Quality costs and Industry 4.0: inspection strategy modelling and reviewing

被引:0
|
作者
Reis, Angelica Muffato [1 ]
Dall-Orsoletta, Alaize [1 ]
Nunes, Eusebio [1 ]
Costa, Lino [1 ]
Sousa, Sergio [1 ]
机构
[1] Univ Minho, ALGORITMI Res Ctr, P-4800058 Guimaraes, Portugal
关键词
Continuous improvement; Cost of quality; Industry; 4.0; Quality control; Simulation; OF-QUALITY; OPTIMIZATION; MANAGEMENT; SIMULATION; FRAMEWORK; DESIGN;
D O I
10.1007/s00170-024-13184-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Inspection strategy (IS) is a key component impacting quality costs. Although often considered an inflexible output of initial quality plans, it may require revisions given the dynamic quality situation of the manufacturing system. It is from this background that the present study aims to model and compare different IS based on the cost of quality (CoQ) approach for a case study in the automotive manufacturing industry. While many computational inspection strategy models (ISMs) are available in the literature, most of them face application challenges and struggle to incorporate real-world data. The present study addresses this gap by developing a model that not only represents a real testing station in a manufacturing line but also uses historical production data. Additionally, in relation to model inputs, this study explores the challenges and opportunities of acquiring reliable quality cost estimates in the Industry 4.0 context. Among the main contributions of this work, the developed CoQ-based ISM can be used as a decision-making aiding tool for inspection revision and improvement, while conclusions about quality cost data collection in the industrial digitalization context can help advance the CoQ approach in practice.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] INDUSTRY 4.0 FOR ADVANCED INSPECTION INDUSTRY 4.0 FOR ADVANCED INSPECTION
    Vaga, Ragnar
    Bryant, Keith
    [J]. 2019 PAN PACIFIC MICROELECTRONICS SYMPOSIUM (PAN PACIFIC), 2019,
  • [2] Towards a Comprehensive Visual Quality Inspection for Industry 4.0
    Rozanec, Joze M.
    Zajec, Patrik
    Trajkova, Elena
    Sircelj, Beno
    Brecelj, Bor
    Novalija, Inna
    Dam, Paulien
    Fortuna, Blaz
    Mladenic, Dunja
    [J]. IFAC PAPERSONLINE, 2022, 55 (10): : 690 - 695
  • [3] An industry 4.0 framework for the quality inspection in gearboxes production
    Cicconi, Paolo
    Raffaeli, Roberto
    [J]. Computer-Aided Design and Applications, 2020, 17 (04): : 813 - 824
  • [4] Multi-Layer Quality Inspection System Framework for Industry 4.0
    Azamfirei, Victor
    Granlund, Anna
    Lagrosen, Yvonne
    [J]. INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2021, 15 (05) : 641 - 650
  • [5] A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0
    Rydzi, Stefan
    Zahradnikova, Barbora
    Sutova, Zuzana
    Ravas, Matus
    Hornacek, Dominik
    Tanuska, Pavol
    [J]. SENSORS, 2024, 24 (17)
  • [6] Reviewing Digital Manufacturing concept in the Industry 4.0 paradigm
    Dener Ribeiro da Silva, Elias Hans
    Shinohara, Ana Carolina
    de Lima, Edson Pinheiro
    Angelis, Jannis
    Machado, Carla Goncalves
    [J]. 52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS), 2019, 81 : 240 - 245
  • [7] The evolution of quality: from inspection to quality 4.0
    Broday, Evandro Eduardo
    [J]. INTERNATIONAL JOURNAL OF QUALITY AND SERVICE SCIENCES, 2022, 14 (03) : 368 - 382
  • [8] A cybersecurity strategy for Industry 4.0
    Barrios, Rita M.
    Schippers, David
    Heiden, Christopher
    Pappas, George
    [J]. AUTONOMOUS SYSTEMS: SENSORS, PROCESSING, AND SECURITY FOR VEHICLES AND INFRASTRUCTURE 2019, 2019, 11009
  • [9] Perspectives for the Strategy Industry 4.0
    Avramov, Josif
    [J]. 2021 29TH NATIONAL CONFERENCE WITH INTERNATIONAL PARTICIPATION (TELECOM), 2021, : 167 - 170
  • [10] Effect of Industry 4.0 technologies adoption on the learning process of workers in a quality inspection operation
    Tortorella, Guilherme Luz
    Anzanello, Michel J.
    Fogliatto, Flavio S.
    Antony, Jiju
    Nascimento, Daniel
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2023, 61 (22) : 7592 - 7607