Industrial Case Study Research Articles

Experimental and numerical investigation of the deep drawing process using a tractrix die – An industrial case study focused on stress and temperature analysis

Experimental and numerical investigation of the deep drawing process using a tractrix die – An industrial case study focused on stress and temperature analysis

An interpretable operating condition partitioning approach based on global spatial structure compensation-local temporal information aggregation self-organizing map for complex industrial processes

Effective and interpretable partitioning of operating conditions in complex industrial processes is crucial for ensuring the efficiency and reliability of emerging integrated production processes. However, contemporary operation–condition partitioning models fall short in this domain for two main reasons. First, the parameters related to purely data-driven methods fail to involve physically meaningful semantics, resulting in suboptimal accuracy and interpretability. Second, there exists a spatial–temporal coupling interaction among process variables that reflects physically meaningful spatial–temporal correlations, further impeding effective interpretation. To address these challenges, an interpretable operation–condition partitioning method based on a Global Spatial Structure Compensation-Local Temporal Information Aggregation Self-Organizing Map is proposed. This approach formulates the operation–condition partitioning problem as a spatio-temporal feature extraction based on the operation–condition partitioning problem. First, a local separable aggregation (LSA) model based on the informer backbone is introduced to partition industrial variables into interpretable time-local groups. This ensures that the model can learn the underlying factors of the condition’s occurrence, thereby providing a reliable basis for interpretable root cause analysis. Next, a Global Spatial Feature Compensator (GSFC) is introduced that leverages the process mechanism knowledge and graph network to establish explicit spatial interrelationships among time-local groups in the LSA algorithm, thereby enhancing the model's inferential capabilities and security assurance. Subsequently, a spatiotemporal self-organizing map is designed to integrate the aforementioned spatial and temporal features, facilitating precise operation–condition partitioning decisions. Finally, the effectiveness and understandability of the proposed method are validated through comprehensive experiments in a real industrial application case study.

Integrated quality 4.0 framework for quality improvement based on Six Sigma and machine learning techniques towards zero-defect manufacturing

PurposeThis research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”Design/methodology/approachThe research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.FindingsThis research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.Originality/valueThis research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development.

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.

The Role of Threat Intelligence in Enhancing Cybersecurity Posture

Organizations confront more complex and persistent cyberattacks in the quickly changing cyber threat landscape of today. Through the provision of actionable insights on emerging threats, adversary tactics, and vulnerabilities, cyber threat intelligence (CTI) becomes an increasingly important component in strengthening the cybersecurity posture of organizational networks. The importance of CTI and its useful uses in enhancing cybersecurity defences are examined in this research study. It explores the several kinds of threat intelligence, methods for compiling and evaluating threat information, and proactive steps that businesses may take to make the most of CTI. This study emphasizes the significance of incorporating CTI into cybersecurity plans to more effectively identify, prevent, and respond to cyber attacks through a thorough review of industry practices and case studies. Organizations may improve their defences against cyberattacks and enhance vital assets and data by comprehending the role that CTI plays in cybersecurity.

Designing a green-resilient closed-loop supply chain to preserve business continuity under interrelated disruptions (a case study of the automotive battery industry)

Improving profitability, implementing the recycling cycle to reduce resource consumption, and managing interrelated disturbances to maintain business continuity are important issues for supply chain management. Accordingly, this study proposes a multi-objective optimization model to design a green-resilient closed-loop supply chain considering pricing decisions under multiple interrelated disturbances. Herein, the fuzzy c-means clustering method is employed to lessen probable scenarios and cluster main suppliers and target markets to reduce mathematical complexity. The multi-objective model is solved by adopting the improved augmented ε-constraint method to achieve the minimum total cost, total environmental impact, and network non-resiliency. The mixed uncertainty is coped with by developing a novel robust optimization method. Finally, an automotive battery manufacturing industry case study is applied to the model. The results reveal that the mutual effects of disturbances intensify the deterioration of objective functions. Also, the dynamic pricing of products reduces the financial vulnerability of the supply chain.

Exploring smart quality predictive modelling approach: a case study of the injection-molding industry

Technology-driven quality monitoring and control can effectively predict, prevent and reduce defects in the manufacturing industry and improve productivity. The study aims to explore real-time injection molding process monitoring and demonstrate intelligent quality control through a case study. The process environment is monitored to capture variabilities through which the relationship of variables with the quality characteristics of molded parts is derived. The defects are represented as a function of process variables using statistical analysis of the past process and product data employing appropriate machine learning methods. From the fitted models, decision rules are retrieved, and desirable process conditions required for making defect-free molded parts are recommended for quality control practice. Further, these models are deployed to predict the defects in the parts during production by observing the real-time process conditions in the manufacturing process environment. This study has drawn significant research and practical implications for the manufacturing industry as it can effectively control quality and automatically fine-tune the process for better quality and productivity.

Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty

ABSTRACT One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs.

A model-based approach for self-adaptive security in CPS: Application to smart grids

Security risk assessment is an important challenge in the design of Cyber Physical Systems (CPS). Even more importantly, the intrinsically dynamical nature of these systems, due to changes in their environment, as well as evolutions in their infrastructures, makes them self-adaptive systems, where security aspects have to be considered in terms of management of detections and reactions for self-protection. In this work, we propose an approach to autonomously mitigate the threats in each reconfiguration at application or infrastructure levels of CPS. We propose and implement a framework for self-adaptive security: software architecture, design method, and integration with model-based decision. We use Attack-Defense Trees for modeling threats, and our approach involves security risk assessment, taking into account its balancing and coordination with quality-of-service aspects. We formulate and formalize the on-line decision problem to be solved at each cycle of the self-adaptation control loop in terms of Constraint Programming (CP) modeling and resolution. The CP model implements a set of constraints that allow to specify secure configurations, evaluated regarding their impact on system performance to pinpoint the most relevant one portraying a good balance between the security and quality of service. We perform validation of our approach with its application to Smart Grids, more particularly to an industrial case study from RTE (the French Energy Transmission company).

Learn, Unlearn and Relearn: Adaptive Cybersecurity Culture Model

In the ever-evolving cyberspace landscape, organisations face persistent threats that continuously mutate and adapt. To effectively defend against these dynamic cyber threats, a fundamental shift in cybersecurity culture is imperative. This paper presents a novel Adaptive Cybersecurity Culture Model (ACCM) that encapsulates the principles of "Learn, Unlearn, and Relearn" as a strategic stance to foster resilience and adaptability in the face of evolving cyber threats. The ACCM emphasizes the importance of continuous learning as the cornerstone of cybersecurity culture. It advocates the adoption of a growth mindset within organisations, encouraging employees to stay updated with emerging threats, technologies, and best practices. However, the model goes beyond mere learning; it underscores the significance of unlearning outdated practices and misconceptions that may hinder effective cybersecurity. Furthermore, the ACC model introduces the concept of "Relearn," emphasizing the need to rapidly adapt and evolve strategies and tactics in response to ever-changing cyber threats. It promotes a culture of agility and adaptability, enabling organisations to respond effectively to both known and unforeseen cyber challenges. The model presented in this paper draws from case studies of various industries to illustrate the successful adoption and transformation of cybersecurity culture using the Learn, Unlearn, and Relearn principles. The ACCM represents a paradigm shift in cybersecurity culture, acknowledging that the ability to adapt is as critical as the ability to protect. By fostering a culture of continuous learning, unlearning, and relearning, organisations can proactively enhance their cyber resilience and effectively defend against the ever-evolving cyber threat landscape. This paper provides a roadmap for organisations to embark on this transformative journey toward a more adaptive and resilient cybersecurity culture.

Genesis, mechanism, and avoidance of cosmetic coating defects – An industrial case study

Film-coated tablets as solid oral dosage forms are a well-accepted way of administering drugs but are not without specific challenges during manufacturing. One relevant criterion of the final product is the visual integrity and therewith, the absence of cosmetic optical defects such as edge chipping. The aim of the present study was to examine the origin of those edge chipping defects, which were observed during commercial manufacturing of film-coated tablets, and to provide recommendations for process optimization to reduce the defect occurrence. The unraveling of the herein described phenomenon necessitated an interplay of in-depth material characterization, discrete element modeling (DEM) as well as an in-house developed optical measurement system for the automated quantification of tablet defects. As a result of this investigation, the automatic unloading step after the tablet coating process was identified as the most critical step for the occurrence of chipping defects and a replacement by manual unloading was proposed to reduce the defect propensity. The recommended optimization was subsequently confirmed in several manufacturing runs and a reduction of defect propensity by a factor of 5 was observed, highlighting the relevance and the impact of the performed thorough investigation.

Rigorous Security Analysis of RabbitMQ Broker with Concurrent Stochastic Games

Modern Internet of Things architectures encompass various computational logic and communication protocols. However, the security issues inherent in these systems pose significant risks, especially at the device edges. Addressing security threats on the deployed communication edges is imperative to ensure a robust and secure communication system. In this study, we propose an approach that utilizes the Concurrent Stochastic Game model (CSG) to specify the behavior of RabbitMQ Broker in the context of IoT systems precisely while considering potential data corruption attacks. For parametrizable evaluation, these attacks and their frequencies are learned. We implement the CSG model in PRISM games for automated analysis, leveraging reward Probabilistic Alternating Temporal Logic (rPATL) to model security requirements as game goals. Empirical validation of the work is presented via an industrial case study that shows how data corruption attacks can impact the sensed data in water dam infrastructure. This assessment gives valuable insights into the RabbitMQ deployment at the edge.

Investigating the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of musculoskeletal disorders: A case study in a car-parts manufacturing industry.

Work-related musculoskeletal disorders (WRMSDs) is a multi-factorial disorder in most occupational setting and it has increased significantly in recent years. This study aimed to investigate the relationship between physical, cognitive, and environmental factors of ergonomics with the prevalence of WRMSDs in a car-parts manufacturing industry. This cross-sectional study was performed among 220 workers in a milling unit of a car parts manufacturing company in 2021-2022. The prevalence of WRMSDs was assessed using the Extended Version of the Nordic Musculoskeletal Questionnaire. Noise exposure was evaluated using dosimetry method. Mental and physical workload were evaluated by the NASA-TLX and key index methods (KIM-MHO and KIM-LHC), respectively. Data analysis was performed using SPSS version 25.0. The subjects' mean age and work experience were 36.3±6.5 and 8.35±6.41 years, respectively. Eighty-five percent of the subjects reported WRMSDs in at least one area of the body. The results of mental workload assessment revealed a high workload mean range (73.23±14.89) in all of the subjects. Mean score of KIM-LHC and KIM-MHO were 738.18±336.42 and 201.86±36.41, respectively with odds ratio of 1.32 for KIM-LHC in creating the WRMSDs. There was a significant relationship between the noise exposure, mental and physical workload and the prevalence of WRMSDs (p-value < 0.05). The results of the present study revealed that environmental, physical and cognitive factors can simultaneously be effective in the prevalence of WRMSDs. Therefore, performing effective control measures requires comprehensive attention to physical, environmental, and cognitive ergonomics in the algorithm of ergonomics management in the workplace.

Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

User journey games: automating user-centric analysis

The servitization of business is moving industry to business models driven by customer demand. Customer satisfaction is connected with financial rewards, forcing companies to invest in their users’ experience. User journeys describe how users maneuver through a service. Today, user journeys are typically modeled graphically, and lack formalization and analysis support. This paper proposes a formalization of user journeys as weighted games between the user and the service provider and a systematic data-driven method to derive these user journey games from system logs, using process mining techniques. As the derived games may contain cycles, we define an algorithm to transform user journeys games with cycles into acyclic weighted games, which can be model checked using to uncover potential challenges in a company’s interactions with its users and derive company strategies to guide users through their journeys. Finally, we propose a user journey sliding-window analysis to detect changes in the user journey over time by model checking a sequence of generated games. Our analysis pipeline has been evaluated on an industrial case study; it revealed design challenges within the studied service and could be used to derive actionable recommendations for improvement.

A Process for Monitoring the Impact of Architecture Principles on Sustainability: An Industrial Case Study

Architecture principles affect a software system holistically. Given their alignment with a business strategy, they should be incorporated within the validation process covering aspects of sustainability. However, current research discusses the influence of architecture principles on sustainability in a limited context. Our objective was to introduce a reusable process for monitoring and evaluating the impact of architecture principles on sustainability from a software architecture perspective. We sought to demonstrate the application of such a process in professional practice. A qualitative case study was conducted in the context of a Dutch airport management company. Data collection involved a case analysis and the execution of two rounds of expert interviews. We (i) identified a set of case-related key performance indicators, (ii) utilized commonly accepted measurement tools, and (iii) employed graphical representations in the form of spider charts to monitor the sustainability impacts. The real-world observations were evaluated through a concluding focus group. Our findings indicated that architecture principles were a feasible mechanism with which to address sustainability across all different architecture layers within the enterprise. The experts considered the sustainability analysis valuable in guiding the software architecture process towards sustainability. With the emphasis on principles, we facilitate industry adoption by embedding sustainability in existing mechanisms.

Redesign for flexibility through electrification: Multi-objective optimization of the operation of a multi-energy industrial steam network

Steam networks are attractive for contributing to the decarbonization of energy systems. The partial or complete electrification of steam generators is expected to reduce direct carbon emissions by replacing fossil fuels. Still, this solution poses challenges in the operation of industrial sites. Most studies on the optimization of steam networks focus on single-period problems that disregard the dynamic operation of flexibility and consider only costs or profit as their objective function. In this study, we analyze the effects of the electrification of steam generators in a multi-period, multi-objective problem, which accounts for profit from energy exchanges, direct and indirect emissions, and steam blow-off. To that end, we use data from an industrial steam network and model it as a multi-objective mixed integer linear program. Five different design scenarios based on flexibility measures in steam networks are formulated with data from an industrial case study. Each problem is solved iteratively using the AUGMENCON-R algorithm to obtain points in the Pareto front efficiently. Electrification together with the inherent storage capacity of the steam network resulted in a potential average profit increase of 488 EUR/day, a potential average emissions reduction of 17.8 tCO2/day, and a potential average blow-off steam reduction of 5.7 t/day. Furthermore, our multi-objective approach reveals an operational carbon emissions abatement cost ranging from 62.5 to 363.6 EUR/tCO2, depending on exogenous data.

The carbon-energy-water nexus of the carbon capture, utilization, and storage technology deployment schemes: A case study in China's cement industry

Carbon capture, utilization, and storage (CCUS) technology plays a crucial role in the transition towards low-carbon emissions in the cement industry. However, it also results in numerous energy and water consumption. To formulate proper CCUS deployment strategies, it is essential to optimize spatial CCUS layouts and examine the carbon-energy-water nexus of the technology. This study combines China's cement emission inventory, representing carbon sources, with carbon storage sites data, and develops a source-sink matching model to determine optimal CCUS deployment schemes under different mitigation target scenarios. In addition, the spatial distribution of energy and water consumption impacts, as well as the carbon-energy-water nexus, are evaluated. Results indicate that the deployment of CCUS in 229, 445, and 627 cement plants is necessary to mitigate 20%, 40%, and 60% of carbon emissions in China's cement industry, respectively. The estimated economic costs of these schemes are 79.4, 167.9, and 269.5 billion CNY, posing a significant financial challenge for the industry. Carbon capture technologies greatly influence the carbon-energy-water nexus of the CCUS deployment schemes, with net energy and water consumption ranging from 271.5 to 2410.9 PJ and − 28.5 to 872.4 million m3. Totally, 13–15 provinces achieve synergic carbon mitigation and water extraction effects by using the technologies with low water intensity. Nevertheless, trade-offs between carbon and energy exist regardless the technology. In conclusion, this study provides valuable insights for planning CCUS deployment schemes by revealing the multi-dimensional impacts on economic costs, energy consumption, and water consumption, which supports the decarbonization of China's cement industry.

Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet – An industrial case study of attempting to comply with EU regulation 2022/63

Recently, concerns have been raised about the safety of titanium dioxide (TiO2), a commonly used component of pharmaceutical film coatings. The European Union has recently prohibited the application of this material in the food industry, and it is anticipated that the same will happen in the pharmaceutical industry. For this reason, pharmaceutical manufacturers have to consider the possible impact of removing TiO2 from the film coating of tablets. In this paper, we present a case study of a commercially produced tablet where the film coating containing TiO2 was replaced with a coating using calcium carbonate (CaCO3) or with a transparent coating. The performance of the coatings was compared by measuring the moisture absorption rate and the dissolution profile of the tablets. In these regards, there were negligible differences between the coating types. The tablets contained a highly photosensitive drug, the ability of the coatings to protect the drug was evaluated through environmental stability and photostability measurements. The HPLC results showed that the inclusion of TiO2 does not provide additional benefits, when humidity and thermal stress is applied, however its role was vital in protecting the drug from external light. There were several decomposition products which appeared in large quantities when TiO2 was missing from the coating. These results imply that photosensitivity is an issue, replacing TiO2 will be challenging, though its absence can be tolerated when the drug does not need to be protected from light.

A review of the current state-of-the-art on in situ monitoring in electron beam powder bed fusion

The industrial development of electron beam powder bed fusion (PBF-EB) is relatively younger and much more limited in terms of global widespread and revenues compared to laser powder bed fusion (PBF-L). Nevertheless, PBF-EB has been adopted in some of the most successful industrial case studies of metal AM, as it provides specific benefits and capabilities that make it a key enabling technology in a variety of industrial applications. Moreover, the recent years have seen a rapid evolution with new actors and new systems entering the market, together with a considerable increase of research and innovation programs. A field of major interest is the development and continuous improvement of in situ sensing and monitoring methods to anticipate the detection of defects, to predict the final quality of the part, and to rethink product qualification procedures. The technological features of the PBF-EB process have motivated the development of solutions that differ from the ones in PBF-L. Some of them have reached a good maturity level, being recently integrated into industrial machines, while others still deserve further research. This study explores the current state-of-the-art on in situ and in-line monitoring of the PBF-EB process, aiming to provide an up-to-date overview of the major differences with respect to PBF-L, currently available methods and their performances, as well as open issues, challenges to be tackled, and perspective for future research and industrial developments.