Digitalization Of Industrial Processes Research Articles

ИННОВАЦИИ И АВТОМАТИЗАЦИЯ В ПРОМЫШЛЕННОСТИ: РОЛЬ И ВКЛАД В ЭКОНОМИЧЕСКИЙ РОСТ И КОНКУРЕНТОСПОСОБНОСТЬ

This article examines the innovative activity of an industrial enterprise as a necessary factor in its economic development and ensuring competitiveness. Digitalization of industrial processes is a complex process that affects external and internal factors. The use of the latest technological solutions is also of great importance. Such as the integration of big data analysis into management decision-making systems. This study also highlights a number of generalized advantages that are achieved in the process of innovative activity of an industrial enterprise.

Digitalization and Platformization in Romania Based on the Digital Platform Economy Index 2020

This article examines the Romanian Digital Platform Economy. In particular, it discusses how this novel paradigm could help Romania adapt to new technological and structural conditions and participate in the upcoming industrial-digital revolution. It intends to investigate the bottlenecks in the country's platform economy. Moreover, it compares the health of the digital entrepreneurial ecosystem with that of neighbouring countries. So that Romania may achieve sustainable growth, it suggests ways to enhance the platform economy framework. The methodology and data analyzed in the study are based on The Digital Platform Economy Index 2020. It combines 61 indicators from different sources in 24 variables, twelve for the digital environment and twelve for the entrepreneurial ecosystem. These variables comprise the 12 pillars. Providing a basis for comparison, they are a crucial component of the model. Lastly, the four sub-indices and the main super-index are calculated. Platform economies like Romania lack technology transfer, financial facilitation, and digital literacy, according to the research. Demand side issues, such as fewer companies operating online and utilizing cloud technologies and low consumer online purchases, inhibit the introduction of new online businesses and technologies. Digitalization of industrial processes is not a trend in Eastern European countries. It is important to note that the study is limited by using macro-level indicators. With no platform-specific examples, it generalizes the concept of the digital economy. As a study topic, the digital platform economy presented in this article is an emerging field of research. <br />Implications for Central European audience: This article identifies the bottlenecks in the digital platform economy in Central and Eastern European countries. Particularly, it emphasizes the necessity to improve digital literacy, the number of multisided platforms networking, as well as technological transfer pillars of the platform economy. Cross-country also compares several dimensions of the digital platform economy. The author theoretically proves the importance of changing business models and embracing digitization in order to move towards a platform economy. In addition to describing what platform economies are and what they are not, the article also discusses how platforms can be combined with traditional firms to maintain a constant economic growth rate.

Modeling The Digital Transformation of Enterprises: A Comprehensive Study

In the modern world, rapid advancement in the field of technology has propelled institutions towards the path of digitization to enhance their business processes and collaboration. Enterprises are now utilizing digital tools with the aim of reducing labor and time requirements, managing information more effectively, and automating decision-making processes. This article is dedicated to the topic of “Modeling the Digitization of Enterprises (SME)” and its primary objective is to explore the achievements and challenges intended by this positive transformation. Industrial digitization is the process of integrating digital tools, business flows, and approaches into every aspect of business and processes within an organization. However, applying digital solutions is not straightforward, and many companies struggle with complexity, uncertainty, and unclear business logic. Indeed, the digitization of industrial processes goes far beyond technology and demands extensive transformation of business processes, business models, and ecosystem relationships. To catalyze this transformation, small and medium-sized enterprises with specialized capabilities in artificial intelligence, analytics, and sensors play a significant role. Achieving success in the digital economy is crucial, prompting organizations to formulate and assess effective strategies for digital transformation. Therefore, there are many complexities associated with modeling and evaluating a digital transformation strategy. Adopting a unified approach is essential to assess a digital transformation strategy. This approach should consider market dynamics, digital disruption characteristics, corporate attributes, as well as the process of implementing the digital transformation strategy. While decision-makers in the industry demonstrate an increasing readiness to invest in the digital transformation of their companies, they lack information about their current status in Industry 4.0 and strategic guidelines for its implementation. This article establishes a guiding unified procedural model, including the identification of specific operational areas in initial acquaintances with Industry 4.0 for companies and their implementation processes. Additionally, it explores a comprehensive model for modeling and evaluating digital transformation strategies.

Feasibility study of measuring instantaneous angular speed of helical gears with magnetoresistive sensors

The digitalization of industrial processes with e.g. the goal to increase the availability of production processes in general and to support the individualization of many products in particular leads to an increasing demand in sensor data. The state of the art for condition monitoring of involute gear trains is the measurement of structure-borne vibration with acceleration sensors. New approaches, such as measuring the instantaneous angular speed (IAS), are gaining in popularity. Machine manufacturers usually wish to use existing sensors or measurement points, where little or no effort is needed to implement the sensor concept. Magnetoresistive (MR) sensors fulfil this complex set of requirements to a high degree: They are comparatively easy to integrate, can be added as an optional component and provide sufficient accuracy.The authors have developed different sensor concepts using MR sensors for measuring IAS in a 1-stage helical gear box. Multiple tests with artificial tooth flank damages have been carried out to evaluate the damage detection potential of the sensor concepts. Finally, a spectrum analysis of the first gear mesh frequency and surrounding sidebands demonstrates the capability for detecting tooth flank damage with different MR sensor concepts.

Millimeter-Wave Contactless Connectors: From Fundamental Research to Commercialization

High-speed electrical connectors are essential components in all modern computing and interconnect systems such as in a display, a smartphone, an automobile, or an industrial robot. With the digitalization of industrial processes along with demand for high-speed consumer electronic products, the connector industry is expected to experience significant growth in coming years. One of the main concerns in electrical connector design is reliability. Because of the nature of physical connections, traditional mechanical connectors are unavoidably exposed to environmental hazards as well as mechanical wear from continuous usage; thus, creating connectivity with long-term reliability can be a major challenge. For instance, many daily personal computer users have experienced physically damaged USB connectors after connecting and disconnecting devices repeatedly over time. Moreover, ever-increasing data rates impose serious signal integrity challenges—especially for cost-driven consumer electronic products.

Implementation of the Modern Immersive Learning Model CPLM

The digitalization of industrial processes is being driven forward worldwide. In parallel, the education system must also be transformed. Currently, education does not follow the opportunities and development of technologies. We can ask ourselves how we can integrate technologies into a traditional learning process or how we can adapt the learning process to these technologies. We focused on robotics education in secondary vocational education. The paper contains research results from a modern learning model that addresses student problem-solving using cyber–physical systems. We proposed a reference model for industrial robotics education in the 21st century based on an innovative cyber-physical didactic model (CPLM). We conducted procedure time measurements, questionnaire evaluations, and EEG evaluations. We could use VR to influence the improvement of spatial and visual memory. The more intense representation of the given information influences multiple centers in the brain and, thus, the formation of multiple neural connections. We can influence knowledge, learning more effectively with short-term training in the virtual world than with classical learning methods. From the studied resources, we can conclude that the newer approach to teaching robotics is not yet available in this form. The emerging modern technologies and the possibility of developing training in this area should be investigated further.

Teaching Integration, Trust, Communication, and Collaboration Competencies Using Challenge-Based Learning for Business and Engineering Programs

The world is living its fourth industrial revolution, consisting in the digitalization of industrial processes and incorporating the use of different technologies, leading to significant changes in future workplaces and future jobs. For these reasons, the next generation of business & engineering professionals must strengthen their technical knowledge and personal competencies to face an adequate adaptation to the challenges brought by this new digital era. In this research, we present different learning methods that reinforce professional competencies focused on Integration, Trust, Communication, and Collaboration (ITC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) which are pointed as some of the main soft competencies needed to face this digital revolution in future businesses. The objective of this article is to propose a new challenge-based learning course-pack including the use of different methodologies such as serious games, gamification, simulation, and case studies to reinforce these ITC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> competencies. The results, composed of individual scores, testimonies, and focus groups, revealed evidence on ITC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> capacities training.

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.

Fully Printed Disposable IoT Soil Moisture Sensors for Precision Agriculture

Digitization of industrial processes using new technologies (IoT—Internet of Things, IoE—Internet of Everything), including the agriculture industry, are globally gaining growing interest. The precise management of production inputs is essential for many agricultural companies because limited or expensive sources of water and nutrients could make sustainable production difficult. For these reasons, precise data from fields, plants, and greenhouses have become more important for decision making and for the proper dosage of water and nutrients. On the market are a variety of sensors for monitoring environmental parameters within a precise agricultural area. However, the high price, data storage/transfer functionality are limiting so cost-effective products capable to transfer data directly to farmers via wireless IoT networks are required. Within a given scope, low-price sensor elements with an appropriate level of sensor response are required. In the presented paper, we have developed fully printed sensor elements and a dedicated measuring/communicating unit for IoT monitoring of soil moisture. Various fabrication printing techniques and a variety of materials were used. From the performed study, it is obvious that fully printed sensor elements based on cheap and environmentally friendly carbon layers printed on the wood substrate can compete with conventionally made sensors based on copper.

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. This work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on real-life data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system.

Análise da Indústria 4.0 como Elemento Rompedor na Administração de Produção

A Primeira Revolução Industrial potencializou a motorização da produção através da máquina a vapor, a Segunda Revolução Industrial, por meio do aço e da eletricidade, deu vida à produção em massa. Uma Terceira Revolução pode ser atribuída à automatização eletrônica das linhas produtivas e agora, segundo Schwab (2016), o mundo está assistindo a Quarta Revolução Industrial baseada na digitalização dos processos industriais. Este artigo faz uma abordagem história do pensamento da administração da produção demonstrando a sua evolução ao longo de sete períodos. Por meio de uma pesquisa bibliográfica será apresentado os principais elementos que compõe a Indústria 4.0.

A MaaS platform architecture supporting data sovereignty in sustainability assessment of manufacturing systems

The digitalization of industrial processes together with the broad integration of IoT infrastructures across production systems, are drastically impacting manufacturing value chains and the business models built on the top of them. Starting from the idea of dematerializing production resources and of embracing servitization, this paper describes the architecture behind a platform in the Manufacturing as a Service (MaaS) domain, creating an ecosystem that acts as a virtual marketplace bringing un-exploited production capacity, as well as other virtual and physical assets, closer to the production demand, to obtain their optimal matching. The platform creates a direct connection among manufacturing services providers, the technologies they integrate, and the products/resources customers are looking for. As the measurement of the sustainability performances of products and companies is a relevant element in the service/product selection process, the platform supports the automated acquisition of manufacturing data, their elaboration through an LCA based engine and the aggregation into indicators. Such indicators are used to sort suppliers into the sustainability perspective and/or provide means of communication of impacts generated by manufacturing. As a data rich platform, it integrates the INTERNATIONAL DATA SPACES (IDS) connector to link different entities through secure exchange and trusted sharing of data, thus supporting data sovereignty. The integration of the IDS reference architecture forms the basis for data privacy and security offering trust between platform participants and guaranteeing for data creators, respect to who is using their data, for how long, for which application, how many times and according to which terms & conditions.