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Projects Accepted in the Field of Intelligent Production Systems

TOFAŞ - Turkish Automobile Factory Project

Project Title: Image Processing and Analysis of Movements and Activities in the Production Environment Project

Customer Institution: Tofaş Turkish Automobile Factory Inc.

Project Manager: Cute Sixok

Consortium Members:  



The project, which will be carried out in the thematic field of smart production, aims to increase efficiency in the mass production line by using artificial intelligence technology. By analyzing the movements of the employees with artificial intelligence, high and low efficiency jobs in production will be detected. In this way, business results will emerge that will increase the total efficiency in the production line. Thanks to the project, the accuracy analysis of the work done in the operation will be made. The analysis of stopping and waiting losses in the production of different vehicles and shift changes will be determined by artificial intelligence. It is aimed to install cameras on the assembly line in the system to be developed in order to realize the project objectives. Based on the images obtained from the cameras, it is planned to make analyzes that will increase work efficiency with artificial intelligence, thanks to the R&D studies to be carried out in the field of deep learning methods.

With this innovative approach in the field of business analysis, it is aimed to offer solutions to increase business efficiency. The project results are exemplary and have a high potential for dissemination.

Şişecam Project

Project Title: Increasing the Production Efficiency of Nano Coating in a Wide Area with the Support of Artificial Intelligence

Customer Institution: Turkey Şişe ve Cam (Bottle and Glass) Factories Inc.

Project Manager: Utku Er

Consortium Members:  



In the project, it is aimed to increase the production efficiency with the artificial intelligence supported decision support system in the glass coating lines in the magnetic field. In this production process, each material is coated as a single layer and measurements are taken before the formation of multiple layers on flat glasses in the magnetic field splash glass coating lines, and it is aimed to achieve the desired target values with high precision (coating thickness homogeneity sensitivity). In line with the results obtained during the production of the single layers, the changes to be made in the process parameters are decided based on the experience of the device operator. Therefore, the operator spends an average of 1-2 hours for the production of a single layer of each material by doing many repetitions (in the range of 5-10). This situation reduces the final product production efficiency.

The main purpose of the project is to produce single layers with a thickness homogeneity tolerance of 1% in maximum 2 repetitions (in less time than the standard method) with artificial intelligence/machine learning.

In the project, the setting process of the silicon nitride (Si3N4) single layer will be made independent of the operator, thereby increasing the production efficiency. In this context, by providing decision support to the operator with the support of artificial intelligence and/or predicting the trim gas flow adjustment, Si3N4 single layer optimization in Şişecam's wide-area production processes will shorten the production transition time and prevent consumable cost losses. With the artificial intelligence/machine learning algorithms and optimization model to be developed in cooperation with the National Defense University and SGE Engineering in the project, the production data that Şişecam monitors and controls in the production processes will be collected and analyzed, and the adjustment parameters that affect the coating homogeneity in the production will be determined without the need for personnel decision. With the communication technology to be developed by SGE Engineering, data in Şişecam coating production systems will be collected and processed and machine communication will be provided.

Polat Enerji Project

Project Title: Artificial Learning Based Decision Support System for Predictive Maintenance Activities in Wind Turbines Using Direct Drive Technology

Customer Institution: Polat Enerji Industry and Trade Inc.

Project Manager: Emre Vardareli

Consortium Members:  



Bu proje, Türkiye’de direct drive (dişli kutusuz) teknolojisine sahip rüzgâr türbinleriyle enerji üreten sahaların verimlilik ve bakım planlama ihtiyaçlarına veri tabanlı yapay zekâ algoritmaları ile çözüm geliştirmeyi amaçlamaktadır. Bu çözüm, rüzgâr santrallerindeki ekipman ve süreçlerde oluşabilecek anormalliklerin önceden tespiti ve teşhisi ile istikrarlı üretime yardımcı olacaktır. Böylece temiz enerji yatırımlarının kârlılığının artmasına yardımcı olacak ve bu alana yapılacak yatırımları teşvik edecektir. Projenin amacı üç basamaktan oluşmaktadır:

  1. By developing artificial intelligence solutions, to determine the relationship of SCADA data collected from Enercon brand direct drive (gearless) wind turbines with each other and with the faults that cause the turbine to stop.
  2. With a predictive maintenance approach, reducing turbine downtimes due to possible failures, shifting turbine downtimes to periods of lower production or no production, instead of periods with high wind speed with significant production.
  3. Thus, converting more of the kinetic energy potential hidden in the wind into electrical energy.

The solution planned in the project is the development of an artificial learning-based decision support system that can support predictive maintenance applications in wind power plants. Predictive maintenance includes the continuous monitoring of processes by means of sensors placed on equipment and the detection and diagnosis of failures before they occur by using statistical or artificial learning techniques on these data, thus making maintenance planning with the help of these decision support systems. In this context, it is aimed to create artificial learning-based normal behavior models by using historical sensor data of critical and auxiliary equipment, to ensure that these models produce optimum results, and to perform model performance tests with data collected from wind field equipment. These trained models will be able to work in cloud-based systems, automatically generate fault flags using instant sensor flow data and send notifications to the relevant field personnel. Many predictive machine learning models will be tested and the final batch modeling methods combining them will be developed.

The aims planned to be achieved in the project are listed below:

  • Supporting the maintenance plans of turbine equipment with a predictive maintenance strategy with models learned from the historical data of each equipment.
  • Contributing to optimum process management by modeling the process parameters (parameter group) of wind power plant processes with artificial learning.
  • As a result of data-based and optimizing process and maintenance plan management, a more efficient and high-availability plant is obtained.
  • Enabling the turbines to work more efficiently by contributing to the time management of the maintenance, repair and operational tasks of the final product maintenance and operation teams at the power plant.

Hayat Kimya Project

Project Title: Developing Artificial Intelligence Forecasting Technologies for Developing New Detergent Formulas and Optimizing Performance

Customer Institution: Hayat Kimya (Chemical) Industry Inc.

Project Manager: Selin Ergun

Consortium Members:



In the sector, where product variety and variability are very frequent, an artificial intelligence-assisted product formulation determination process will be developed, which will shorten the time to commission a new product. In the project, a digital data library will be designed with the results of spectrophotometric measurements of the past multiple formulation inputs and the spectrophotometric measurements of the washings made with special fabrics, and these data will be transferred to artificial intelligence technology, which will use it effectively and innovatively compared to humans.

In the project, an active learning structure will be designed and which additional experiments should be done will be determined according to the output of this setup. Innovative machine learning methods suitable for the purpose of the project will be investigated and regression structures will be used for stain performance estimation and formulation estimation. Model performances will be compared with base performances obtained from basic approach methods and integration will be made for the highest performance model selected for the purpose. In addition, a smart test results raw material formulation decision support software will be developed that can work in integration with experiment and cost information. Thanks to the software, different raw material compositions can be suggested by the application and the compositions with the highest to the lowest stain removal performance will be listed by scoring method.

The aims planned to be achieved in the project are listed below:

  • With the artificial intelligence supported simulation to be developed, various laundry detergent formulations will be created and the need for experimentation and performance testing in the laboratory will be minimized.
  • The project will also be able to improve the prediction system according to new data defined by machine learning.
  • Thanks to the project output, it will be beneficial in terms of sustainability and economy by reducing the consumption of chemicals and water, with the rapid delivery of formulations closest to the expected target.
  • The machine learning model and digital experiment library software to be developed will function as an intelligent experiment and raw material composition optimization decision support software belonging to the model digital library software family to be developed using artificial intelligence.

Temsa Project

Project Title: Predictive Maintenance Estimation and Intelligent Spare Parts Warehouse Management with Artificial Intelligence and Machine Learning Methods of Data

Customer Institution: Temsa Skoda Sabancı Transportation Vehicles Inc.

Project Manager: Eve Sibel Yurtseven

Consortium Members:





It is aimed to carry out predictive maintenance by using artificial intelligence technology in the thematic field of smart production. Thanks to the sensors placed on the engine and undercarriage of Temsa's electric buses, data can be collected regularly. By processing this data, the vehicle's maintenance time, maintenance location and spare parts required for maintenance will be determined with the artificial intelligence-based decision-making support system. In this way, smart warehouse and inventory management will be possible. It is envisaged that unexpected maintenance will be reduced by 20% per vehicle and, thanks to the increase in continuity in operation, at least 10% in costs will be gained.

The ultimate goal of the project is to realize smart fleet management with artificial intelligence. Although there is a limited number of passenger cars in the market, it is expected to be the first product for the bus segment. Based on this, it exhibits an innovative approach and aims to be one of the leading products in its segment.

Tekkan Project

Project Title: Total Equipment Efficiency Measurement and Digital Twin Based Decision Making Control Software Development Project for Plastic Injection Molding Process

Customer Institution: Tekkan Plastic Industry and Trade Inc.

Project Manager: Erdem Hacioglu

Consortium Members:



The project, which will be developed in the thematic field of smart production, aims to create the digital twin of the production bench in the field of plastic injection molding. A highly accurate, reliable, time-critical and scalable decision-making software solution will be developed thanks to the digital twin decision-making software supported by artificial intelligence technologies. In this way, in the process of commissioning the new product, production parameters will be estimated according to the targeted quality level. As a result of these, it is aimed to have a total equipment efficiency of at least 80%.

The decision-making software to be developed has a great market and sales potential in the national and international manufacturing industry, as it addresses sectors such as automotive, packaging, electrical-electronics, defense, where plastic injection machines are used extensively.

Matay Automotive Project

Project Title: Artificial Intelligence Supported Error Prevention and Predictive Intelligent Production System Development Project in Robotic MIG/MAG Welding Processes

Customer Institution: Matay Automotive Supply Industry and Trade Inc.

Project Manager: Mehmet Uysalgil

Consortium Members:




Within the scope of the project, which will be carried out with artificial intelligence technology in the thematic field of smart production, it is aimed to develop a predictive quality application in exhaust production systems. The quality of the weld made in the welding operation will be monitored instantly. It is planned to create an artificial intelligence-based smart system solution in order to prevent spillage burrs, weld holes and weld gaps that occur during the welding operation, to get rid of destructive inspection, and to prevent control and scrap costs. The aim of the project is to monitor the production and quality parameters instantly with high resolution and to determine the parameter correlations that cause the error. Thanks to artificial intelligence-based modeling, it is aimed to catch the situations that cause quality loss and production errors at the beginning.

Among the innovative approaches of the project, there is monitoring the exhaust welding process with high resolution. In addition, it is aimed to present and commercialize the anomaly detection and predictive quality software to be developed as an almost plug-and-play product in order to be disseminated in enterprises in the welding manufacturing sector.

Arçelik A.Ş. Projesi

Project Title: Yapay Zekâ Tabanlı Plastik Enjeksiyon Kalite Kontrol Sistemi Projesi

Customer Institution: Arçelik A.Ş. Projesi

Project Manager: Burak Tosun

Consortium Members:



Plastik enjeksiyon süreci için daha önce geliştirmiş oldukları veri toplama ürününe, yapay zekâ desteği eklenecektir.  Laboratuvar ortamında geliştirilecek olan temel eğitim modelleri, sahada gerçek koşullarda iyileştirilecek ve üretim aşamasında kalite kestirimi yapay zekâ ile yapılacaktır. Çalışma sonuçlarının yan sanayi dahil olmak üzere yaygınlaştırılması hedeflenmektedir. Kalite ölçümlerindeki güvenilirliğin ve verimliliğin artması sağlanacaktır.

Arçelik A.Ş. Projesi

Project Title: Plastik Enjeksiyon Kalıplarında Optimal Soğuma İçin Yapay Zekâ Tabanlı Kanal Tasarım Yazılımı Projesii

Customer Institution: Arçelik A.Ş. Projesi

Project Manager: Ahmet Hamit Yılmaz

Consortium Members:



Kalıp soğutma kanal tasarımları, yapay zekâ desteğiyle gerçekleştirilecektir. Plastik enjeksiyon sürecinde, üretim süresine en çok etki eden soğutma başarımının bu sayede en üst seviyeye çıkarılması hedeflenmektedir. Bu kazanım tüm üretim bantlarında önemli bir maliyet iyileştirmesi sağlayacaktır.

Borçelik Çelik San. Tic. A.Ş. Projesi

Project Title: Önceden Eğitilmiş Derin Öğrenme Tabanlı Anomali ve Nesne Tespit Modelleri ile Çelik Yüzey Uygunsuzluk Kontrol Sisteminin Geliştirilmesi Projesi

Customer Institution: Borçelik Çelik San. Tic. A.Ş. Projesi

Project Manager: Saygın Kaçar

Consortium Members:



Çelik üretiminde kritik seviyede öneme sahip aşamalardan biri olan kalite kontrol sürecinin, yapay zekâ desteğiyle yürütülmesi gerçekleştirilecektir. Kalite kontrolü kesintisiz yapılacak olup, ürün kalitesinin yükselmesine ve kayıpların azalmasına katkı sağlayacaktır.

Fercam Cam San. ve Tic. Ltd. Şti. Projesi

Project Title: Bilgisayarlı Görü ve Yapay Zekâ Tabanlı Cam Kalite Kontrol Sistemi Geliştirilmesi Projesi

Customer Institution: Fercam Cam San. ve Tic. Ltd. Şti. Projesi

Project Manager: Ammar Yasin Yenigelenler

Consortium Members:



Savunma ve otomotiv sanayileri için üretilen camın kalitesini ölçmek için yapay zekâ destekli tarayıcı geliştirilecektir. Kalite ölçüm sürecinin iyileştirilmesi için gerçekleştirilecek olan bu tarayıcı, kalite fire oranlarını önemli ölçüde azaltacaktır. Aynı zamanda tarayıcının yurtiçi ve yurtdışı pazarda pay elde etmesi de hedeflenmektedir.

İmaş Makina Sanayi Anonim Şirketi Projesi

Project Title: Yapay Zekâ Tabanlı Yeni Nesil Şerit Testereli Metal Kesme Makinelerinde İzlenebilirlik, Verimlilik ve Kestirimci Bakım Projesi

Customer Institution: İmaş Makina Sanayi Anonim Şirketi

Project Manager: Hakkı Ekem

Consortium Members:



Firmanın kendi tasarımı olan testereli metal kesme makinalarına, yapay zekâ desteği eklenecektir. Kesme sürecinde oluşabilecek kayıpların en aza indirilmesi hedeflenmektedir. Yapay zekâ ile kesme makinasının başarımı artacak, yurtiçi ve yurtdışı pazarda rekabet gücü elde edilecektir.

Teknorot Otomotiv Ürünleri Sanayi ve Ticaret A.Ş. Projesi

Project Title: Otomotiv Süspansiyon ve Yönlendirme Parçaları için Yapay Zekâ Tabanlı Maliyet Tahmini Yazılımı Geliştirilmesi Projesi

Customer Institution: Teknorot Otomotiv Ürünleri Sanayi ve Ticaret A.Ş.

Project Manager: Murat Arslanoğlu

Consortium Members:



Teklif oluşturma sürecinde yapay zekâ desteğinden yararlanarak, zamandan tasaruf ederek verimliliğin artması hedeflenmektedir. Yapay zekâ yardımıyla, gelen siparişlerin alt yüklenici tedariği ve fabrika ortamındaki üretim ve tasarım süreçleri birleştirilerek, üretim planlama optimizasyonu yapılması da diğer bir kazanım olacaktır.

Türkiye Şişe ve Cam Fabrikaları A.Ş. Projesi

Project Title: Yapay Zekâ ve Makine Öğrenmesi Yöntemleri ile Cam Rengi Optimizasyonu Projesi

Customer Institution: Türkiye Şişe ve Cam Fabrikaları A.Ş. Projesi

Project Manager: Duygu Güldiren

Consortium Members:



Cam üretim sürecinde yapay zekânın kullanımı sayesinde, cam renginin her zaman tek bir değerde üretilmesi gerçekleştirilecektir. Yapay zekâ ile elde edilecek kazanım, yurtiçi ve yurtdışı üretim ortamlarında zamandan bağımsız olarak aynı cam rengi kalitesinin sağlanmasıdır. Rekabet gücünü artırması da diğer bir kazanım olacaktır.

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(SGE) Cyber Security Institute

The Cyber Security Institute, which was established to carry out studies to increase the national cyber security capacity, carries out research and development activities in the field of cyber security; carries out solutions-oriented projects for military institutions, public institutions and organizations and the private sector.

The main fields of activity of our institute, which has made a significant contribution to the creation of cyber security knowledge and tactical infrastructure in our country with many successful projects to date, are secure software development, penetration tests and vulnerability analysis.

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(IZE) Artificial Intelligence Institute

Artificial Intelligence Institute is the first institute established within the scope of TUBITAK centers and institutes, which cuts the sectors and research fields horizontally and focuses directly on the emerging technology field. For this reason, it constitutes an innovative model in terms of both the open innovation and co-development approach of the institute and its focus on emerging technology.

Artificial Intelligence Institute aims to develop core technologies in the field of artificial intelligence and bring these innovations from the forefront of science to the use of the industry as soon as possible. Focusing on the transformative potential of artificial intelligence, it will continue to play its part in pioneering efforts to create and sustain artificial intelligence-based innovation, growth and productivity in Turkey. Working with industry and public institutions in Turkey, together with other organizations within the artificial intelligence ecosystem, spreading the use of artificial intelligence and increasing the workforce specialized in this field are among its primary goals.