic Parameters Identificationof a 3-DOF Flight Simulator Platform . . . . . . . . . . . . . . . . . . . . . . . . . . 536Zhen-Yu Hong, Xuan Gao, Jia-Ren Liu, Dong-Sheng Zhang,and Zhi-Xu Zhang

Analysis of Soil Disturbance Process and Effect by Novel SubsoilerBased on Discrete Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544Jinguang Li, Ziru Niu, Xuemei Liu, and Jin Yuan

Design and Analysis of Drive System of Distributing Machine . . . . . . . . 554Guiqin Li, Xuehong Li, and Peter Mitrouchev

Design and Experimental Study of the SpinachContinuous Harvester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559Yeben Song, Liangliang Zou, Xuemei Liu, and Jin Yuan

Design and Test of End-Effectors of Control System for WhiteAsparagus Selective Harvesting Robot . . . . . . . . . . . . . . . . . . . . . . . . . . 567Baogang Dou, Yang Li, Xuemei Liu, and Jin Yuan

Design of Greenhouse Environmental Monitoring System Basedon Arduino and ZigBee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576Lijuan Shi, Qing Li, and Shengqiang Qian

Design of Marine Elevator Car Frame . . . . . . . . . . . . . . . . . . . . . . . . . . 583Xiaomei Jiang, Lanzhong Guo, and Shuguang Niu

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Dynamic Balance Analysis of Crankshaft Based on Three-Dimensional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592Chuanhong Zhou, Xiaoyu Jiang, and Xiaotong Wang

Effect of Stress Triaxiality on Plastic Deformation and DamageEvolution for Austenite Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601Ying Wang, Jian Peng, and Kaishang Li

Intelligent Fertilization Strategy Based on Integration of Soil Moistureand Forecast Rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608Lin Liu, Yang Li, Ming Hao, Xuemei Liu, Kun Yang, and Jin Yuan

Investigation into Velocity Choice for Determining AerodynamicResistance in Brush Seals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616Yuchi Kang, Meihong Liu, Xiangping Hu, and Jinbin Liu

Key Structure Innovation and Optimization Designof Bucket Elevator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623Fang Ma, Feng Xiong, and Guiqin Li

Multibody System Modelling and Simulation: Case Study onExcavator Manipulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628Yohannes Regassa and Hirpa G. Lemu

Numerical Simulation of the Flow Field Characteristicin a Two-Dimensional Brush Seal Model . . . . . . . . . . . . . . . . . . . . . . . . 636Jinbin Liu, Meihong Liu, Yuchi Kang, and Yongfa Tan

Research on Design Conflict Based on Complex Network . . . . . . . . . . . 643Guiqin Li, Maoheng Zhou, and Peter Mitrouchev

Simulation and Analysis for Overlapping Probabilityof ADS-B 1090ES Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649Dong Zhu, Chengtao Feng, Kaibin Chu, and Zhengwei Zhu

Stress Analysis of Pre-stressed Steel Wire Winding UltrahighPressure Vessels Based on Birth and Death Element Method . . . . . . . . 655Yi Lu and Jie Zhu

Structural Design and Kinematic Analysis of a Weakly Coupled3T Parallel Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663Wei Zhu and Qian Guo

Structure Design and Analysis of Coal Drying Equipment . . . . . . . . . . . 668Xinqi Yu and Zhaoyang Wang

Study on a New Type Cosine Rotator Pump . . . . . . . . . . . . . . . . . . . . . 674Min Zou, Yongqiang Qiao, and Liangcai Wu

Contents xvii

Study on Soil Disturbance Behavior of Globoid Subsoiling ShovelBased on Discrete Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684Zhenbo Xin, Ziru Niu, Xuemei Liu, and Jin Yuan

Study on the Torque of Sleeve Permanent Magnetic Couplings . . . . . . . 694Jian Wu, Xinyong Li, and Lanzhong Guo

The Influence of T Groove Layout on the PerformanceCharacteristic of Cylinder Gas Seal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701Xueliang Wang, Meihong Liu, Xiangping Hu, and Junfeng Sun

xviii Contents

About the Editors

Yi Wang obtained his PhD from Manufacturing Engineering Center, CardiffUniversity, in 2008. He is a Lecturer in Business School, University of Plymouth,UK. Previously, he worked in the Department of Computer Science, University ofSouthampton, and at the Business School, Nottingham Trent University. He holdsvarious visiting lectureships in several universities worldwide. He has specialresearch interests in supply chain management, logistics, operation management,culture management, information systems, game theory, data analysis, semanticsand ontology analysis, and neuromarketing. He has published 75 technicalpeer-reviewed papers in international journals and conferences. He co-authored twobooks: Operations Management for Business and Data Mining for Zero-defectManufacturing. He also authors one new book: Intelligent Fashion Supply Chain.

Kesheng Wang holds a PhD in Production Engineering from the NorwegianUniversity of Science and Technology (NTNU), Norway. Since 1993, he has beenappointed Professor in the Department of Mechanical and Industrial Engineering,NTNU. He is a Director of the Knowledge Discovery Laboratory (KDL) at NTNUat present. He is also an Active Researcher and serves as a Technical Adviser inSINTEF. He was an Elected Member of the Norwegian Academy of TechnologicalSciences in 2006. He has published 21 books, 10, and over 270 technicalpeer-reviewed papers in international journals and conferences. His current areas ofinterest are intelligent manufacturing systems, applied computational intelligence,data mining and knowledge discovery, swarm intelligence, condition-based moni-toring and structured light systems for 3D measurements and RFID, predictivemaintenance, and Industry 4.0.

Jan Ola Strandhagen is a Research Director of the research center SFI Norman atSINTEF. He is also a Professor in the Department of Mechanical and IndustrialEngineering, Norwegian University of Science and Technology (NTNU). He holdsa PhD in Production Engineering from NTNU (1994). His research interests havefocused on production management and control, logistics, manufacturing

xix

economics and strategies. He has managed and performed R & D projects in closecollaboration with a wide variety of Norwegian companies and participated as aresearcher and a project manager in several European projects.

Tao Yu is the President of Shanghai Second Polytechnic University (SSPU), China,and a Professor of Shanghai University (SHU). He received his PhD from SHU in1997. He is a Member of the Group of Shanghai manufacturing information and aCommittee Member of the International Federation for Information Processing (IFIPTC5). He is also an Executive Vice President of Shanghai Science VolunteerAssociation and an Executive Director of Shanghai Science and Art Institute ofExecution. He managed and performed about 20 national, Shanghai, enterprisescommissioned projects. He has published hundreds of academic papers, of whichabout thirty were indexed by SCI, EI. His research interests are mechatronics,computer-integrated manufacturing system (CIMS), and grid manufacturing.

xx About the Editors

Industry 4.0

Industry 4.0 Closed Loop ToleranceEngineering Maturity Evaluation

Kristian Martinsen(&)

Department for Manufacturing and Civil Engineering,Teknologivn 22, 2815 Gjøvik, NorwayKristian.martinsen@ntnu.no

Abstract. Closed Loop Tolerance Engineering (CLTE) is introduced as amodel of information flow – feed forward and feedback- between functionalrequirements, tolerance selection, process capabilities and product performance.“Industry 4.0” and “Cyber physical manufacturing systems” opens new poten-tials for information and data exchange along variation management activities,when developing, producing and manufacturing products. This paper describes amethod for evaluation of the maturity level of the CLTE data and informationexchange. The method is based on and validated through empirical findingsfrom field studies in a number of manufacturing companies.

Keywords: Tolerancing � Quality assurance � Digital manufacturing systemClosed loop tolerance engineering

1 Introduction

1.1 Tolerances and Tolerance Engineering

Tolerances are defined in order to limit components and products geometry and toensure interchangeability, quality and function according to the customer demands. Theselected tolerances will usually also impact manufacturing and inspection processesand thus manufacturing costs. In spite of the increasing ability to assess processcapabilities and other data and the increasing number of design software; tolerances arestill often determined with lacking insight. This may lead to inappropriate tolerances.Too tight tolerances “to be on the safe side” regarding assembly and product functionand insufficient tolerance distribution are typical errors. Geometry features having anover-qualified manufacturing process are potentially more expensive than necessary.On the other hand, will under-qualified processes lead to problems to meet the qualityrequirements without sorting or other measures.

Literature reports many examples on this; Zhang (1997) [1] states that “many partsand products are certainly over-toleranced or haphazardly toleranced, with pre-dictable consequences”. Singh [2] point at the negative effects of inappropriate toler-ances of increased cost and lacking product quality. Ali et al. [3] and Krogstie andMartinsen [4] point at the costs and efforts to change tolerances at a later stage. Addingto this is a seemingly lack of attention to tolerance engineering. As Watts [5] states;“all industry is suffering, often unknowingly, of the lack of adequate academic

© Springer Nature Singapore Pte Ltd. 2019K. Wang et al. (Eds.): IWAMA 2018, LNEE 484, pp. 3–11, 2019.https://doi.org/10.1007/978-981-13-2375-1_1

attention on tolerances”. He claims tolerancing has “gradually been removed from thecurriculum at universities and has been replaced by other product developmentcourses”. Oddly enough have popular management paradigms that originates fromquality and variation control such as TQM, Six Sigma and Lean a lacking attention totolerancing. The focus is mainly on management [4]. “Not only has tolerances lowexplicit attention within industry, academia and product development literature;managers are lacking tools to address tolerancing activities” [4]. Tolerancing has been“kept in a high degree of technical focus” with focus on norms and standards [6, 7].

There are many different product development methodologies and approacheswhere tolerances and variation management are addressed, such as Robust design [8]and Design for Manufacturing (or DfX) [9, 10]. A comprehensive listing of models andmanagement control of product development shows, however, that tolerancing is notaddressed in many other approaches [11–14].

1.2 Closed Loop Tolerance Engineering

Krogstie and Martinsen have developed a conceptual model of Closed Loop ToleranceEngineering (CLTE) [15]. CLTE (Fig. 1) is a model for “systematic and continuous re-use and understanding of product-related knowledge, with the aim of designing robustproducts and processes with the appropriate limits of specifications”. CLTE sees tol-erancing as activities not limited to the traditional activities of tolerance-specification,allocation, modelling/optimization and synthesis, but also an organizational process,with information flow and ability to collect, use and reuse data. Prevent problems formoccurring, attention to and understanding of tolerances in the whole value chain, factbased tolerance engineering are some benefits expected. Good tolerance engineeringpractice includes a collective ability to detect critical situations in the product devel-opment phase [16]. A critical situation is the decision-making between a desirable ornegative consequence in the future. CLTE is distinguished from other approaches byrepresenting the “skilled knowledge-based collaboration with a specific focus on theimportance of defining appropriate tolerances”. CLTE has been applied for analyzingtolerance engineering practices in different companies, including a high-precisionaerospace company [17].

Fig. 1. The CLTE-model [15]

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The CLTE - model has a “feed forward” and a feed-back” information flowdimension. It contains four interconnected activities: 1 - Defining functional require-ments, 2 - Defining tolerances, 3 - Considering of production capabilities and 4 -Confirmation of functional performance. Furthermore, six pairs of closed loops ofrelations; 1a/b etc., see Fig. 1. The closed loop relations links activities togetherpassing information forward in the project flow, as well as to the predecessors in thefeed-back dimension. The ability to prepare and utilise information and data from bothfeed forward and feed-back dimension is the main key element. The need for cross-functional teams for product and process development is a well-known concept [18]and the proposed CLTE is a cross-functional.

2 Industry 4.0 CLTE

Industry 4.0 is a strategy for implementing the so called 4th industrial revolution, and acentral concept is Cyber-Physical Manufacturing systems [19] where the physical andthe virtual processes are providing simultaneous data-accessing and processing.Machine learning/Artificial intelligence, sensor based monitoring and control, multi-agent/holonic systems, (wireless) sensor networks, (big) data mining,virtual/augmented reality etc. are some technologies that are mentioned. Better con-nectivity, productivity, efficiency, information flow, robustness, flexibility are some ofthe expectations to Industry 4.0.

There are a growing number of scientific articles on Tolerance Engineering inIndustry 4.0. One example is Gianetti [21], who suggests a framework for processrobustness, improving process robustness with quantification of uncertainties inIndustry 4.0. She proposes to use big data analysis to find “Likelihood Ratios” forprocess capabilities used to set robust tolerance limits. Another is Söderberg et al. [22]discussing how a digital twin with “geometry representation of the assembly, kinematicrelations, FEA functionality, Monte Carlo simulation, material properties and link toinspection data base”. One might also argue that the vast number of articles onComputer Aided Tolerancing (including CIRPs own conference track) really are part ofthe essence of Industry 4.0- although the term “Industry 4.0” is newly “invented” [23–28] (Fig. 2).

The Acatech study Industrie 4.0 Maturity Index [20] defines 6 steps from “Digi-talisation” to “Industrie 4.0”. Stage one and two are more or less current industrystatus. Stage three to six represent different steps from seeing, to understanding, topredicting what will happen and finally autonomous response. Collecting and dis-playing data, (out of the “silos” and useful across the company), up-to–date models atall times, simulations, optimisation, and ultimately autonomy (response without humanassistance) are key competencies. The Acatec steps of Industry 4.0, four last (Industrie4.0) stages could mean the following for CLTE;

1. Visibility – what is happening; Instant and constant data collection and visualisa-tion along the CLTE model. A “digital shadow” (or twin) across data silos withsemantic linking of useful data for tolerancing (see also [22]).

Industry 4.0 Closed Loop Tolerance Engineering 5

2. Transparency – why is it happening; Ability to analyse and present data in a usefulway for potential users along the CLTE

3. Prediction – what will happen; Ability to use the data for simulations and opti-misations at all levels

4. Adaptability – Autonomous response –self-optimising CLTE without humanintervention. The ultimate goal for CLTE would be to have an instant and auton-omous flow of information and data across the CLTE chain “translating” infor-mation to adapt to the specific use and suggest decisions for the user. This levelwould mean that the product designer e.g. automatically gets relevant processcapabilities and suggested optimised tolerance distribution and process path as anautomated relation in the CLTE model.

3 CLTE Maturity Assessment Model

Based on the CLTE the author is here proposing a maturity assessment model CLTE.This maturity model was developed as a combination of literature study, discussionwith industry partners, and case studies in selected Norwegian companies, mainly inthe SFI Manufacturing research centre.

The CLTE maturity assessment can be used to evaluate and plan improvements in acompany regarding their management of tolerancing and variations. The model consistsof two parts; first part is assessing how well the company is performing in the 12relation loops. Secondly how information flow in the relation loops and how data isstored, assessed and used. Both can be done as self-assessment by the company and byan external expert. It would be recommended to do both followed by a reflectionworkshop with discussions on actions to improve.

Fig. 2. Actech Industrie 4.0 maturity index [20]

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3.1 Performance Assessment

The performance assessment is done by grading the company performance according toquestions given regarding the performance on the CLTE relation loops (1a–6a, 1b–6b).Grades are from (1) not applied, (2) poor, (3) medium, (4) good and (5) excellent. Thelist underneath is a simplified/summary of the questions given.1a How well are functional requirements transformed to tolerancing – by whom, in

which form and which tools?1b How well are the decision basis for selected tolerances stored and fed back to aid

functional requirements description in following projects?2a How well do the tolerances fit the manufacturing capabilities? How well are

tolerance stack-up [29], critical tolerances and reference surfaces working?2b Howwell are existing process capabilities used in tolerancing?Howwell are quality

and productivity data on current products used in tolerancing of new products?3a Are process capabilities and parameters and their effect on product performance

and inspection well known?3b Are sources of variation in product performance well understood? Is knowledge

gained in product performance tests looped back tomanufacturing? Can variation inproduct performance be traced back to variation in the manufacturing processes?

4a How are functional requirements information used in manufacturing? Are criticalparameters known and manufacturing and inspection processes sufficientlyattended?

4b How well are process capabilities fed back (and make an influence) on functionalrequirements?

5a How well are the relations between (critical) tolerances and the productperformance understood? How are defined tolerances deciding product perfor-mance assessment?

5b How well are critical tolerances and their variation influence on the productperformance understood?

6a How satisfactory is the product performance according to the functionalrequirements? How are functional requirements influencing the product perfor-mance assessment?

6b To what extent is existing product performance fed back to aid definition offunctional requirements in following projects?

The results can be shown as spider diagrams comparing the company assessment,the expert(s) assessment and the wanted future scenario/goal.

3.2 Information and Data Exchange Assessment

The second part of the maturity assessment is the information and data exchangeassessment. For each relations loop (1a to 6a, and 1b to 6b) the company must agree onwhich stage they are (and wish to be):Stage 1: No organised information exchanges.Stage 2: Information exchange based on expert’s subjective opinions. Cross-

functional teams using semi-quantitative tools such as FMEA.

Industry 4.0 Closed Loop Tolerance Engineering 7

Stage 3: Information exchange containing real data on ad-hoc basis. Time-consuming data processing and analysis using highly qualified personnel.Unknown or weak data quality with little to no meta data. Data mainly forinternal use. Some use of computer-aided decision support.

Stage 4: Systemized (but manual) regular data and information exchange, analysisand computer-aided decision support. Data management with a broader usein mind. Meta data and cross-linked data, but still manual translation ofinformation to adapt to the use. Generally good data quality and ability toassess and grade data quality.

Stage 5: Instant and autonomous data exchange. Automatic translation of data andinformation to adapt to the specific user. Automated data processing,simulation and optimisation and suggested decisions. Automated assess-ment, filtering and signal processing for maximum data quality.

3.3 Examples from Industry Case Study

The charts underneath show the results from one industry case study. It is a globalleading company in a specific niche as a Tier 1 automotive supplier. They own their ownproduct patents and are developing, manufacturing and assembling a complete range ofproducts within their niche. The assessment was made in two workshops separated by anexpert mapping and analysis. The expert assessment is based on semi-structuredinterviews, observations and analyses of a few selected products (Figs. 3 and 4).

The charts show a typical picture where the feed forward (1a to 6a) are moreadvanced than the feed-back loops (1b to 6b). Similar results can be found in othercompanies. There are some deviations on the expert vs. company self-evaluation.This is not untypical; the companies are in some cases more “hard” on themselves on

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3a

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performance

Fig. 3. Relation Performance assessment

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Fig. 4. Information flow assessment

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the self-assessment than the expert’s assessment. The relation performance goals are inthis case somewhat ambitious based on the current grade, but it is long-term goalswhere the company mean they have to be.

4 Discussions

4.1 Discussion on the CTLE Maturity Assessment Model

The maturity assessment model is a semi-quantitative model useful as a tool to map acompany and to point at possible improvements. This is not an exact numerical modeland any comparisons between different companies should be done with care. The casecompanies are all measuring and storing large quantities of data in product tests,numerical models etc. in product development, productivity and capability/variabilityin manufacturing, product geometry in inspection processes and measurement of actualproduct performance. The data material is, however, usually stored for a specific useand to transfer the data and extract information for use by other departments is cur-rently difficult. For example, are all data form statistical process control stored, but totranslate these charts to process capabilities and make it easy usable for the productdesigners and tolerance definition is still not straight forward. This is one of theobstacles the Industry 4.0 paradigm should solve. Stage 5 in the information and dataexchange assessment is currently not reachable for most companies. A key to this willbe a seamless interconnection of Manufacturing Execution Systems (MES), ProductLifecycle Management (PLM), Computer Aided Engineering, including ComputerAided Tolerance Engineering software.

4.2 Shortcomings of the CTLE Model – Future Extensions

The CLTE model is currently focusing on the process within one company. Futuremodels must include tolerancing and variation management in the supplier vs. cus-tomer relations in the supply-and distribution chain. Furthermore; future CLTE modelsshould include information and data exchange with product use phase and end-of-lifeand possibly remanufacturing of products. One of the current trends are the manu-facturers liability of products after end-of-life (EOL) as well as the extension of theproduct to a product-service system. Products containing sensors opens for newbusiness models, but the data collected could also be used for future CLTE activities,such as functional requirement definitions. Tolerance engineering and variation man-agement will also be vital for a circular manufacturing with increasing remanufacturingof products and components rather than re-melting or disposal at the EOL.

5 Conclusions and Further Work

Industry 4.0 will most likely open new opportunities for information flow, dataassessment and exchange for variation management and tolerancing engineering. Thispaper has suggested a maturity model that can be used to lift tolerancing on the agenda

Industry 4.0 Closed Loop Tolerance Engineering 9

and point at improvement potentials for companies. This is a proposal for a manage-ment tool. Further work would see longitudinal results from industries using the toolwith measured improvements.

Acknowledgements. The author wishes to thank the discussion partners and the case studycompanies. The work reported in this paper was based on activities within the centre for researchbased innovation, SFI Manufacturing in Norway, and is partially funded by the Research Councilof Norway under contract number 237900.

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