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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:  

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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:  

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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:  

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This project aims to develop a solution for the efficiency and maintenance planning needs of wind farms in Türkiye that produce energy with direct drive (gearless) technology, using data-driven artificial intelligence algorithms. This solution will assist in stable production by detecting and diagnosing potential abnormalities in equipment and processes at wind farms in advance. Thus, it will help increase the profitability of clean energy investments and encourage further investments in this area. The project's objective consists of three steps:

  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:

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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:

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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:

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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:

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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.

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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.

Discover institutes laboratories technologies products projects of BİLGEM.

Discover institutes laboratories technologies products projects of BİLGEM.

Intern

TÜBİTAK BİLGEM builds its basic strategy for the future on qualified knowledge and qualified people focused on national targets in the research, technology development and innovation ecosystem.

Starting from the understanding that "the most important resource of a country is generally people, specifically scientists," TÜBİTAK encourages and supports our youth from an early age. In this context, providing young minds with early exposure to technology production is crucial for the success of our National Technology Move. Accordingly, TÜBİTAK BİLGEM offers internship opportunities to undergraduate students from universities every year.

You can follow internship announcements and submit your applications through the Career Gateway at https://kariyerkapisi.cbiko.gov.tr.

You can access frequently asked questions about internships at TÜBİTAK BİLGEM from here. 

Application Conditions
  • Students enrolled in undergraduate (2nd year and above) and associate degree programs in departments offering education in universities and conducting insurance procedures through the higher education institution to which they are affiliated can benefit from the internship opportunity.
  • For undergraduate and associate degree students, a minimum Weighted Grade Point Average (GPA) of 2.50 out of 4 is required. The GPA of candidates with a 100-point system is converted to a 4-point system based on the "Conversion Table of Grades from the 4-Point System to the 100-Point System" published by the Higher Education Council.
  • There is no requirement for a foreign language certificate during the internship application process.
  • Students enrolled in departments such as Forensic Computing Engineering, Computer Sciences, Computer Science and Engineering, Computer Engineering, Computer and Informatics, Computer and Software Engineering, Information Systems Engineering, Electrical and Electronics Engineering, Control Engineering, Control and Computer Engineering, Control and Automation Engineering, Mechanical Engineering, Mechatronics Engineering, Telecommunication Engineering, or Software Engineering in universities can apply for internships.

Internship applications are accepted between December and January, and the internship period covers June, July, and August.

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Scholar

Scholar assignments are made for research and development activities for undergraduate, master's, doctoral students, and post-doctoral researchers. In our center, scholars are appointed for practical purposes in externally funded, TARAL, or European Union projects.

You can contact us via the email address bilgem.yetenekkazanimi@tubitak.gov.tr to apply to be a scholar.
Application Conditions

(1) The conditions for undergraduate scholars in externally funded projects conducted by the institution are specified below:

  •  Being a student continuing undergraduate education at higher education institutions established in Turkey (excluding foreign language preparatory students).
  • Having a weighted cumulative GPA for previous years, excluding preparatory years, based on the university's grading system, which satisfies the formula score and foreign language requirements in the recruitment criteria.
  • Completing at least the first semester of the first year of undergraduate education.
  • Having a GPA of "+3.00" and a University Placement Exam Ranking of "10,000 ≥" for undergraduate general average.
  • For foreign students placed in Turkish universities without taking the ÖSYM exam or for those who completed undergraduate education through exams such as Vertical Transfer Exam, the lowest university placement ranking of the department from the year the candidate started the undergraduate program is considered in the ranking formula.

(2) The conditions for master's degree scholars in externally funded projects conducted by the institution are specified below:

  • Being a student continuing master's degree education at higher education institutions established in Turkey (excluding special students and foreign language preparatory students).
  • Currently pursuing a master's degree in the project's field of responsibility.

(3) The conditions for doctoral students in externally funded projects conducted by the institution are specified below:

  • Being a student continuing doctoral education at higher education institutions established in Turkey (excluding special students and foreign language preparatory students).
  • Currently pursuing a doctorate in the project's field of responsibility or conducting a doctorate in areas determined within the framework of the YÖK-TÜBİTAK Doctoral Program Project Collaboration Protocol. (Students in medical specialization and artistic proficiency are accepted as doctoral students.)
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Candidate Researcher

Students in the 3rd and 4th years of relevant engineering departments at universities can apply to our Part-Time Candidate Researcher positions through our Job Application System at kariyer.tubitak.gov.tr. By doing so, they can gain work experience at TÜBİTAK BİLGEM during their university years.

This program does not have an end date. Candidate Researcher personnel working part-time during their university period can seamlessly transition to full-time employment as Researcher personnel at TÜBİTAK BİLGEM without interrupting their career journey after graduating from the undergraduate program.

Application Conditions

Conditions for the Candidate Researcher Program:

  • Being a 3rd or 4th-year student in the relevant departments specified in the announcements at universities.
  • Foreign language proficiency: Achieving appropriate scores in the exam types specified in the announcement or studying in a program that is 100% in English for undergraduate education.
  • Satisfying the formula score:

Weighted Graduation Average + (10,000/University Placement Exam Ranking) + Additional Score* >= 3.20

*Candidates who have achieved rankings and awards in national and international competitions will receive an additional score of 0.3.

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Researcher

By joining TÜBİTAK BİLGEM as a Researcher, you can contribute to developments in the fields of information technology, information security, and advanced electronics. You'll have the opportunity to make your mark on innovations, closely follow advancements, enhance your skills, and shape your future by advancing in your career.

You can apply to our currently open positions through the TÜBİTAK Job Application System .

Application Conditions

Conditions for Job Application:

  • Foreign language proficiency: Attaining appropriate scores in the exam types specified in the announcement or studying in a program that is 100% in English for undergraduate education.
  • Fulfilling specific requirements stated in the announcement (such as undergraduate department, years of experience, expertise, etc.).
  • Satisfying the formula score:

For Candidates with Less than 3 Years of Experience:

Weighted Graduation Average + (10,000 / University Placement Exam Ranking) + Additional Score* >= 3.20

 

For Candidates with 3 Years and More of Experience:

Weighted Graduation Average + (10,000 / University Placement Exam Ranking) + 5*[1 / (1 + e^(5 - years of experience) ) ] + Additional Score* >= 3.20


*Candidates who have achieved rankings and awards in national and international competitions will receive an additional score of 0.3.

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MILSEC 4 - Secure IP Terminal

SAFE IP TERMINAL

While the MİLSEC-4 terminal offers an up-to-date solution for next-generation secure communication (voice, data and video) in IP networks, it provides an uninterrupted communication service by maintaining the compatibility of secure voice communication in PSTN networks with PSTN secure phones in use.
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Configuration, surveillance and software update processes of MILSEC-4 terminals are carried out securely remotely using the Security Management Center (GYM). MİLSEC-4 terminal is capable of IP Network Key Loading (IPAAY) through secure communication with GYM without the need for an additional device.

MİLSEC-4 terminals are interoperable with MİLSEC-1A and MİLSEC-2 phones and offer the opportunity to replace MİLSEC-1A and MİLSEC-2 phones without interruption in the gradual transformation of PSTN networks to next generation IP networks.

FEATURES

  • End-to-end secure voice communication in PSTN networks
  • End-to-end secure voice, image and data transmission in IP networks
  • NATO SCIP compliance on IP networks
  • Compatibility with commercial SIP products
  • Interoperability with MILSEC1A and MILSEC2 secure phones
  • National and AES crypto algorithms
  • Remote software update
  • Easy operation with touch screen

It is subject to the sales license to be given by the Ministry of National Defense.