Comparison of different magneto-rheological fluids’ stability

The purpose of this paper is to compare two different methods for describing the sedimentation in different MRFs. It was decided to obtain electric resistivity of the MR fluid under magnetic field and to measure inductance of the solenoid, which is around the MR fluid, for diagnostic of the sedimentation. The electrical resistivity of the MR fluids and inductivity of the solenoid was measured daily for 11 days for 5 different MR fluids: (Lord MRF-140CG, Lord MRF-122EG, Liquids Research Company MRHCCS4-A and Liquids Research Company MRHCCS4-B and MUDZH-3 made in A. V. Luykov Heat and Mass Transfer Institute, Minsk, BY). These two methods and according procedures are described below

DIG-MAN: Integration of digital tools into product development and manufacturing education

General objectives of PRODEM education. Teaching of product development requires various digital tools. Nowadays, the digital tools usually use computers, which have become a standard element of manufacturing and teaching environments. In this context, an integration of computer-based technologies in manufacturing environments plays the crucial and main role, allowing to enrich, accelerate and integrate different production phases such as product development, design, manufacturing and inspection. Moreover, the digital tools play important role in management of production. According to Wdowik and Ratnayake (2019 paper: Open Access Digital Tool’s Application Potential in Technological Process Planning: SMMEs Perspective, https://doi.org/10.1007/978-3-030-29996-5_36), the digital tools can be divided into several main groups such as: machine tools and technological equipment (MTE), devices (D), internet(intranet)-based tools (I), software (S). The groups are presented in Fig. 1.1. Machine tools and technological equipment group contains all existing machines and devices which are commonly used in manufacturing and inspection phase. The group is used in physical shaping of manufactured products, measurement tasks regarding tools and products, etc. The next group of devices (D) is proposed to separate the newest trends of using mobile and computer-based technologies such as smartphones or tablets and indicate the necessity of increased mobility within production sites. The similar need of separation is in the case of internet(intranet)-based tools which indicate the growing interest in network-based solutions. Hence, D and I groups are proposed in order to underline the significance of mobility and networking. These two groups of the digital tools should also be supported in the nearest future by the use of 5G networks. The last group of software (S) concerns computer software produced for the aims of manufacturing environments. There is also a possibility to assign the defined solutions (e.g. computer programs) to more than one group (e.g. program can be assigned to software and internet-based tools). The main role of tools allocated inside separate groups is to support employees, managers and customers of manufacturing firms focused on abovementioned production phases. The digital tools are being developed in order to increase efficiency of production, quality of manufactured products and accelerate innovation process as well as comfort of work. Nowadays, digital also means mobile. Universities (especially technical), which are focused on higher education and research, have been continuously developing their teaching programmes since the beginning of industry 3.0 era. They need to prepare their alumni for changing environments of manufacturing enterprises and new challenges such as Industry 4.0 era, digitalization, networking, remote work, etc. Most of the teaching environments nowadays, especially those in manufacturing engineering area, are equipped with many digital tools and meet various challenges regarding an adaptation, a maintenance and a final usage of the digital tools. The application of these tools in teaching needs a space, staff and supporting infrastructures. Universities adapt their equipment and infrastructures to local or national needs of enterprises and the teaching content is usually focused on currently used technologies. Furthermore, research activities support teaching process by newly developed innovations. Figure 1.2 presents how different digital tools are used in teaching environments. Teaching environments are divided into four groups: lecture rooms, computer laboratories, manufacturing laboratories and industrial environments. The three groups are characteristic in the case of universities’ infrastructure whilst the fourth one is used for the aims of internships of students or researchers. Nowadays lecture rooms are mainly used for lectures and presentations which require the direct communication and interaction between teachers and students. However, such teaching method could also be replaced by the use of remote teaching (e.g. by the use of e-learning platforms or internet communicators). Unfortunately, remote teaching leads to limited interaction between people. Nonverbal communication is hence limited. Computer laboratories (CLs) usually gather students who solve different problems by the use of software. Most of the CLs enable teachers to display instructions by using projectors. Physical gathering in one room enables verbal and nonverbal communication between teachers and students. Manufacturing laboratories are usually used as the demonstrators of real industrial environments. They are also perfect places for performing of experiments and building the proficiency in using of infrastructure. The role of manufacturing labs can be divided as: • places which demonstrate the real industrial environments, • research sites where new ideas can be developed, improved and tested. Industrial environment has a crucial role in teaching. It enables an enriched student experience by providing real industrial challenges and problems

Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises

In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article—Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability

Decision Support Method for Dynamic Production Planning

Small and medium-sized engineering production companies face challenges that are related to unpredicted rapid changes of availability of the work force, materials and equipment. Those challenges are especially difficult to solve for companies focusing on unit or batch production and when they are collaborating with customers who require short lead times. A four-month observation was carried out in a metal processing company in Lithuania to understand the most common rising problems and developing solution for computerised decision support systems. It was discovered that the company needs a computerised “employee centred” system for the improvement of the allocation of tasks to employees. Such a need proved to be the most urgent one, especially during pandemics. An algorithm for the analysis and automated allocation of the employees’ tasks has been developed and tested. The proposed algorithm is universal and may be applied in different SMEs for engineering production