Ventil 3 / 2021 • Letnik 27 178 INDUSTRIJA 4.0 1 Introduction About one in seven jobs in the European Union is provided by industry. It provided 15% of total employment and value-added in 2017 [1]. Manu- facturing accounts for two thirds of the total R&D activities and provides half of productivity growth. That represents around 65% of both exports and imports to and from the EU. It is reasonable to as- sume manufacturing will remain a vital sector in the EU for its innovation, productivity, and trade potential [1]. The collapse of global supply chains created by the COVID-19 pandemic has stressed the importance of keeping at least a part of every manufacturing sec- tor in the region. This process has much to do with educating and managing human resources. Unlike manual labourers, operators of modern, automated manufacturing require higher skills and therefore command higher wages [2]. After the recession following the 2008 financial crisis, low-skilled workers had a much harder time finding a new job compared to their medium or high-skilled counterparts, especially in manufac- turing. There was a significant employment shift towards highly educated workers starting in 2011 [3]. Automation systems became more and more complex, so the demand for skilled workers went up with the complexity. As a result of advanced manufacturing, there was also an increase in quality control and standards [4]. Workers could be helped in the transition process by being enabled to acquire the necessary skills for employment. Skills and competences of the fu- ture are not even defined yet, so there is no syl- labus that can teach people how to acquire them. Businesses could help their employees achieve the know-how in time by retraining them and consid- ering multi-sector skilled partnerships. Hence, they can use the full potential of the same collaborative models that support many of the currently ongo- ing technology-driven business changes. This glob- al industrial transformation is a reality and there is wide consensus that Europe should embrace this change while making sure it works for everyone. [5] h uman -m achine i nteraction in i nDustry 4.0 anD b eyonD Bianca-Iulia Ionescu Bianca-Iulia Ionescu, BSc Computer Engineering, INEA d.o.o., Ljubljana Abstract: Manufacturing represents around 15% of the value-added and total employment in Europe. It is also of great importance to the EU and its R&D efforts, to which almost two thirds is contributed by manufactur- ing. Many of these efforts have so far been focused on automating manufacturing and the use of smart devices, which both experts and the general public have since named Industry 4.0. While Industry 4.0 focuses mostly on connectivity and data analytics, the focus will shift more towards the human component in the future. The reason is, on one hand, in the growing complexity of the manu- facturing processes. There is also growing awareness that in most industries the operators cannot be fully replaced by machines in spite of the growing power of automation supported by artificial intelligence. In the context of the fluctuating labour force and a need for new skills and knowledge, the question is how to include the operators in the control process in the most efficient manner. There are two important as- pects of that problem. One is training the operators and shortening the learning process, which becomes essential for the overall productivity. The second aspect concerns the efficient interaction between the operators and machines. The aim of this paper is to review the present position of the operators in manufacturing and the enabling technologies supporting their role in automation. A glimpse of new approaches to decrease the learning curve of the operators will be addressed, such as various potential training systems and required knowl- edge management methods in a paperless, more effective future. That is complemented by new ways of adapting human-machine interfaces and novel ways of implementing the user experience. Keywords: human machine interface, user experience, operator, training, Industry 4.0, Industry 5.0 Ventil 3 / 2021 • Letnik 27 Plenty of institutions such as the McKinsey Glob- al Institute (2018) [6] and PwC (2018) [7] have at- tempted to gauge the likely extent of automation over time. However, these studies mainly focus on the technical feasibility of substituting humans with machines. In fact, not all relevant technical and non-technical factors are considered, so that the impact of automation is overestimated in a sense of automation reducing the need for human participa- tion in plant management and control. That means not all the predicted job losses will actually hap- pen, but the character and the requirements of the jobs will change. Not every technologically feasible automation is economically rational. If all the jobs that could be automated were indeed automated, the required investment would be so extensive that it would be unrealistic from a macroeconomic per- spective [4]. To better perceive the relationship between human operators and automated systems, Seymour Pap- ert coined the very helpful concept of technocen- trism. He considers technocentrism a combination of techno- and egocentrism, manifested as a “falla- cy of referring all questions to the technology” [8]. Anthropocentrism, on the other hand, is a concept advocating that human beings are the most signif- icant entities in the world [9]. After a period of be- lief that automation can entirely replace humans, soon followed by disappointment, industry shifted to focus more on “human-centred automation.” An important step was the introduction of ISO stan- dard 13407 in 1999. It is based on the philosophy that empowers people to design for people and address their core needs while doing so. In the con- text of man-machine interaction, technocentrism explores the limits on what technology can do, whereas anthropocentrism tries to centre the inter- action around humans. The aim of this paper is to present an ontology of the human position in automation from the point of view of ideas of technocentrism and anthropo- centrism. The main concepts and categories will be reviewed in Section 2. Section 3 focuses on the particular issues related to the interaction between humans and machines. The conclusions section summarises the main highlights. 2 The ontology of the human-machine relationship in automation 2.1 Historical perspective With each industrial revolution, the role of humans in the manufacturing process continually decreased. It all started by replacing the manual procedures with machines powered by steam and water (Industry 1.0), and later on with the widespread adoption of electrical power (Industry 2.0). In the next phase, In- dustry 3.0 came with a shift to digital electronics, which brought intensive automation especially on the physical level (e.g., controlling speed of the ma- chines, movements of robots, etc.) [10]. Industry 4.0 arrived with the advancements in arti- ficial intelligence, where decision-making has been visibly intruded by the machines, thus supporting the technocentric paradigm. It has its emphasis on interconnecting the existing technology through the Internet of Things (IoT), which has access to real-time data. Industry 4.0 allows business owners to better control and understand every aspect of their operations, as well as make full use of instant data to boost productivity, improve processes, and drive growth [11]. The question is: with such rapid and turbulent changes, what will the place and role of human operators in the future be? The forthcoming Industry 5.0 is going to play a sig- nificant role in our society. It will complement the existing Industry 4.0 approach by focusing back on humans. It seeks to, in some respects, redefine industry by directing research and innovation to- wards the transition to a sustainable and resilient European industry. The concept itself was defined in July 2020. Its approaches are already a part of new policy initiatives by contributing to three of the European Commission’s priorities for 2019- 2024: “An economy that works for people,” “the European Green Deal,” and “Europe Fit for the Dig- ital Age” [13]. A similar concept is Society 5.0, proposed by the government of Japan in 2016, that “will be able to balance economic advancement with the resolu- tion of social problems by providing goods and ser- vices that granularly address manifold latent needs regardless of locale, age, sex, or language” [14]. Both Industry 4.0 and Society 5.0 emphasize the use of technology and entail a top-down, state- led approach with collaboration between indus- try, academia, and the governmental sector. The visions differ in the use of cyber-physical systems measuring the outcomes or scope of the intended future effects of technological innovations. While Industry 4.0 calls for a manufacturing-centric in- dustrial revolution that does not account for its impact on the public, Society 5.0 focuses heavi- INDUSTRIJA 4.0 179 Figure 1 : Evolution of manufacturing through industri- al revolutions [12] Ventil 3 / 2021 • Letnik 27 180 ly on exactly the public impact of technology and the need to create a better society that caters to diverse needs and preferences [14]. One could therefore view Industry 5.0 as a logical continua- tion of Industry 4.0 with the human-centric ideals of Society 5.0 at its core. 2.2 The role of operators Human operators are a fundamental component of the production process. Located on the factory floor, they are in charge of supervising the machin- ery used in the production process, as well as tak- ing proper action whenever needed (especially in the presence of alarms and faults) [15]. Even with the increase in factory automation, they remain crucial for conducting manual assembly or mainte- nance procedures [16]. With Industry 5.0 being a turning point in how humans and automated systems interact, opera- tors will be in a closer relationship with intelligent machines such as collaborative robots (cobots) [17]. Due to directly interacting with humans, hu- man-machine interactive systems require safety certifications based on thorough risk analyses that introduce additional costs for the manufacturers in case of any change of the application. However, the adoption of cobots in manufacturing is a financially risky choice due to the lack of consistency and pre- dictability of the market [18]. 2.3 Operator-machine interaction The operator-machine interaction is in fact a key point in automation systems. If not designed prop- erly, a system requires an extended amount of time for being operated, and with the staff freedom to change employment at any time, it prevents the sys- tem from ever being used at its full potential [19]. Although there are no broadly available studies on how human operators manage and perceive their interaction with machines, equipment is getting more and more complex, resulting in an increased need for skilled workers [31]. The operations teams are rarely included in the design process of hu- man-machine interfaces (HMIs), partly due to a significant increase of product variants that have shorter product life cycles [16]. That causes the operators to be disconnected from the process it- self, leading to a high dependency on the system to signal any potential unexpected behaviour through alarms or process interlocks [19]. Humans tend to favour interacting with physical ob- jects. Under real life circumstances, the use of both procedural and motor skills is crucial for the safety and efficiency of the manual procedures [16]. The AVEVA Group’s situational awareness whitepaper of 2014 points out it is common to hear that it will take about two years for an operator to become proficient in the use of a system [19]. Following the example from the consumer elec- tronics industry, automation system designers have turned to a more user-centric approach. It follows that in order for us to comprehend the experience of the operator-machine interaction, we need to un- derstand the user first. We need to make sure that the user can answer the question “Who does what, when, and under which condition?” [20]. This is to ensure that the system they interact with is quick and intuitive, and that the system itself enables easy reversion of actions, or a simulation mode is imple- mented to motivate the operator to explore the sys- tem and reduce their stress when doing so [16]. 2.4 Situational awareness (SA) Situational awareness relies on the perception of information in the environment, interpretation of its meaning in context, and anticipation of its conse- quences for an appropriate response [21]. There are three distinct levels of SA. The transition between them is done through cognition, judgement, and decision-making, respectively [19], as seen in Fig. 2. Situational awareness can complicate our work in- stead of simplifying it if not used adequately. In one study [22] that tasked users to explore the virtual environment and locate victims and fires within a short period of time, it was shown that sometimes more data means less understanding. In critical sit- uations when a quick response is needed, the user might be overwhelmed by a big number of messag- es received and act on less critical information. If we add a complicated query in the background as well, usability of the system is reduced even further [22]. INDUSTRIJA 4.0 Figure 2 : Levels of situational awareness [19] Ventil 3 / 2021 • Letnik 27 181 Situational awareness is an important concept that can enhance the overall interaction between oper- ators and machines. The use of situational aware- ness has an important role in the design of HMIs. There has been a lot of research done on situa- tional awareness theory, design, training, and mea- surements since the 1990s. Although originally met with scepticism, interest in the topic grew quickly from its initial start in aviation into various other fields. It has since taken hold in the fields of cog- nitive psychology and human factors, becoming a part of the mainstream consensus despite not hav- ing an agreed-upon definition [23]. 2.5 Manufacturing execution system (MES) An MES is a platform where, based on information from the process and requirements imposed by the production, targets are transformed into decisions. Manufacturing execution systems aim to increase the transparency of manufacturing systems by performing data acquisition from the manufactur- ing processes, reactive planning, and bi-directional communication to both enterprise resource plan- ning (ERP) systems and shop floor systems, con- trolled by supervisory control and data acquisition (SCADA) systems [24]. Both ERP and SCADA systems are responsible for gathering and analysing real-time data [25]. SCADA does not enable us to track the detailed transfor- mation of raw materials to finished goods through the entire production process. In fact, it provides specific information at individual stages, such as ef- fectiveness, product quantity, and average produc- tion speed. ERP needs a system capable of dealing with both real-time data and transactions to make sense of the overwhelming amount of information at the production level. Here is where MES comes into play [26]. An MES integrates several applications that are al- ready used on the factory floor with each other, as a part of a bespoke system, as well as with corpo- rate information and process control systems [27]. These applications provide decision support to the production processes and create a consistent view of production data. They also bring more benefits with them, such as traceability and error preven- tion of production, increased overall equipment ef- ficiency (OEE), and others [28]. An MES is purely a technical entity, becoming more and more powerful in terms of decision support but requires exchange of information with the op- erators. For an operator on the factory floor, the interaction with the MES should be intuitive, con- cise, and unambiguous; especially if they only see a partial picture of the processes running in the plant on the physical level, as well as of the processes related to data processing, information elicitation, and machine decision-making. It is important to understand where to focus our attention while de- signing MESs with the goal of maximising the effi- ciency of inclusion of the operators in the system control loop. 3 What matters for efficient inclusion of the operator in the loop? As defined by the National Institute of Standards and Technology of the United States, a human-ma- chine interface (HMI) is the hardware or software through which an operator interacts with a control- ler, that can range “from a physical control panel with buttons and indicator lights to an industrial PC with a colour graphics display running dedicated HMI software” [29]. The design of effective and easy to use human-ma- chine interfaces is an important component of pro- duction efficiency [15]. As established in the previ- ous section, the role of human operators remains crucial, so the design of new HMIs is considered to be a key challenge for the new industrial service solutions [30]. A well-designed human-machine in- terface provides support in a way that is effective and easy to understand, by addressing operators’ needs and difficulties [31]. Aranburu et al. [32] suggest that new methods for evaluating the user experience when interacting with industrial HMIs are needed. These methods need to focus on increasing the efficiency and ef- fectiveness of the task execution, while generating positive emotions in operators, so they are more motivated in their learning process [32]. 3.1 User experience (UX) User experience (UX) has its focus on “having a deep understanding of users, what they need, what they value, their abilities, and also their lim- itations” [33]. It goes beyond system functionality and usability, but there is a different perception of it between the scientific and academic fields. In the scientific community, there is a bigger emphasis on experience-related factors, whereas in the in- dustry, the efforts focus on practical and functional aspects of the experience, such as usability, novel- ty, or the life cycle [32]. In Fig. 3, created by Peter Morville, we can see one interpretation of the sev- eral aspects of UX. Currently, the human-machine interfaces focus on providing the operator with control over the pro- duction processes and access to relevant informa- tion [35]. Usability studies can show us how easy INDUSTRIJA 4.0 Ventil 3 / 2021 • Letnik 27 182 or complicated using an interface for an operator is [15]. Sometimes products that directly affect the user’s experience, such as a computer mouse or a keyboard, might be omitted when creating the in- terface [30]. Recent technological advancements raise the ques- tion of what can be improved and how to increase the interaction capabilities of the operator with the human-machine interfaces. In one paper [35], the authors propose a workstation-operator interac- tion that has the capability to adapt the ongoing interaction with the user, to improve performance, safety, well-being, satisfaction, and production measures [35]. Another study [30] proposes the implementation of an experience context capturer (ECC). That is a “user centred tool that analyses the user experience context within the industrial HMI environments and its influence on the development of positive experiences” [30]. 3.2 Learning curve A learning curve is defined as “a correlation be- tween a learner’s performance on a task and the number of attempts or time required to complete the task,” and it can be represented by an injective function on a graph. The theory proposes that the more the learner repeats the task, the better their performance gets. In industry, the learning curve can be used to track the manufacturing costs re- lated to workers’ performance. Due to the varying speed with which the user learns throughout the process of achieving proficiency, the sigmoid curve model is most representative [36]. Most current human-machine interfaces come with a major disadvantage: they need to be operated by an expert who has to complete their tasks and set up the machine through the user interface. These interfaces tend to be too complex for the less spe- cialised operators. A simple, structured HMI would allow less training to manage the entire process effectively while avoiding wrong decisions and im- proving operator response [31]. There is a high lack of standardisation of produc- tion systems. This implies a higher learning curve, because no common principles and methods are assimilated by the user over time [31]. The standard that defines the key performance indicators used in manufacturing operations management, ISO 22400, specifies a selected number of KPIs in cur- rent practice [37]. However, it does not yet cover human-machine collaboration, and it is question- able whether it will in the future [38]. 3.3 Training An important aid in training and getting an opera- tor accustomed to a system are user manuals. They contain detailed, comprehensive procedures about how to operate the HMI. In theory, that means that no other human personnel are required to teach a newly employed operator. In reality, the beginners are still being supervised or shadowing a more experienced operator and require weeks, if not months, to achieve full proficiency, as mentioned by Krajewski [19]. Complex production systems require complex hu- man-machine interfaces that result in complex user manuals. This phenomenon makes inexperienced operators unable to interact with and more experi- enced operators feel uncomfortable to operate the system. User manuals are the most widespread tool supporting operators in handling critical scenarios, but they are insufficient on shop floors [39]. They cannot predict all possible corner cases of a sys- tem’s operation, especially for a complicated sys- tem with multiple potential fail points. Even proper training cannot cover all possible scenarios. INDUSTRIJA 4.0 Figure 3 : User experience honeycomb [34] Figure 4 : The model of learning curve [36] Ventil 3 / 2021 • Letnik 27 183 Many training systems have been proposed for manual procedures (e.g., assembly, maintenance, setup) [40]. However, these systems focus on what the user can remember about the sequence of work steps. Currently, many of the existing virtual training systems focus on providing visual infor- mation presentation. For a manual procedure, the acquisition of precise motor skills is a key compo- nent that should be present. The operator should receive real-time feedback on their performance for self-assessment. Mistakes in production are not acceptable, but no alternative is provided to learn in most cases [16]. Operators’ training is a low-documented topic in both the industry and the scientific communities, which is the same as many other aspects; but this should change. It teaches us how to reduce the learning curve of operators, increase machinery usage efficiency, and reduce potential accidents in the workplace, so there could be more knowl- edge shared on the topic to help the industry im- prove. 4 Knowledge Management Learning is at the heart of problem-solving and de- cision-making [20]. Whenever one is given an un- familiar task, time and resources are spent to learn the task before being able to perform it produc- tively. To avoid duplicate work and to enable oth- ers to learn from our experience, the knowledge we have gained should be documented. Knowledge management (KM) is the process of creating, using, sharing, and maintaining information and knowl- edge [41]. After a number of years and new situa- tions, people tend to forget the original solution, so here is where KM comes in handy. As mentioned in section 3, a common way to keep the knowledge of operating machinery still relies on user manuals that are usually both ineffective and insufficient to allow an operator to gain full confidence in operating the system [16]. There are ways of reducing the need for written materials though, and that is by assessing and im- proving the usability of the current human-machine interfaces. One proposed way to do that is through subjective usability assessment, more exactly cog- nitive walkthrough, which uses the following four questions in its process [15]: 1. Does the user (of the interface) understand what one should do? 2. Is the user able to identify the interaction tool? 3. If the user is able to identify the interaction tool, can they understand what they should do to achieve the goal? 4. After the action has been performed, can the user understand the answer? The previous questions are meant to check all the unconscious phases that a user goes through when using an interactive system, because in the men- tioned process, at first, they are given a task, and then are asked to map a list of actions required to complete the given goal. The questions give a good insight into how the task is planned, approached, and later on, how the response received is per- ceived by the user [15]. Another proposed idea in the literature is to have a social network for sharing knowledge that enables asking questions to other operators, aiming to help especially when faced with unexpected events such as troubleshooting or unscheduled mainte- nance [39]. The core of user manuals covers basic usage of the system. A way to reduce the necessity of that can be implementing a real-time feedback training, as explained in section 3. However, this kind of a system can only be applied to manual procedures. They have been noticed to be more effective than video-based training systems exactly because of the feedback component that is a crucial key in manual operations in both teaching the user and giving them the required confidence to perform the respective actions in the future [16]. An idea that might help reduce the need for user manuals is a training system for human-machine in- terfaces. More exactly, in a similar way that some video games are doing it—by not just presenting the information on screen and having you click through it, but instead having a demo/simulation mode. That makes the user actually interact with the system and gives feedback on their behaviour in a similar way to what the active feedback train- ing is doing for manual procedures. This way, we would allow the operator to understand the needed sequence of steps required to perform an opera- tion and allow them to try it out without impacting production. No data would be recorded in the pro- duction system, and it would allow them to do a short refreshment course at any moment they feel they need it just to regain their confidence or re- fresh their memory. The world is moving to smart devices and applianc- es. Very few people still read user manuals cover to cover, and that should make us decide carefully what needs to be included in them. For example, the most useful part in the user manual of a coffee machine is the cleaning cycle—a frequent but not daily procedure that, if not done properly, can dam- age the machine. Because of this, the user needs guidance in completely and correctly executing all the steps for it. Similarly, for HMIs, the most import- ant parts to be covered in a user manual should not be those for regular usage, but rather for trouble- shooting. However, it is impossible to cover all po- tential situations in a classic document type user INDUSTRIJA 4.0 Ventil 3 / 2021 • Letnik 27 184 manual, so there is a need for a more flexible ap- proach, and the end result should be widely avail- able for all operators. 4.1 Workflow One way to help maintain an operation’s defined sequence of steps is with the use of workflow management software. It is a good way to en- able knowledge management, since it helps with keeping track of standard operational procedures (SOPs) and improving process efficiency by 40%. It also helps with creating and monitoring KPIs while reducing costs by approximately 30% [42]. Some common functionalities of a workflow man- agement software are prompt notification systems along with detailed reporting, task assignment, controlled access to specific parts of the system, and graphical process modelling. A good work- flow-based system can omit errors and replace the user manual by automating all the required proce- dures. This way, it shortens the learning curve while improving quality and providing good decision support if configured accordingly. Workflow application is a hot topic in computer application science [43] and has attracted great attention in China amongst other countries, in con- text of their re-industrialisation strategy [13]. 4.2 Digital twins One way to help workers get more easily accus- tomed to the interfaces would be to bring the demo/simulation mode from the testing environ- ment, where a digital twin is used to simulate the system [44], all the way to human-machine inter- faces. From the operator’s perspective, adding a simulation mode with some virtual reality capabil- ities and a platform to exchange knowledge with their colleagues would both be very beneficial, since operators are located mostly at their workstations, therefore having a harder time to reach other peo- ple directly. Such an additional app would however require the management to contact a third-party company that could develop such a mobile soft- ware, which would increase the initial costs. User experience has hardly been studied with re- gard to human-machine interfaces. However, sev- eral methods to evaluate the UX when interacting with HMIs have been proposed, and they are based on the context of the interaction [30]. The three questions posed make us better under- stand what the user needs to know to feel comfort- able when using an interface. The base is, of course, understanding what to operate and how, but it is equally important to understand why that proce- dure is even needed in the first place, and how it makes the operator feel when they are doing it. People’s emotions are often overlooked, but it is important to know how a user feels about their en- vironment and the results of their work. In the end, if we want to fully improve the user experience, we need to be aware of the entire context of the inter- action. Context can be divided into micro and macro user experiences. The first one is analysed from the us- er’s perspective gained while interacting with the system, while the latter one is wider and covers more intrinsic aspects such as their employer and its values, social environment, and culture [30]. 5 Conclusions The paper reviews the potential changes in HMI de- sign to ensure a better user experience for the op- erator, by both reducing the time needed to master the interface and removing the necessity of a user INDUSTRIJA 4.0 Figure 5 : User experience in industrial workspaces, in context [30] Ventil 3 / 2021 • Letnik 27 manual. Several training methods and their bene- fits are presented, as well as how close HMIs cur- rently are to the core tenet of Industry 5.0: being human-centric. The position of the human operator in the age of rising complexity of manufacturing is discussed in this paper. The main entities which affect the per- formance of the operator and consequently the performance of the entire manufacturing process are reviewed, and some observations are deduced. A good human-machine interface allows anyone to know how to react in common usage situations. A really good user experience is tailored to the op- erator, which makes its development even harder considering that opinions can be linked to one’s personality and even in the same context, two as- sessments of it might have completely different re- sults [30]. Various improvement possibilities were reviewed: what sort of training methods are and can be used, how to handle knowledge management inside the plant, and why to introduce situational awareness and workflow in the automation systems. We want to raise awareness among designers of industrial automation systems on re-defining the role of the operator in the emerging manufacturing automation paradigms. This work will serve as one of the bases for INEA to continue conceptualising a new generation of HMIs for complex large-scale manufacturing processes. It seems the future of manufacturing is expected to lead to safer jobs, requiring new and higher skills. Those are already needed, however, due to the ex- isting complex systems that sometimes make even the most experienced operators feel uncomfortable using them, especially when it comes to undocu- mented scenarios that might appear during usage. With the fifth industrial revolution, the focus shifts to placing the added-value and well-being of the workers at the centre of the production process, while respecting the production limits of the plan- et. We should give value back to the people, and with the help of Industry 5.0 guidelines we can do exactly that: promote talent, diversity, and human empowerment. 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Jiang, Meilin Wang, Yinhao Ye, Yangshuai Wang, Yi Zhou, Qing Liu, Implementation of a Message-driven Manufacturing Execution Workflow System, Procedia CIRP, Volume 83, 2019, Pages 655- 660, ISSN 2212-8271, doi.org/10.1016/j.pro- cir.2019.04.125. [44] networkworld.com/article/3280225 (Last ac- cessed 22.03.2021) 186 INDUSTRIJA 4.0 Ventil 3 / 2021 • Letnik 27 187 INDUSTRIJA 4.0 Industrijski uporabniški vmesniki v industriji 4.0 in pogled naprej Razširjeni povzetek: Proizvodna dejavnost predstavlja približno 15 % dodane vrednosti in tudi 15 % vseh delovnih mest v Evropi. S takšnim obsegom je torej kritičnega pomena za EU in njeno raziskovalno-razvojno dejavnost, saj skoraj dve tretjini vseh investicij pride prav s tega naslova. Velik delež raziskav se osredotoča na avtomatizacijo proizvodnje in uporabo pametnih naprav, kar strokovnjaki kot tudi splošna javnost opredeljujejo z oznako industrija 4.0. Industrija 4.0 se trenutno osredotoča predvsem na povezljivost in analizo podatkov, v prihodnje pa se bo nekoliko bolj usmerila na uporabnika in človeško komponento nasploh. Razlogov za to je več: naraščajoča kompleksnost proizvodnih procesov na eni strani, na drugi pa vse večje zavedanje, da delo operaterja – kljub vse večjim zmožnostim umetne inteligence – v popolnosti nikoli ne bo moglo biti zadovoljivo na- domeščeno s tehnologijo. Ob teh dejstvih se odpirajo nova vprašanja učinkovitega vključevanja delovne sile v proizvodne procese, saj vse večja kompleksnost sistemov narekuje nova znanja in spretnosti, te pa je ob naraščajoči fluktuaciji delovne sile vse težje zagotoviti. Ta izziv lahko rešimo s krajšim časom učenja in uvajanja operaterjev ter z učinkovitejšo interakcijo med operaterjem in napravo oz. procesom, ki ga upravlja. Namen tega prispevka je pregled trenutnega položaja operaterjev in tehnologij, ki omogočajo in podpira- jo njihovo vlogo v avtomatiziranih proizvodnih procesih. Dotaknili se bomo nekaterih novih pristopov za hitrejše učenje in potrebnih metod za (brezpapirno) upravljanje z bazami znanj, vse to v povezavi z novimi načini prilagoditve industrijskih uporabniških vmesnikov in celostne uporabniške izkušnje. Ključne besede: industrijski uporabniški vmesnik, HMI, uporabniška izkušnja, operater, učenje, industrija 4.0, industrija 5.0 Vse za avtomatizacijo proizvodnje Sistemi za avtomatizacijo - Industrijski računalniki - Krmilniki za avtomatizacijo strojev - Programirljivi logični krmilniki (PLC) - Distribuirane I/O enote - Vmesniki človek-stroj (HMI) - Sysmac Studio Robotika - Industrijski roboti - SCARA roboti - Kolaborativni roboti - PICK & PLACE roboti - Mobilni roboti Varnostna tehnika - Naprave za zaustavljanje in nadzor v sili - Varnostna stikala - Varnostna vrata - Varnostne preproge - serija UMA Nadzor in preverjanje kakovosti - Identikacijski sistemi - Sistemi za kontrolo kvalitete - Merilni senzorji Senzorika - Fotoelektrični senzorji - Senzorji barve in označb - Senzorji s svetlobnimi vodniki - Senzorji za površine - Optični senzorji in ojačevalniki - Induktivni senzorji - Mehanski senzorji in mejna stikala - Senzorji za procesne veličine Komponente za nadzor delovanja - Senzorji in regulatorji temperature - Napajalniki - Brezprekinitveno napajanje (UPS) - Časovniki - Števci - Programirljivi releji - Digitalni prikazovalniki - Naprave za spremljanje energije Stikalne komponente - Elektromehanski releji - Polprevodniški releji - Nizkonapetostni preklopniki - Stikala in tipke - Terminalni bloki Pogonska tehnika - Krmilniki gibanja - CNC krmilniki - Servo sistemi - Frekvenčni pretvorniki - Varnostni senzorji - Varnostni logični krmilni sistemi - Varnostni izhodi - Verikacijski sistemi - Vision sistemi in industrijske kamere Za višjo produktivnost.