افزایش سریع سیستم های تولید قابل اجرا در صنعت 4/0
ترجمه نشده

افزایش سریع سیستم های تولید قابل اجرا در صنعت 4/0

عنوان فارسی مقاله: یک سیستم پشتیبانی تصمیم برای افزایش سریع سیستم های تولید قابل اجرا در صنعت 4/0
عنوان انگلیسی مقاله: A Decision Support System for rapid ramp-up of industry 4.0 enabled production systems
مجله/کنفرانس: کامپیوترها در صنعت - Computers In Industry
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: مدیریت صنعتی، تولید و عملیات، مدیریت پروژه، مدیریت عملکرد، سیستم های اطلاعاتی پیشرفته
کلمات کلیدی فارسی: سیستم فیزیکی سایبری، سیستم پشتیبانی تصمیم، صنعت 4/0، تولید هوشمند، افزایش نرخ تولید
کلمات کلیدی انگلیسی: Cyber-Physical System، Decision Support System، Industry 4.0، Intelligent manufacturing، Production ramp-up
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.compind.2020.103190
دانشگاه: Information and Technologies Institute, Centre for Research and Technology Hellas, 57001, Thessaloniki, Greece
صفحات مقاله انگلیسی: 14
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 7/122 در سال 2019
شاخص H_index: 87 در سال 2020
شاخص SJR: 1/242 در سال 2019
شناسه ISSN: 0166-3615
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14334
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Related works

3- Ramp-up Decision Support System

4- Experimental procedure and results

5- Discussion

6- Conclusions

References

بخشی از مقاله (انگلیسی)

Abstract

Production ramp-up is a key phase during the introduction or changeover of a production system. Process calibration and tuning are inevitably required to make such a system fully operational and let it reach its maximum production yield. A complex decision-making process takes place in order to optimally tune the system and requires a long time for testing and experimenting that will determine the system behaviour. This work considers the sequential nature of ramp-up and proposes a Cyber-Physical Systems approach based on data capturing, learning mechanisms and knowledge extraction, leading to an Industry 4.0 compliant Decision Support System (DSS) for human operators. The proposed system is implemented as an online DSS and also supports offline learning using previously gathered knowledge. A number of experiments have been carried out on a micro scale assembly station, validating the expected benefits of the proposed DSS. Results show a reduction of over 40% in the number of ramp-up steps required when using the DSS.

Introduction

Nowadays, production systems (PSs) are subject to rapid technological changes, which led to very short product lifecycles and high variety of products, to meet the growing demand of product customization (Kalir and Rozen, 2018). In addition to that, PSs becomes more modular and able to change from one configuration to another (Colledani et al., 2018). The rapid introduction of new products, mostly complex, and changeover cause a prolonged and more frequent production ramp-up (Letmathe and Rößler, 2019). Ramp-up phase is very important for PSs because having high outputs with good quality and fast cycle time are the key factors for increasing profits, where at the beginning of product life-cycle product prices are the heights and competition is scarce (Kalir and Rozen, 2018). Production ramp-up is a decision-making process where human experts decide on the best actions to fine-tune the process. It is a highly complex parameter tuning process with many interrelated factors leading to a well-defined goal. Studies on ramp-up report long lead times of up to a few months from the initial sys-tem built to full production (Terwiesch and Bohn, 2001). Since the product, PS, and the supply chain are new, uncertainty is very high, making the ramp-up process very difficult to manage (Hansen and Grunow, 2015). Also, ramp-up process is unstable, expressed in the unpredictability of the system at this stage, which makes planning proactive steps to avoid some problems ineffective for many ramp-up problems (Schuh et al., 2015).