6,567 research outputs found
Maintainability and evolvability of control software in machine and plant manufacturing -- An industrial survey
Automated Production Systems (aPS) have lifetimes of up to 30-50 years,
throughout which the desired products change ever more frequently. This
requires flexible, reusable control software that can be easily maintained and
evolved. To evaluate selected criteria that are especially relevant for
maturity in software maintainability and evolvability of aPS, the approach
SWMAT4aPS+ builds on a questionnaire with 52 questions. The three main research
questions cover updates of software modules and success factors for both
cross-disciplinary development as well as reusable models. This paper presents
the evaluation results of 68 companies from machine and plant manufacturing
(MPM). Companies providing automation devices and/or engineering tools will be
able to identify challenges their customers in MPM face. Validity is ensured
through feedback of the participating companies and an analysis of the
statistical unambiguousness of the results. From a software or systems
engineering point of view, almost all criteria are fulfilled below
expectations
Definition of the Future Skills Needs of Job Profiles in the Renewable Energy Sector
The growth of the renewable energy industry is happening at a swift pace pushed, by the emergence of Industry 4.0. Smart technologies like artificial intelligence (AI), Big Data, the Internet of Things (IoT), Digital Twin (DT), etc. enable companies within the sector of renewable energies to drastically improve their operations. In this sectoral context, where upgraded sustainability standards also play a vital role, it is necessary to fulfil the human capital requirements of the imminent technological advances. This article aims to determine the current skills of the renewable energy industry workforce and to predict the upcoming skill requirements linked to a digital transition by creating a unified database that contains both types of skills. This will serve as a tool for renewable energy businesses, education centers, and policymakers to plan the training itinerary necessary to close the skills gap, as part of the sectoral strategy to achieve a competent future workforce.This research was partly funded by (a) the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B)*, (b) the 4gune cluster, Siemens Gamesa and Aalborg University through the project “Identification of the necessary skills and competences for professionals of the future renewable energy sector”, and (c) Lantek, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair
Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization
Abstract
The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor forceThis research was partly funded by (a) the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B). (b) the HAZITEK call of the Basque Government, project acronym Adit4All and (c) Accenture, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair
Modularity and Architecture of PLC-based Software for Automated Production Systems: An analysis in industrial companies
Adaptive and flexible production systems require modular and reusable
software especially considering their long term life cycle of up to 50 years.
SWMAT4aPS, an approach to measure Software Maturity for automated Production
Systems is introduced. The approach identifies weaknesses and strengths of
various companie's solutions for modularity of software in the design of
automated Production Systems (aPS). At first, a self assessed questionnaire is
used to evaluate a large number of companies concerning their software
maturity. Secondly, we analyze PLC code, architectural levels, workflows and
abilities to configure code automatically out of engineering information in
four selected companies. In this paper, the questionnaire results from 16
German world leading companies in machine and plant manufacturing and four case
studies validating the results from the detailed analyses are introduced to
prove the applicability of the approach and give a survey of the state of the
art in industry
Simulation of site-specific irrigation control strategies with sparse input data
Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions.
An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller
Air pollution and livestock production
The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings
Towards Ontology-Based Requirements Engineering for IoT-Supported Well-Being, Aging and Health
Ontologies serve as a one of the formal means to represent and model
knowledge in computer science, electrical engineering, system engineering and
other related disciplines. Ontologies within requirements engineering may be
used for formal representation of system requirements. In the Internet of
Things, ontologies may be used to represent sensor knowledge and describe
acquired data semantics. Designing an ontology comprehensive enough with an
appropriate level of knowledge expressiveness, serving multiple purposes, from
system requirements specifications to modeling knowledge based on data from IoT
sensors, is one of the great challenges. This paper proposes an approach
towards ontology-based requirements engineering for well-being, aging and
health supported by the Internet of Things. Such an ontology design does not
aim at creating a new ontology, but extending the appropriate one already
existing, SAREF4EHAW, in order align with the well-being, aging and health
concepts and structure the knowledge within the domain. Other contributions
include a conceptual formulation for Well-Being, Aging and Health and a related
taxonomy, as well as a concept of One Well-Being, Aging and Health. New
attributes and relations have been proposed for the new ontology extension,
along with the updated list of use cases and particular ontological
requirements not covered by the original ontology. Future work envisions full
specification of the new ontology extension, as well as structuring system
requirements and sensor measurement parameters to follow description logic.Comment: 10 pages, 2 figures, 2 table
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