Mapping of processes and risks in the digital transformation in metrology of ionizing radiation, a case study in X-rays air kerma calibration

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INTRODUCTION
The concept of management generally refers to a set of principles related to the functions of planning, organizing, directing, and controlling. It consists of working efficiently with the available resources to achieve the expected goals with the least possible expenses [1]. A quality management system integrates all processes, techniques, and strategies to ensure that products and services are delivered according to expectations [2]. In this context, it is observed that in recent years, management focused on quality has progressively gained greater relevance in the Metrology of Ionizing Radiation (MIR).
Recently a new management concept has emerged, the so-called management 4.0, which is a response to the demands of the 4th industrial revolution from the digital transformation. This type of management is based on environment virtualization, integrating areas, and monitoring data in real-time. For example, the article [3] presents several European initiatives to support the new industrialization of Europe, such as the German Industry 4.0, the French Industry du Futur, and the Portuguese i4.0.
In this scope of metrology 4.0, the importance of mathematical and physical simulations and computer-based experiments is rapidly increasing. If such simulations imitate real measuring devices and measurements, they can be called "virtual measuring instruments." In this context, the task of metrology is to ensure the reliability of simulation results if they are used in the same way as real measurements [4].
At the same time, it can be observed that the digital transformation process enables the emergence of new products and processes that push proven quality assurance measures to their limits. This is particularly evident in the case of complex products that dynamically change their state after being put on the market. To be reliable, a product would need to be tested several times during its life cycle, sometimes continuously, and even today, there are no definitive solutions for this. One example is the applications of machine learning in medical devices. Although several innovative medical products are currently being developed with a high share of software, only a fraction leaps into the healthcare market.
Why have neural networks not yet been trained to evaluate the quality of individual mammography images? One of the main reasons is the lack of structured metrological processes as well an objective, verifiable, and reproducible validation of Artificial Intelligence (IA) technologies [4].
New projects worldwide (Digital-SI Task Group; SmartCom: European Metrology Cloud; GEMIMeG and Met4FoF) [5] are collaboratively developing applications and infrastructure for digital calibration certificates, researching the comparability of real and virtual measurements and also working on evaluation methods with scope for machine learning and artificial intelligence. Thus, aiming to support the country's technical and scientific development, this study aimed to map the processes and risks related to X-rays air kerma calibration.

MATERIALS AND METHODS
A state-of-the-art study described [6] discusses the evolution of an emerging research topic and systematically reviews [3], [5], [7]- [21]. The analysis of all these studies, together with the experience of the LABPROSAUD/IFBA laboratory experts, were used to identify the risks, build the process mapping of the contemporary calibration, and the flow chart projection of the future calibration.
For the quantification of risks related to the process, the Failure Mode and Effect Analysis -FMEA method was used [22] with the following sequence:  Not so easy Can be observed after some process checks 3 Medium It is necessary to use standard checking tools 4 Difficult It is necessary to use specific tools 5 Not detected It cannot be detected  125 Unacceptable RPN > 64 Urgent corrective action required Additional criteria: 1 Any RPN < 27 is a residual risk and can be addressed in the continuous improvement process. 2 If any criteria are 5, the RPN should be classified at least as "Relevant." connections between them for the execution of the flow, which means that each process member performs its task independently. Figure 1. The macro flow of the contemporary calibration process Figure 2 shows the mappings detailing the procedures related to the calibration of the kerma in the air, using the substitution method, according to the methodology [23], and mechanisms to guarantee the quality of the results required by [7]. There are 23 tasks to be performed, 15 of which are manual. The estimated total time was 1h30 per calibration. So, a calibration process for equipment with five ionization chambers is estimated at 7h30.  Table 3 shows the results of the statistics (O) and technique analysis (S and D). It identifies and quantifies the risks and their effects related to each agent in the contemporary calibration process at the place of study. The main risk identified was the error in the user's analysis and use of the calibration certificate. Its main vectors are complexity and number of quantities related to the area, lack of metrological user training, lack of metrological management of user equipment, manual certificate analyses process, complexity in the presentation of calibration certificate results, and cultural factors (perception of the meaning of the word "calibration" as "adjustment").  4th stage, the quality management system analyzes the data and monitors the process, continuously improving through data science and machine learning. In 5th step, the Digital Calibration Certificates -DCC is generated using blockchain, cryptography, and a markup language with rules for formatting documents (so that humans and machines can easily read them), for example, Extensible Markup

Contemporary calibration
Language -XML). These actions enable data protection and certificate parsing automation.

5-
The receiver processor checks the data for errors and divides it into calibration data and environmental conditions. The data is transformed into a format that will allow a comparison with the data obtained by the equipment to be calibrated. At the same time, the processor converts the environmental data into an appropriate format that will be sent as a signal to the environmental actuator sensor.
6-The environmental actuator sensor adjusts and maintains the environmental conditions to the same condition as the laboratory.
7-The intelligent sensor and actuator measure the values of the equipment to be calibrated and produce an electrical signal.
8-This signal is submitted to the receiver's processor and compared with the data from the reference standard. The difference between the data is recorded and processed to calculate instrument errors and uncertainty. If necessary, the intelligent sensor and actuator adjust some equipment parameters to be calibrated at the request of the receiving processor.
9-Then, the data is sent to the laboratory's quality management system, which analyzes and generates the digital calibration certificate.
10-At the end of the process, the laboratory sends the digital calibration certificate to the metrological cloud.
The [7] in clause 8.5, "Actions to face risks and opportunities, refers to the term "Risk-based thinking" which is a proactive approach in managing possible deficiencies and errors that may occur during the process; thus, even though there is still no calibration 4.0 for ionizing radiation area, table 4 presents the results of prospection of the quantification of risks related to this process. ³It is estimated that the use of AI and automation can significantly reduce the occurrence rate 4 Unique risks of the calibration 4.0 model [15] For the quantification of the exclusive risks of the 4.0 model, it was used the value found in 4.0 voltage calibrations and published in [15]. As the data transmission technologies are likely to be the same between the calibrations 4.0, it is reasonable to estimate that these values are the current typical ones for the technology, so they can be used in a projection of the kerma calibration with X-rays or quantities that have similar measurement and physical processes such as the calibration of electrical quantities used in radiodiagnosis like (voltage and current in the X-rays tube). The quantification of the other risks in the projection of the calibration 4.0 as, for example, the error in the analysis and use of the certificate and calibration were estimated by consensus among the Labprosaud experts and discussed at the PTB international conference [24]. It is estimated that the use of AI, automation, and DCC can significantly reduce the occurrence of this risk since this analysis would be done only by machines, which mitigates the main factors related to the occurrence in estimating this risk.
Comparing the contemporary and calibration 4.0 processes, it was possible to observe a small reduction in the total risk. Still, there are new risks unique to the 4.0 model, all with a severity of 5, and how to mitigate them is still unknown. It is also possible to estimate that AI automation can significantly reduce the risks of measurement, identification, and error in the analysis and use of calibration certificates.
In the case of calibration 4.0, the risk level may vary significantly with the type of processor transmissor/receptor, sensor/actuator chosen, and the communication network's reliability and integrity. For this study, the effects of the actions to mitigate the risks from processors and sensors have not been estimated since there is no statistical data for them yet. Therefore, it is crucial to note FMEA is not a one-time event and should be re-evaluated whenever there are changes in equipment, people, or method.
Study limitations: risk is calculated by mathematical means (data statistics). As the calibration process is "company secrets," there is a lack of "typical values" data; different laboratories use different techniques, so a particular independent approach would be the best way to do the risk analysis. Risk is different from risk perception. Although judgments must be made based on facts, cultural, religious, social, and political factors can influence the perception of risks among the agents involved. This means that these quantifications are limited to the current level of knowledge available about these risks and for this specific case study. However, since this is a pilot project, it can serve as a guide for future studies.
The risk analysis of this study corroborates that of [15] in which the agents involved in the calibration process are economic entities. The ALARA concept (as low as reasonably achievable) is applied in risk management. It is important to emphasize that residual risks and uncertainties continue