Ebook: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning

This book of proceedings consists of papers carefully selected for the 11th Swedish Production Symposium (SPS2024), held at University West, Sweden, in Trollhättan from 23 to 26 April 2024. The title and overarching theme of SPS2024 is Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning. This aligns with the European Commission’s vision for the future of manufacturing, emphasizing stakeholder value, the societal role of industry, worker well-being, and environmental sustainability.

The Swedish Production Symposium (SPS) is organized by the Swedish Production Academy (SPA), established in 2006. SPA’s vision is “to drive and develop production research and higher education in Sweden, and to increase national cooperation in research and education within the production area.” (See https://swedishproductionacademy.se). SPS launched in August 2007 in Gothenburg, Sweden with a mission to provide a platform for SPA members, industry professionals, and academics interested in production research and education to share insights and ideas.

In recognition of the essential collaboration between product development and production for successful product realization, SPS2024 is co-organized in Sweden by SPA and the Product Development Academy (PDA) (see https://www.productdevelopmentacademy.se), continuing the collaboration initiated at SPS2020, which was hosted by Jönköping University.

This book features the 59 papers accepted for presentation at the symposium, categorized into six sections reflecting the thematic areas of SPS2024:

∙ Sustainable manufacturing

∙ Smart production and automation

∙ Digitalization for efficient product realization

∙ Circular production

∙ Industrial transformation for sustainability

∙ Integration of education and research.

The contributors to the proceedings represent a diverse array of affiliations, and the 59 papers in the proceedings are co-authored by individuals from 13 Swedish universities, 4 international universities, one research institute, and 11 companies.

We would like to extend our gratitude to:

∙ All authors for their valuable contributions.

∙ Members of the Scientific Committee for their diligent review of submitted papers.

∙ Session chairs for their assistance during the symposium.

∙ Keynote speakers Daniel Sperling (Founding Director, Institute of Transportation Studies, University of California, Davis), Alice Øritsland (Vice president HR Vattenfall Distribution & Director People, Culture & Sustainability), Henrik Runnemalm (Vice president Global Technology Centre, GKN Aerospace), Amer Mohammed (Expert in digitalization, innovation, AR and AI), and Thomas Böllinghaus (Head of Department Component Safety, Bundesanstalt für Materialforschung und – prüfung (BAM) for sharing their expertise and experiences.

∙ Linda Bohlin Trajkovski (CEO, Innovatum Science Park/Blå bioekonomi), and Frank Smit (Vice President Vehicle Solutions, NEVS) for sharing their perspectives.

∙ Panel discussion participants Per Norlander (Vice President Sveriges Ingenjörer), Mélanie Despeisse (Associate Professor of Sustainable Digitalized Production, Chalmers University of Technology), Lena Killander (Horizon Europe Programme Committee Expert at Vinnova), Cecilia Warrol (Senior Expert, Teknikföretagen), Maria Dollhopf (Programme Manager at the Knowledge Foundation), Dina Koutsikouri (Associate Professor of Information Systems, University of Gothenburg), Amanda Dalstam (CoE Director Engineering IT, GKN Aerospace), and Luca Costa (CEO, International Institute of Welding (IIW)) for the exchange of their knowledge and insight.

∙ Jan-Eric Ståhl (Professor, Lund University) and Anders Jarfors (Professor, Jönköping University) for their valuable input.

∙ Vinnova - Production 2030, GKN Aerospace, and Trollhättan Stad for their support.

∙ Lastly, everyone else who contributed to the realization of the symposium.

We are immensely grateful for your attendance and contributions, which made SPS2024 such a resounding success.

This work focuses on reducing the experimental need for creating a reliable tool life model for a data set of 46 tool data points with its resulting tool life for a single-tooth side milling application in medium carbon steel, C45 E.

Based on the data set, 615 180 unique tool life models are created using Colding’s equation.

This is achieved by creating models using different unique subsets of the complete data set where the cardinality is varied from 7 to 43.

The paper shows that the improvement from adding more data points to the modelling are neglectable after 34 data points are included in the modelling if a maximum absolute model error ≤ 9% is sought.

Furthermore, it is shown that the prediction error increases when extrapolating outside the range of equivalent chip thickness and cutting speed used for the modelling work compared to an interpolative error within the range.

By carefully planning the experimental set-up by maximising the cutting speed and feed range decreases the risk of creating a non-relevant model where the prediction error increases when located outside the modelling range.

Metrology and characterisation of products’ functional surfaces are of key importance in smart and sustainable manufacturing. By proper measures of resulting topography and dimension at the micro-cm level, higher process control can be achieved, leading to more efficient production with products closer to defined targets. Commercial surface metrology systems for lab- and in/on-line applications have increased in the last decades, but the wood sector has not yet benefited from this development. A better understanding of sawn wood topography combined with smart online metrology systems is expected to lead to a substantial reduction of waste in sawmill production, both by transforming waste pieces and sideboards into engineered wood products and by optimising the sawing process (e.g. by using thinner saw blades and reduced tolerances). It would also open new design possibilities and challenge the construction sector to replace today’s materials with renewable raw materials. Additionally, sawmills will be less dependent on incoming timber dimensions. This study is the first step towards a better understanding of sawn wood topography and how relevant surface features can be detected and analysed to enable the next generation of functional wood surfaces for various applications. By identifying the measuring instrument’s capability to capture surface topographical features of sawn wood, this paper discusses the requirements for efficient measurement techniques. It opens for future implementation of machine learning algorithms to in-line monitor and control the machining process. All tested metrology techniques showed promising results. To capture machining marks, the instrumentation needs to have lateral resolutions on the um level and a measurement area covering some cm; thus, the laser scanning system seemed to be a good compromise.

Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.

The influence of pulsed laser beam welding (LBW) parameters on the weld geometry and imperfections of a new nickel-based superalloy called G27 was studied by a statistical design of experiment, and the microstructures of the weld fusion zone (FZ) of the pulsed laser beam-welded G27 were characterized. No evidence of cracks is found in the FZ and heat-affected zone (HAZ). Other weld imperfections, such as undercut and underfill, were also hardly observed. The pulse factor significantly influenced all the responses, i.e., minimum weld width (Wm), root excess weld metal, and average pore diameter, whereas welding travel speed significantly influenced Wm and root excess weld metal. Power and interaction between pulse frequency*pulse factor were statistically significant in influencing the root excess weld metal and average pore diameter, respectively. The pulse frequency and interactions between power*travel speed, power*pulse factor, power*pulse frequency, travel speed*pulse factor, and travel speed*pulse frequency did not significantly influence any response. Microsegregation pattern that occurs during weld solidification leads to the formation of Nb-rich MC carbides and Nb-rich Laves phase as the major secondary phase constituents in the FZ of as-welded G27. The presence of brittle Laves phase requires careful consideration when developing suitable post-weld heat treatment of G27.

A continuously growing and mutating market with increasing demands on environmental sustainability, illuminated by unraveling environmental threats, highlights the need for greater resource efficiency in modern manufacturing industries. Machining has become pivotal in the pursuit of sustainability given its superior efficiency in manufacturing of metallic products.

In theory, the efficiency of a machining operation is dictated by the material removal rate and chip formation behavior. However, this may also present additional challenges as chips can be difficult to evacuate from the workpiece, and cause chip carryover. Chip management aims to contain manufacturing costs by addressing this issue and simultaneously pursuing higher machining efficiency. Persistent chips trapped in internal cavities is particularly challenging and can affect quality, and result in costly re-manufacturing, scrapping of portions of produced volume. Effective chip management strategies is therefore crucial to avoid these problems.

While there is established research on machining theory and chip removal, proposed solutions are often focused on a specific implementation and lack a holistic perspective on the phenomenon. This study aims to fill this gap in the knowledge by providing a strategic and practical application to tie together information that would otherwise be disaggregated. A case study was conducted to investigate the role of machining, and identify the source of chip carryover and its effect on subsequent production stages. Practical verification demonstrated the effectiveness of the proposed interventions in reducing production costs by decreasing the cycle time and reducing waste, and improving product quality. The future of sustainable manufacturing processes relies on the adoption of strategies and interventions that contribute to the efficiency and sustainability of the manufacturing industry. This study provides a valuable contribution to this effort.

During laser processing, complex effects can occur regarding the laser-material interactions. A high laser energy input leads to surface melting and even boiling. The resulting recoil pressure can create the so-called keyhole, a vapor channel existing during welding and called cut front during laser cutting. On the keyhole front wall, the induced recoil pressure pushes the melt downwards and can ejects melt drops. Usually, those melt ejections are seen as undesired spattering or necessary waste to enable the cutting. However, outflow characteristics can tell more about the complex process behavior. Therefore, this work aimed to relate melt ejection formation effects to keyhole behavior in order to get a better understanding of the complex laser-matter-interactions and fluid flows. Axial beam shaping was used to create different energy inputs into the keyhole front walls. Beam shaping was done with an optic that can superposition up to four laser beams in axial direction, leading to varying intensity distributions on the inclined keyhole front walls. Based on high-speed image analysis, it was seen that different outflow characteristics occur depending on the beam shapes. A high intensity on the front keyhole wall could be related to high temperatures on the keyhole wall. The outflow mechanism was shown to be able to move from corrugating to atomizing drop generation at increasing temperature due to temperature-dependent material properties. The main influencing factors are assumed to be the vapor speed and the keyhole/drop diameters that define the outflow mechanism.

Metal cutting is physically defined by a tool separating a chip by developing a stagnation point after the work material has passed a shear plane. This distinguishes the method group metal cutting or machining from shearing and wedging processes. The development of the stagnation point is central to the functioning of a metal cutting process. The stagnation point and its behavior has a great influence on the different load conditions of the tool as it controls both the temperature distribution and the mechanical load distribution around the tool’s edge-line. This publication shows that there is a temperature drop at the stagnation point where the shear stress changes sign, which means that the shear stress at this point assumes the value zero. Furthermore, it is demonstrated that there is a correlation between the position of the temperature dip, its size and the selected cutting data as well as tool geometry and current work material. The knowledge of the interaction between the position of the stagnation point and the tool coating type in combination with the tool’s micro- and macro-geometry will be of importance for the development of high-performance tools optimized for different machining applications.

Wire Arc Additive Manufacturing (WAAM) is developing rapidly in recent years due to its advantages, such as higher productivity, lower cost, acceptable quality, and the availability of advanced welding processes. Cold Metal Transfer (CMT), as the most well-known Gas Metal Arc Welding (GMAW) process, is widely used in WAAM. The uniqueness of CMT lies in minimizing the heat input of the process. However, there is a drawback to the lower heat input, impacting the quality of the geometry of the welding bead, sharp transitions at the weld toe, inclusions, etc., particularly for higher alloyed steel, e.g., tool steel. In this study, two processes were employed: CMT and Pulse Multi Control (PMC). Two types of shielding gases were used, namely 2% CO2 + 98% Ar and 20% CO2 + 80% Ar. Two levels of wire feed speed were selected: high and low levels. A full-fraction factorial experimental matrix was created, and bead-on-plate samples were produced with different GMAW processes, i.e., CMT and PMC. The geometry of the bead-on-plate, including penetration, bead width and height, and toe angle, was evaluated and analyzed. A correlation between the process factors (shielding gas, type of process, and wire feed speed) and the geometry of the bead was analyzed and determined. A protocol is proposed based on the study results for the selection of WAAM processes.

In future generation aviation, light weight, and thermally stable SiC/SiC ceramic matrix composites (CMCs) are considered the most promising structural materials to replace traditionally used Ni-based superalloys. However, in the presence of steam (a common combustion reaction product) and corrosive species (from ingestion of debris along with the intake air during take-off and landing), accelerated degradation of CMCs compromising its structural integrity is inevitable. Environmental Barrier Coatings (EBCs) are protective ceramic coatings consisting of rare earth (RE) silicates as a topcoat with silicon as a bond coat, and are widely used on CMCs, to impede their surface recession. Thermal spray techniques are commonly employed to deposit EBCs, with highly crystalline, dense, and crack free coatings being desired for robust performance. In general, the high particle velocity and efficient energy transfer in axial feeding systems can result in coatings with higher density, reduced oxide content, and improved mechanical properties. In the present study, axial plasma sprayed ytterbium disilicate (YbDS) coatings deposited on silicon carbide (SiC) substrates using varying plasma spray parameters have been comprehensively characterized. Microstructure, porosity, and hardness have been studied for YbDS coatings deposited by varying nozzle diameter, carrier gas flow rate and stand of distance (SOD) during plasma spraying. Erosion and thermal cyclic fatigue performance of these coatings has also been investigated.

Welding or brazing of metals to ceramics often leads to failures under aggressive conditions due to abrupt changes in physical, chemical, and thermal properties at the metal-ceramic interface. Metal-ceramic Functional Graded Materials (FGMs) replace the strict interface with a gradual transition of composition and properties, which protects the material from failures. The powder-blown Laser-Directed Energy Deposition (DED-LB) is one of the widely known Additive Manufacturing (AM) processes that offer unique features like developing FGMs and multi-material structures. Various studies have been conducted to process metal-ceramic FGMs using the DED-LB process but significant differences in thermal properties, varying laser-material interactions, and the possibility of formation of complex reaction products make the processing of metal-ceramic FGMs challenging. This study aims to understand the effect of laser power on a ceramic substrate, and its interaction with a metal powder introduced in the melt pool. A single track of nickel-based superalloy Alloy 718 powder was deposited on an Alumina substrate with different laser powers. The deposition was performed with and without substrate pre-heat to understand the effect of pre-treatment on deposition. Metallographic analysis was performed to reveal the microstructure of the resolidified metal mixed ceramic region.

Laser powder bed fusion (PBF-LB) processing of Haynes 282 using a layer thickness of 90-μm is studied for process capability and feasibility for increased productivity. To optimize the build rate while producing metallurgically sound parts, the effect of laser parameters on the resulting melt pool dimensions and porosity are analyzed. In a previous study, individual parameters such as laser power, scan speed and hatch distance and combination have been studied. In this study, three levels of hatch distance have been investigated while varying power and speed to same extent at all three (3) hatch distance. Similarly, the highest power level has been studied for the effect of speed and hatch distance. The melt pool dimensions, and porosity were measured in the plane parallel to the build direction. Comparison of measured responses with individual parameters provides partial trends of melt pool dimensions and porosity. It was observed that high power and/or low speed and low hatch distance increase the melt pool dimensions and reduce porosity until keyhole mode is reached. Variation of two parameters while keeping the third parameter constant was found to affect porosity due to the transition from lack of fusion type defects to keyhole porosities. The presence of dominant type of defect is identified using average feret ratio, which is the ratio of maximum length to minimum length across parallel surfaces inside a defect. It is found successful to use the combination study of process parameters and resultant type of defects to map the process to achieve minimal defects is identified for 90-μm layer thickness, through the combination study of process parameters and the resultant type of defects.

In the emerging field of Additive Manufacturing (AM), the promise of unparalleled design flexibility, resource efficiency, and rapid prototyping has captivated both industry and academia. While AM techniques offer a wide range of manufacturing possibilities, they also present unique challenges in ensuring structural integrity and material properties. Non-Destructive Testing (NDT) methods, including Ultrasonic Testing (UT), have emerged as invaluable tools for evaluating the internal structure of AM components without compromising their integrity. By employing NDT techniques, it is possible to detect flaws such as porosities, cracks, and other inhomogeneities early in the manufacturing process, thereby improving reliability, extending the lifespan, and reducing the overall environmental footprint of AM products. While the occurrence of defects from processes such as welding is well-established, documented and standardized with regards to NDT, a knowledge gap exists for defects in the field of AM. Specifically, reference reflectors commonly used in the industry, such as side-drilled holes and flat bottom holes, are well understood when machined into components using traditional (subtractive) means. AM offers more flexibility, e.g., adding closed internal reference reflectors directly from the build-process. Twelve straight blocks were manufactured using Laser Powder Bed Fusion (PBF-LB) with carefully selected artificial defects. All defects were created by CAD (Computer Aided Design) seeding, i.e., introducing voids into the CAD-model. The blocks were inspected using Phased Array Ultrasonic Testing as well as conventional ultrasonic testing. It was shown that the as-built surface of PBF-LB has an adverse impact on the ultrasonic testing signal response, and the detectability of defects was quantified under the different conditions (machined surface compared to as-built). It was shown that the build direction has an impact on the morphology and the UT signal response from internally seeded defects.

This publication describes a concept that intends to enable an optimization of machining with regard to the balance between criteria related to technology, economy and sustainability. The work is of a discussion nature and intends to provide a framework for further research and development in the area. Previous research and development during the 80’s and 90’s is presented in general terms and in particular the reasons for its limited success in providing real-time feedback on machining operations are highlighted, despite very large financial investments even by today’s standards. Ongoing research worldwide in current process optimization and its associated building blocks will be highlighted, and identified important work is referenced. Below are new conditions that can be linked to both process knowledge and its modeling, as well as new conditions for developing integrated sensors that can handle the extreme environment in and around a processing operation. A previous limiting factor has been signal processing and signal transmission, which with new knowledge and developed technology in the last 10 years provides new conditions for process optimization in real time.The need for new and up-to-date principles for process optimization, which also integrate sustainability issues and environmental impact, has increased in importance in several respects. Important issues such as tool utilization, efficient use of materials and high time utilization have become relevant as these process results control both energy consumption and environmental impact. The geopolitical development linked to the availability of critical tool materials such as cobalt and tungsten also drives research issues that can generally optimize and streamline production processes.Finally, the publication describes the possibility of realizing a real-time feedback and optimized machining that takes into account technology, economy and sustainability, through interdisciplinary research across several levels of technology readiness (TRL). The results are expected to have positive effects on several production factors before, during and after machining. Developed technologies in machining can also make valuable contributions to the development of other products and production processes.

Many industries are today heavily exposed to competition which increases the demand for continuous innovations, faster product changes and continued improvements, this is especially true for the automotive industry. Such demands raise the complexity and set a need for continuous training and development of our operators and assembly personnel to keep up with new designs and product changes. This, in combination with an aging population and a growing shortage of experienced assembly workers, increases the need for efficient training capabilities.Today most of the operator training is supervisor driven and takes place in the live production environment working with real products. This approach might introduce uncertainties and a risk to the production system as less experienced workers, still in training, might jeopardize quality, ramp ups and takt time. With the rise of virtual reality there are growing possibilities to carry out these training sessions in a more secure, non-disruptive, virtual environment without jeopardizing ramp ups, takt time or quality. This paper evaluates the possibility to introduce virtual multi-user operator training as an alternative to traditional supervised “on-site” training for assembly workers. Recreation of different assembly task from an automotive case company was created in virtual reality while introducing multi-user functionality to allow multiple operators and supervisors to observe, instruct and evaluate the performance of the operator in training. The developed demonstrator is used as the discussion basis throughout a focus group interview study with selected participants from an OEM case company and the potential of a multi-user virtual reality application as a complement for traditional operator training in operator training is discussed and future research directions for multi-user virtual reality trainings at OEMs is presented.

Production systems of the future may be in constant flux and reconfiguration, continuously adapting to changing production conditions. Digital models and simulation are powerful tools that can be used for their design and operation. These models must co-evolve with the physical system to sustain their usefulness and relevance. This poses a significant barrier, given the complexities involved in their efficient creation and maintenance. To understand whether certain system design concepts make the simulation process easier, this study aims to investigate a combination of concepts that promote reconfigurability and flexibility to explore whether they can positively influence the simulation process. By integrating modularization, interface standardization, and a service-oriented architecture it is believed to support faster and easier creation and updates of digital models. Modularization enhances flexibility by decomposing complex systems into independent, interchangeable modules. Standardizing interfaces ensures uniformity and compatibility among modules. Using a service-oriented architecture entails the encapsulation of various functionalities within modules as services, which can be dynamically requested. Shedding light on the advantages arising from modeling and simulating systems adhering to the mentioned concepts the research also aims to lay the groundwork for further investigation into the potential synergies of these promising production concepts. The study’s methodology includes modeling and programming of industrial robotic production modules adhering to predefined physical and logical interfaces. Interoperability and service orchestration are achieved through a service-oriented architecture. A simulated Manufacturing Execution System is integrated to facilitate handling of module services, product data and service requirements. Finally, a specialized software plugin was developed to support rapid module instantiation into a production system for evaluation. Results suggest that using a modular approach may ease modelling and simulation efforts and could be supported further by developing tailored tools for rapid system development.

The demand for customized products in a saturated market of trendy customers forces the manufacturing industries to transform their manufacturing from a high volume of uniformed products to low volumes and a high mix of products. High mix and low volume manufacturing is most often manually performed since existing automation solutions are only profitable for mass manufacturing, due to explicitly designed control software where the product data is implemented as low-level control code. Highly flexible automated manufacturing systems such as Plug & Produce are requested, but challenges still exist before industrial implementation. This article proposes a digitally configurable system where data for new or modified products data is configured from the perspective of the product and its manufacturing processes instead of the manufacturing resources. In a Plug & Produce system, process modules with manufacturing resources are easy to replace for new or modified products and possibly to duplicate if higher capacity is needed. Configurable multi-agent systems are proposed by several researchers as a control system for Plug & Produce. An agent is a piece of autonomous computer code that negotiates with other agents and concurrently solves tasks, distributed on parts and resources. A part is a part of a product and part agents handle manufacturing goals for the parts. Resource agents know their capability and start operating as soon as they are plugged in. Resource agents follow pluggable process modules containing manufacturing resources and act as drivers for the modules. Gantry robots have by design a naturally orthogonal coordinate system and most often lack the functionality to handle work and tool coordinate objects as standard industrial robots do. Work objects refer to a base coordinate system and tool objects contain a reference to the tool center point. These references are in this article integrated into resource agents together. A place coordinate agent has the global perspective of the Plug & Produce cell and provides the process modules with reference coordinates of the place they are plugged into. Coordinates are recalculated from a product perspective into a resource perspective by coordinate transformations built into the skills of resource agents. This structure enables the possibility for process planners in the manufacturing company to make changes on a daily basis. A test with a gantry robot Plug & Produce demonstrator was performed and presented in this article to verify the generic structure of the gantry robot control system into agents.

The integration of production automation drives innovation in manufacturing by enhancing efficiency, quality, and cost reduction. However, the capital requirements of conventional automation solutions hinder many manufacturing companies. Production Automation as a Service (PAaaS) emerges as a cost-effective alternative, offering improved flexibility and efficiency. Yet, adopting PAaaS faces challenges: a lack of expertise, awareness, and cultural resistance. This study explores PAaaS implementation in manufacturing, identifying its specific needs and challenges. Qualitative research across ten diverse manufacturing companies reveals two key drivers: technological advancement and evolving business models. It highlights four primary benefits—cost-effectiveness, flexibility, efficiency, and product quality. Simultaneously, it addresses five significant challenges—legacy system integration, cybersecurity, internet dependency, expertise gaps, and downtime risks. To aid early decision-making, the study proposes a framework covering drivers, benefits, challenges, and suitable strategies. This study contributes to the ongoing discussion on smart production and automation development by focusing on business model innovation and the pay-as-a-service approach.

The use of automation is reshaping tasks in diverse industries, leading to increased productivity and efficiency. The manufacturing sector, in particular, has enjoyed significant advantages from automation, including enhanced quality control, waste reduction, and improved worker safety. However, while the advantages of automation in manufacturing are clear, the implementation of automation in complex manufacturing processes is not without its challenges. One such challenge is ensuring adaptability to new products. In addition, the initial investment for automation in manufacturing processes often presents a significant financial difficulty, particularly in the areas of engineering, design, and programming. The aim of this paper is to provide flexible solutions that can be adopted on any manufacturing line within a short timeframe. This type of flexible solution is referred to as Automation Packaged Solution (APS). APSs involve the deployment of robotic systems and vision technologies to automate specific tasks. The key advantage of these flexible solutions is their ability to adapt to the introduction of new products into the production line without the need for extensive reengineering and reprogramming. The approach involves designing detailed computer simulations based on the initial solutions and bringing the solution to life through an offline commissioning method. In this research, a case study was conducted at a manufacturing plant in Sweden, where two APSs were introduced to their assembly line: Precise screwing and accurate application of product labeling. These APSs play a crucial role in facilitating rapid upgrades and adjustments to automation systems, especially considering the diverse range of product models. This adaptability reduces the time and resources required for reconfiguration and contributes to enhanced operational efficiency, cost-effectiveness, and more sustainable manufacturing solutions. Moreover, it opens up the possibility of transferring these APSs to another production line if the need arises.

Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data.

This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions.

Digital transformation of production systems is a challenging task that demands radical responses from existing organizations. During the digital transformation of productions systems tensions occur that need to be managed and the purpose of this paper is to identify paradoxes in the digital transformation of production systems. Paradox theory has been applied as an analytical framework when identifying digital transformation paradoxes and tensions. A case study has been conducted and two manufacturing companies’ digitalization projects have been studied and analyzed in combination with data from workshops around digital transformation. The results were mapped into four types of paradoxes: organizing, performing, belonging, and learning. We conclude that the identified tensions are intertwined, and a major tension is the degree of standardization of technologies (standardization vs customization) and a more agile way of working (learning by doing vs learning before) doing is a trend within the digital transformation of production system. Our findings are relevant to operations managers and others interested in tensions during the digital transformation of production systems.

In the evolving landscape of modern manufacturing, a novel concept known as Feeding-as-a-Service (FaaS) is emerging, part of the larger Automation-as-a-Service (AaaS) framework. FaaS aims to optimize feeding systems in cloud manufacturing environments to meet the demands of mass customization and allow for quick responses to production changes. Therefore, it fits into the Manufacturing-as-a-Service (MaaS) system as well. As the manufacturing industry undergoes significant transformations through automation and service-oriented models, understanding how FaaS fits into the other frameworks is essential.

This study presents a systematic literature review with two primary objectives: first, to contextualize FaaS within AaaS and MaaS, highlighting similarities, differences, and distinctive characteristics; second, to identify and clarify the essential Key Performance Indicators (KPIs) crucial for its strategic implementation.

KPIs are pivotal metrics guiding organizations toward manufacturing excellence. In this context, common KPIs focus on efficiency and quality, such as resource utilization, and error rates. Other KPIs are also crucial, such as the ones related to cost reduction and customer satisfaction. For FaaS, the most relevant include also data security, data management, and network speed.

This research provides a valuable KPI framework for FaaS developers, aiding in strategic decision-making and deployment in industrial settings. It also contributes to a broader understanding of KPIs in manufacturing, which benefits both researchers and industrial practitioners.

The results of the review, though, fail to address other crucial indicators for ‘as-a-Service’ business, such as Churn Rate and Total Contract Value. Future research will address these limitations through methods ranging from questionnaires to practitioner interviews, with the aim of gathering the knowledge needed for real-world implementations.

Knowledge graphs are generating significant interest in industry and research. These graphs can be enriched with data to represent aspects of production systems such as their structure, component interrelationships, and conditions. This provides opportunities to gain insights into system behavior, performance, and states. Such insights could potentially be leveraged by a wide range of technologies for a multitude of purposes and applications such as system control, process optimization, and informed decision making. However, the existing literature addressing industrial applications of knowledge graphs related to production systems remains limited in scope and depth. This underscores the importance of developing methods for exploring the potential use and implementation of knowledge graphs in such systems. The primary focus of this study centers on facilitating such exploration by developing a virtual commissioning simulation framework. A modular production system is modelled that leverages physics, moving product dynamics, and incorporates authentic PLC and robot programs. A knowledge graph is integrated and enriched with data representing various aspects of the system. An application is developed to facilitate product routing and prioritization. A service-oriented approach is used that leverages graph data processing and exchange for service registration and matching. System simulations are conducted and subsequently the framework is evaluated for outcomes and findings. This study demonstrates the successful design and implementation of a production system simulation framework that uses knowledge graphs for system functionality. It demonstrates the exploration of knowledge graph applications through the development of a modular and service-oriented system that includes system functionality supported by the graph. The results highlight the potential of simulation suggesting its capacity for valuable exploration regarding potential applications of knowledge graphs within production systems.