Esnek imalat hücrelerinin dijital ikiz modellemesi = Digital twin modeling of flexible manufacturing cells

Abstract

Hücresel üretim, malzeme taşıma, bekleme süreleri ve envanteri azaltmak için makineleri, süreçleri ve iş istasyonlarını bağımsız hücrelerde gruplandırmayı içeren bir yalın üretim yaklaşımıdır. Hücresel üretim tesislerinin yerleşim tasarımı, verimlilikleri, üretkenlikleri ve çıktıları üzerinde önemli bir etkiye sahiptir. Ancak, tesis yerleşim tasarımı için geleneksel deneme yanılma yöntemleri zaman alıcı, maliyetli ve riskli olabilir. Dijital ikiz benzetimi, tasarımcıların tesisin sanal modellerini oluşturmasına ve farklı yerleşim düzenlerini fiziksel olarak uygulamadan önce test etmesine olanak tanıyarak güçlü bir alternatif sunar. Hücresel üretimin kritik bir yönü, üretkenliği, kaliteyi ve genel performansı önemli ölçüde etkileyebilecek tesisin yerleşim tasarımıdır. Bu yazıda, bir hücresel üretim tesisinin yerleşim tasarımını optimize etmek için dijital ikiz benzetimi kullanmayı öneriyoruz. Çalışmamız, dijital ikiz benzetiminin üretkenliği en üst düzeye çıkaran, israfı en aza indiren ve malzeme ve ürünlerin sorunsuz akışını sağlayan en uygun yerleşim tasarımını belirlemeye yardımcı olabileceğini gösteriyor. Ayrıca, üretim sistemindeki değişikliklerin tesisin performansı üzerindeki etkisini değerlendirmemizi sağlayarak karar vericilerin bilgiye dayalı kararlar almasına yardımcı olur. Çalışma ile birlikte Bilgisayar destekli tasarım aşamasına sembolik olarak giriş yapılmış ancak detaylandırma ve iyileştirme oranlarına sonraki çalışma ile değinilecektir. Tasarımda iyileştirme bu yazı içerisinde ortaklaştırma ile yansıtılmıştır. Yazı içerisinde ele alınan bir bölüm içerisinde malzeme kullanımı, ara ürün kullanımı, hat kullanımı tasarıma bağlı ortaklaştırılmıştır. Genel olarak, bu makale, hücresel üretim tesisi yerleşim tasarımını optimize etmek ve operasyonel performansı iyileştirmek için güçlü bir araç olarak dijital ikiz benzetimlerinin potansiyelini vurgulamaktadır. Bu yazıda, bir hücresel üretim tesisinin yerleşim tasarımını optimize etmek için dijital ikiz benzetiminin kullanımına ilişkin bir vaka çalışması sunuyoruz. Ekipman ve makinelerin ayrıntılı 3B modellerinin yanı sıra hücrelerin yerleşimini içeren tesisin dijital ikiz modelini oluşturma sürecini açıklıyoruz. Birden fazla senaryo üzerinden kontrol edilen bu model ile detaylı verim oranı eldesi yapıyoruz. Daha sonra, farklı düzen yapılandırmalarını simüle etmek ve bunların döngü süresi, verim ve ekipman kullanımı gibi temel performans göstergeleri üzerindeki etkilerini değerlendirmek için dijital ikizi kullanıyoruz.

Cellular manufacturing is a lean manufacturing approach that involves grouping machines, processes, and workstations into individual cells to reduce material handling, waiting times, and inventory. Layout design of cellular manufacturing plants has a significant impact on their efficiency, productivity and output. However, traditional trial and error methods for facility layout design can be time consuming, costly and risky. The digital twin simulation provides a powerful alternative, allowing designers to create virtual models of the facility and test different layouts before physically implementing them. A critical aspect of cellular manufacturing is the layout design of the plant, which can significantly affect productivity, quality, and overall performance. In this paper, we propose to use a digital twin simulation to optimize the layout design of a cellular manufacturing plant. Our study shows that digital twin simulation can help determine the optimal layout design that maximizes productivity, minimizes waste, and ensures a smooth flow of materials and products. It also helps decision makers make informed decisions by enabling us to evaluate the impact of changes in the production system on the plant's performance. This prevents error, waste and helps to achieve gains in indices such as cost, time and quality. With the study, the computer-aided design phase has been entered symbolically, but the elaboration and improvement rates will be mentioned in the next study. In addition, the information can be improved in future studies or by detailing this study. Improvement in design is reflected in this article with commonization. In a section discussed in the article, the use of materials, the use of intermediate products, the use of lines are combined depending on the design. Overall, this article highlights the potential of digital twin simulations as a powerful tool to optimize cellular plant layout design and improve operational performance. In this article, we present a case study of the use of digital twin simulation to optimize the layout design of a cellular manufacturing plant. The symbolic structures used are detailed in the text. We describe the process of creating a digital twin model of the facility, which includes detailed 3D models of equipment and machinery, as well as the layout of cells. With this model, which is controlled over more than one scenario, we obtain a detailed yield rate. These scenarios give us an idea to get the most efficient results by using the maximum amount of cost and time. It makes it possible to get answers as soon as possible by shelving the old and uncontrolled processes called trial and error. We then use the digital twin to simulate different layout configurations and evaluate their impact on key performance indicators such as cycle time, throughput and equipment utilization. The main reason that distinguishes the digital twin from other simulation programs and makes it our choice is that it can be intervened instantly and can directly transfer data with the help of sensors after it is implemented. It can receive program data instantly and reflect the instant situation to us. It removes doubts about data accuracyand helps us optimize production lines, strengthen team collaboration and financial decision-making through precise results. In the thesis, first of all, the existing system is analyzed. For this, we have carefully and correctly transferred all the data in the system within the program. Then we started the process by entering the reference time timer for the system process. We instantly monitored the situation with the tables we created taking into account the stations. However, it is possible to receive instantaneous data during the process. At the end of the process, we clearly found where the problem occurred, together with the reports and tables. Then we focused on two possible scenarios. We made the improvements that could be made in the design phase before and supported it with visuals in the article. We have updated 2 scenarios in the program with all their details. As a result of the comparisons made with the results, we took the scenario where the yield rate improved and compared it with the current situation. The first comparison is to select the highly efficient one among the scenario alternatives, the second comparison is to see the improvement between the current situation and the improved situation. In detail, comparisons can be made for all stations one by one over different report outputs. This gives us the opportunity to interpret not only on a single subject, but at every moment of all processes. In the improved situation, there may be situations with a lower yield rate than the current situation. these are issues that can be improved later. However, if the yield rate is improved in general, it is accepted. Flexible production cell selection was made in both scenarios. However, the transition to the Single flex cell actually solved all the problems instead of creating problems as envisaged. In addition, the remaining regions from other lines can be converted into necessary areas such as stock areas, new production areas, offices. It can be reflected as a different efficiency. It is a structure that supports collaborative for efficiency by changing the flexible manufacturing cell design index. Within the study, the flexible manufacturing cell transformation was carried out, and the previously used areas were no longer used, and the gain was achieved. It maximizes space management by making the best use of the available space. This support is mentioned in this article. What should be considered in the efficiency ratio comparison is the distribution of its own situation and the importance of its location on the station. Stations that cause problems are seen in reports and graphs just before stations with high blocked rates. After the determination of the problem, the point for intervention is determined. Intervention points are determined in this way and these points are concentrated for improvement. The scenarios chosen for process improvement were chosen over the scenarios that were considered to be the most likely based on production experience. In fact, contrary to what the second scenario seems, it is seen that it reduces the yield rate, reduces the number of production and results in a worse result. Buddha clearly emphasizes and supports the importance of using digital twins. It is possible to achieve better or worse results with different scenarios and different placements. Some issues, such as increased usage area, cannot be found mathematically, but as stated, efficiency increase has been achieved in terms of area gain. Outputs based on mathematical value are the determining factor in the desired issues. Percentage distributions are at the forefront of the issues considered in thereport outputs. This is another result shown in the scenarios. In addition, the net increase in the number of products produced completely indicates the positivity of the result. When the whole article is considered, it is seen that the result is positive, there is a serious improvement in the increase in yield rates, the structure can be monitored in a simpler way and it has a structure that allows instant intervention. The flexible manufacturing cell, the Digital twin, is an indication that the predicted benefit for the PLM structure has been achieved and demonstrated.