Product Lifecycle Data Research Articles

Reliability oriented key quality characteristics driven integrated built-in reliability activity chain and approach for manufacturing process

Product reliability is determined at the design phase and built into the product through manufacturing process. To effectively implement reliability activities at each stage, reliability data in product life cycle is integrated with KQC (key quality characteristics), and the concept of R-KQC (reliability-oriented KQC) is proposed. Based on the R-KQCs, this study presented an integrated Built-in reliability (BIR) approach to ensure that the product reliability formed losslessly conforms to the reliability goal determined in the design phase. First, the connotations of BIR and BIR-oriented RQR (manufacturing system Reliability, manufacturing process Quality, and product Reliability) chain are proposed by considering the evolution mechanism of product reliability in the life cycle driven by the R-KQCs. Second, a product BIR activity chain consisting of plan, synthesis, and improvement based on R-KQC is presented by the BIR-oriented RQR chain. Third, an R-KQC-driven BIR assurance technology framework based on the BIR activity chain is proposed, and a product reliability modeling considering the deviation of R-KQCs is established based on the framework. Finally, the validity of the proposed approach is verified using the reliability assurance for an automobile engine cylinder head as an example.

Digital Twin-Driven Product Sustainable Design for Low Carbon Footprint

Abstract Product sustainability is a pressing global issue that requires urgent improvement, and low-carbon design is a crucial approach toward achieving sustainable product development. Digital twin technology, which connects the physical and virtual worlds, has emerged as an effective tool for supporting product design and development. However, obtaining accurate product parameters remains a challenge, and traditional low-carbon product design primarily focuses on design parameters. To address these issues, this paper proposes a method for data collection throughout the product lifecycle, leveraging the Internet of Things. The paper envisions the automatic collection of product lifecycle data to enhance the accuracy of product design. Moreover, traditional low-carbon design often has a limited scope that primarily considers product structure and lifecycle stage for optimization. In contrast, combining digital twin technology with low-carbon design can effectively improve product sustainability. Therefore, this paper proposes a three-layer architecture model of product sustainability digital twin, comprising data layer, mapping layer, and application layer. This model sets the carbon footprint as the iterative optimization goal and facilitates the closed-loop sustainable design of the product. The paper envisions sustainable product design based on digital twins that can address cascading problems and achieve closed-loop sustainable design.

Stop Guessing in the Dark: Identified Requirements for Digital Product Passport Systems

The Digital Product Passport (DPP) is a concept for collecting and sharing product-related information along the life cycle of a product. DPPs are currently the subject of intense discussion, and various development efforts are being undertaken. These are supported by regulatory activities, especially in the case of the battery passport. The aggregation of product life-cycle data and their respective use, as well as the sharing of these data between companies, entrepreneurs, and other actors in the value chain, is crucial for the creation of a resource-efficient circular economy. Despite the urgent need for such a solution, there is currently little attention given to the digital infrastructure for the creation and handling of the DPPs (i.e., the so-called DPP system). Moreover, there is so far no common understanding of what the requirements for a DPP system are. This is the background and underlying motivation of our paper: we identify the requirements for a DPP system in a structured way, i.e., based on stakeholder involvement and current literature from science and industry. In addition, we compose, categorize, and critically analyze the results, i.e., the list of requirements for DPP systems, in order to identify gaps. Summarized, our research provides insights into the criteria to be considered in the creation of an actual DPP system.

Product Life Cycle Data Management and Analytics in RAMI4.0 using the Manufacturing Chain Management Platform

The RAMI4.0 model consists of three axes; the Layers axis, the Hierarchy axis, and the Life Cycle Value Stream axis. This model unifies the various aspects of I4.0 to allow data generated from manufacturing and business activities to be shared effectively. The Manufacturing Chain Management software aims to provide a platform where all three axes and their associated data are tightly integrated and can be used to provide I4.0 connectivity as well as insights into the manufacturing supply chain. A data framework is proposed whereby the data from these activities can be collected and used to make more insightful decisions about an organisations value chain, value stream, and the life cycle of their product portfolio. The MCM platform is aimed at SMEs, which generally has lesser financial ability to invest into I4.0 technologies. Hence, the MCM platform is designed to be flexible and scalable whilst maintaining compliance with international I4.0 standards. A furniture factory in Selangor, Malaysia has been chosen to implement the Value Stream Mapping aspect of the life cycle management tools in their manufacturing line to test its effectiveness.

Internet of Things for building façade traceability: A theoretical framework to enable circular economy through life-cycle information flows

Traceability is considered a crucial requirement to enable Circular Economy (CE). Product and process life-cycle data can facilitate circular asset management preserving the asset's value over time and reducing resource consumption. Many scholars point out how the loss of traceability data, lacking information reliability, and unstructured data are still barriers to the widespread application of CE. In the building façade sector, an increased interest on traceability is dictated by a growing demand for environmental product certifications. However, these aspects are often limited to collect data at supply chain stage, thus neglecting a huge amount of information produced during the asset service life. To foster an accessible and life-cycle oriented asset traceability, this research investigates the Internet of Things (IoT) as a potentially disruptive technology for supporting information management. The objective of this work is twofold: (i) to identify what façade life-cycle information is needed to promote CE and (ii) to clarify the enabling role of IoT in tracking, storing, and sharing such information. Through a scoping review combined with interviews to professionals, a theoretical framework structured on four key elements (stakeholders, information list, information management tools, and IoT) is proposed to fill the literature gap and support façade industry in the circular transition. Further research will have to be conducted to face the digital-physical integration issues and develop business models able to fully exploit traceability information value.

Data-driven cleaner production strategy for energy-intensive manufacturing industries: Case studies from Southern and Northern China

Cleaner production is an effective strategy for improving material utilization, reducing energy consumption, maximizing product output, and minimizing emissions during the entire manufacturing processes. An increasing number of enterprises promote the implementation of cleaner production. In the Industry 4.0 context, product-embedded information devices are widely used to capture product lifecycle data. Product lifecycle management provides an opportunity to achieve cleaner production strategy. This paper presents a data-driven cleaner production strategy by product lifecycle management for energy-intensive manufacturing industries. The data-driven strategy is proposed and explained by the lifecycle of cleaner production data. Implementation models are developed to illustrate the proposed data-driven cleaner production strategy. Furthermore, two case studies from Southern and Northern China are presented to demonstrate the strategy. Results show that the unit energy consumption and the energy cost of production in Company A are reduced by at least 3% after applying the proposed strategy. In addition, the “cradle-to-gate” lifecycle data analysis indicates that the costs of environmental pollution in Company B are diminished significantly. Finally, energy-intensive manufacturing companies in China are compared and evaluated, and the effectiveness of the proposed strategy is discussed.

HardOps: utilising the software development toolchain for hardware design

ABSTRACT Open collaborative design of physical products between remote partners poses unique challenges. This is due to both the complex and interconnected data required for product design and manufacture, and to the centralised computing infrastructure traditionally used to manage product lifecycle data. While modern cloud-based solutions to collaborative design are gaining popularity, they diminish the control of each design partner. In contrast, software designers readily collaborate on highly complex software, while retaining direct control of the files they are editing due to the dominance of distributed version control. Distributed version control for software is often coupled with ‘Developer Operations’ or DevOps tools to automate critical processes and facilitate communication. This paper explores how DevOps workflows can be adapted for open development of hardware. An example of how DevOps for hardware (HardOps) can be implemented is provided by the OpenFlexure Microscope project. While much ground remains to be broken in this field, HardOps can support a new paradigm of distributed hardware development, with enormous benefits not only for open-source hardware, but also commercial hardware.

Sustainability Assessment of Products manufactured by the Laser Powder Bed Fusion (LPBF) Process

Assessing the sustainability of a product is dependent on considering individual product life cycle data. Based on the information on material, energy and information flows, evaluation tools such as the life cycle assessment (LCA) or the calculation of the cumulative energy consumption can be used. Especially the methodology of LCA according to ISO 14040/44 allows a selection of alternative analysis and measurement cycles as well as a combination of impact assessment indicators that have a direct and significant influence on sustainability. The comparison of various manufacturing processes allows the identification of relations between the applied evaluation tools and the product- as well as process-specific parameters during the production phase, and throughout the entire product life cycle. Within the various case studies considered, a distinction can be made both between the definition and the indicators of sustainability and between the ecological, economic or social dimensions within the motivation formulated. This paper describes the results of the literature review on the sustainability assessment of additive manufacturing processes in general as well as the Laser Powder Bed Fusion process as an example. A research map shows which phases of the product life cycle of an additive manufactured component are considered and which methods can be used to assess their sustainability. The result is a summary of the state of the art regarding the methods of sustainability assessment of additive manufacturing processes. In addition, an outlook can be determined on how the different phases of the product life cycle can be evaluated with tools that currently receive less attention.

Towards an Integrated Approach to Infrastructure Damage Assessment in the Aftermath of Natural Hazards

The world has witnessed an alarmingly increasing number of serious natural hazards. In the aftermath of a hazard, relevant authorities/agencies face, among others, the challenging tasks of rapidly evaluating and assessing the damages to infrastructures and restoring their essential functionality and operation. The availability of reliable, high-quality structural and operational/maintenance data of a structure and its health, before and after a natural hazard, can be instrumental in the rapid assessment of a damaged structure. We collectively refer, in this paper, to the existing as-built and facility operational information about a structure or an infrastructure asset represented respectively in Building Information Modeling (BIM) and Infrastructure Asset Management (IAM) systems as Product Lifecycle Data (PLD). Arguably, PLD combined with other post-hazard condition assessment data can provide a more reliable and integrated solution for a rapid damage assessment of buildings and other critical infrastructures. Unfortunately, the application of PLD in this critical area has been unexplored in the literature, and the mapping between PLD and damage assessment methods is loosely investigated. In an effort to address this research gap, this paper provides a critical analysis of the most common structural damage assessment methods and explores the potential of combining them with PLD to provide more reliable, comprehensive, and integrated solution for damage assessment. Findings from this study could be useful for practitioners in selecting the most appropriate and effective methods to conduct damage and safety assessments of critical infrastructures. The study will also assist the further theoretical developments in the integration of PLD with different damage assessment methods.

A Data Management Approach Based on Product Morphology in Product Lifecycle Management

In the product life cycle from conception to retirement, there are three forms: conceptual products, digital products and physical products. The carriers of conceptual products are requirements, functions and abstract structures, and data management focuses on the mapping of requirements, functions, and structures. The carrier of digital products is digital files such as drawings and models, and the focus of data management is the design evolution of product. Physical products are physical entities, and their attributes and states will change over time. Existing data model research often focuses on one or two forms, and it is even impossible to integrate three forms of data into one system. So, a new data management method based on product form is presented. According to the characteristics of the three product form data, a conceptual product data model, a digital product data model, and a physical product data model are established to manage the three forms of data, respectively, and use global object mapping to integrate them into a unified data model. The conceptual product data model has a single data model for a single business stage. The digital product data model uses the core data model as the single data source, and uses one stage rule filter to add constraints to the core data model for each business stage. The physical product data model uses the core data model to manage the public data of the physical phase, and the phase private data model focuses on the private data of each business phase. Finally, a case of Multi-Purpose Container Vessel is studied to verify the feasibility of the method. This paper proposes three product forms of product data management and a unified data management model covering the three product forms, which provides a new method for product life cycle data.

Markov chain optimization of repair and replacement decisions of medical equipment

• The study determines the optimal repair or replacement decision based on the product lifecycle data. • A Markov Decision Process (MDP) framework is proposed for optimizing repair decisions. • A dataset of medical device repair and maintenance is used to show the application of the model. • Product lifecycle data is essential for lowering repair and replacement costs. The cost of repair and maintenance of medical devices can be fairly burdensome to the healthcare industry. Healthcare providers often consider plans such as increasing in-house repairs, using multiple repair service providers, or timely replacement of devices to overcome this issue. This study aims to develop a data-driven Markov Decision Process (MDP) framework based on Discrete-Time Markov Chain (DTMC) model to optimize medical equipment repair and replacement decisions. Effective decision-making on whether to repair or replace is crucial to managing the product lifecycle cost and the costs to healthcare facilities. The study determines the optimal repair or replacement decision based on the product lifecycle data and current product failure status. It utilizes a net present value model to maximize expected value over an infinite time horizon. The study uses a dataset of 24,516 repair and maintenance records of 5,171 individual medical devices of a particular type to extract parameters needed for the optimization model. The dataset provides a rich baseline for analyzing different failures and event modes during product lifespan such as battery-related issues, random failure, preventive maintenance, and physical damage. It further quantifies the chance of moving from one product status to another. The model outcomes are discussed for a particular case. The findings reveal the most frequent reasons for failures and the most economically viable repair and replacement decision for each end-of-use device based on their current condition. Several sensitivity analyses are conducted to clarify the impact of operating revenue and warranty time on the repair or replacement.

How coordinated sectoral responses to environmental policy increase the availability of product life cycle data

PurposeEnvironmental Product Declarations (EPDs) and Product Carbon Footprints (PCFs) have a significant potential for contributing to consumption-based approaches to climate change. This paper provides an important building block towards a theoretical model of the factors accounting for variations in the availability of life cycle data across countries. It does so by positing a mechanism linking industry associations’ institutional role within environmental policy processes to the availability of product life data and by empirically validating it.MethodsInterviews, qualitative document analysis, web scraping, quantitative text analysis, set-theoretical causal reasoning, and process tracing.Results and discussionEnvironmental policies that stipulate industry-government deliberations and assign a coordinating or mediating role to industry peak associations can stimulate the exchange of environmental information among industrial sectors. The policy instruments of determination of ‘best available techniques’ (BAT) towards standard setting, negotiated collective agreements and carbon pricing all contribute towards the institutionalisation of organised information exchange within industry. This lowers transaction costs for the monitoring, reporting and verification of sectoral environmental data and can thus be conducive to the creation of sectoral life cycle assessment data, with positive knock-on effects on the availability of firm- and product-specific LCA labels.ConclusionsIndustry associations’ institutional role within environmental policy processes can partially explain cross-national variations in the availability of product life cycle inventories.

Environmental impact of mutualized mobility: Evidence from a life cycle perspective

Today's urban transportation systems face increasing challenges such as greenhouse gas (GHG) emissions, urban air quality, and traffic congestion. In this context, various initiatives of mutualized mobility have emerged. However, notably lacking is assessing the environmental impacts of mutualized transportation modes from a life cycle perspective. Using the actual urban transportation big data and related product life cycle data, this study combined with the life cycle assessment methodology and a “bottom-up” approach, explores the effect of mutualized mobility on greenhouse gas emissions of urban transportation systems for both Beijing and Toronto. The results showed that mutualized mobility might positively affect the sustainability of urban transport systems, albeit in very different ways. In Toronto, the annual per capita greenhouse gas emissions in 2016 decreased by 2.8 kg of carbon dioxide equivalent (CO2-eq) compared to 2011. Both carpooling and car-sharing displayed a higher curbing potential than ride-hailing. In a city characterized by higher population density like Beijing, ride-hailing created negative impacts. Deadheading (i.e., pulling an empty trailer) was the critical factor affecting ride-hailing's environmental performance, which accounted for about 30% of the total vehicle life cycle emissions. Counter-intuitively, greenhouse gas emissions of station-based bike-sharing (SBBS) were almost six times that of privately-owned bicycles and even higher than public transportation. This study's results can be used as a starting base for decision-makers to devise more appropriate strategies and programs to promote the sustainability of mutualized mobility and urban transportation systems. Meanwhile, it can also help the public at large to better understand the characteristics and environmental impacts of mutualized mobility to adopt more sustainable mutualized mobility alternatives.

Blockchain-based deployment of product-centric information systems

Collecting and utilizing product life-cycle data is both difficult and expensive for products that move between different industrial settings at various points of the product life-cycle. Product-centric approaches that present effective solutions in tightly integrated environments have been problematic to deploy across multiple industries and over longer timespans. Addressing deployment costs, incentives, and governance, this paper explores a blockchain-based approach for the deployment of product-centric information systems. Through explorative design science and systematic combining, the deployment of a permissionless blockchain system for collecting product life-cycle data is conceptualized, demonstrated, and evaluated by experts. The purpose of the blockchain-based solution is to manage product data interactions, to maintain an accurate single state of product information, and to provide an economic incentive structure for the provision and the deployment of the solution. The evaluation by knowledgeable researchers and practitioners identifies the aspects limiting blockchain-based deployment of solutions in the current industrial landscape. Combining theory and practice, the paper lays the foundation for a blockchain-based approach to product information management, placing design priority on inter-industrial and self-sustained deployment.

Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

APPROACH FOR SUSTAINABILITY CRITERIA AND PRODUCT LIFE-CYCLE DATA SIMULATION IN CONCEPT SELECTION

AbstractCompanies struggle with identifying relevant sustainability aspects strategically, assessing alternative solutions quantitatively, and making trade-offs. This paper reports results from a prescriptive study with an aerospace company, and presents the Sustainability Criteria And product life-cycle Data Simulation (SCADS) approach. Based on strategic integration of sustainability indicators, this approach aims to enable visualisation and comparison of the sustainability implications of different concepts in early design phases of product development.

An Edge-Based Distributed Decision-Making Method for Product Design Scheme Evaluation

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.

Method for data inventory and classification

One of the most notable drivers of the fourth industrial revolution is the availability of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data leads to promising potentials in strategic product planning. However, many companies are confronted with major challenges in identifying and specifying all the relevant heterogeneous and manifold data sources. This is due to the lack of simple approaches and methods for data inventory. In this paper we present a method for identifying and structuring data and their sources systematically by employing a framework to classify data sources and giving examples for application.

Blockchain-based data management for digital twin of product

The concept of digital twin was originally proposed to assist product lifecycle management through the high-fidelity virtual product. In product lifecycle, there are many participators constructing a complicated network with enormous product lifecycle data. It is difficult to manage data of digital twin efficiently and securely from the perspectives of data storage, data access, data sharing, and data authenticity among the complicated network. Moreover, the virtual product is always updated to the latest state of the physical product by overwriting the history virtual product that records the development process of digital twin. To address abovementioned data management problems simultaneously, a data management method for digital twin of product based on blockchain technology is proposed in this paper. A peer-to-peer network is constructed to enhance data sharing efficiency among participators. The basic structure of blockchain is introduced, including block, chain, and transaction. The blockchain is used for data storage through cryptography that ensuring only eligible participators can access the corresponding data. The change sensitive characteristic of blockchain can ensure data authenticity as well. In addition, the concept of smart contract can be used to execute some actions automatically to increase data sharing efficiency. A case study is presented to demonstrate the effectiveness of the proposed data management method. The results show that the proposed method can solve the abovementioned data management problems simultaneously.

Significance and Challenges of Data-driven Product Generation and Retrofit Planning

One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypotheses leads to promising potentials in strategic product planning. In this thesis paper, we postulate the need for data-driven product generation and retrofit planning as an interdisciplinary field of research. We define and analyze the key concepts and derive requirements in a structured way. Based on an exhaustive research of existing approaches, we structure open research questions and propose a roadmap in order to shape future research efforts.