Procedia manufacturing最新文献

To facilitate the transition from a linear to a circular economy there is a need to develop digital tools that can provide comprehensive and useful data to enhance repair, remanufacturing, reuse, and recycling. This paper explains the typology of information key for such end, it discusses the current limitations and difficulties to implement methods in a digital platform, and a description of already existing digital solutions that will be further improved in the context of the DigiPrime project. A preliminary analysis of the availability and accessibility of information about electronics is required before the definition of a digital tool to support circular economy. First, the paper discusses the need of ‘digitalized’ end ‘standardized’ information of electronics to promote circular strategies. Then, diverse digitalization approaches by several teams of the Digiprime project are detailed. The paper concludes with a description of the next steps needed to align the diverse digitalization approaches and its potential contribution to improve the electronics sector.

In recent years there has been a growing interest in Circular Economy (CE), which promises to reduce waste and improve sustainability. The promise of CE is to change the conventional “take–make–dispose” that causes massive waste flows based on the integration of demanufacturing and remanufacturing processes within value chains. This integration requires breaking the ”silos” of the circular chain to establish new collaborative and sustainable value networks. The paper introduces a novel digital platform for the CE, which is currently under development in the H2020 DigiPrime project. The platform is destined to facilitate seamless and trusted information exchange across circular actors, while offering a range of value-added services that enable manufacturers, remanufactures, recyclers and other actors to gain insights in the status of recycling and waste management processes. The latter facilitates the implementation of zero waste processes, along with the assessment of the performance of the circular chain. The paper introduces the architecture of the digital platform, along with its data modelling, exchange and data traceability mechanisms. It also presents a CE use case used to validate the platform.

Managing the quality of global supply chains is critical for the success of global companies. Quality disseminates through the supply chains. Supply chain quality integration facilitates each company to use its main expertise which can lead to higher quality and cost reductions. Digital audits enhance the possibilities to ensure the production quality at the suppliers. A global supply chain quality integration is illustrated through the case of the Boeing 787 Dreamliner development. The development process was a major challenge for Boeing because not only several innovations were introduced in the 787 plane, but the supply chain quality integration reached new levels for the company. The 787-program had process, management, labor, and demand risks. The first 787 plane was delivered three years later than planned and the budget was exceeded by 10 billion USD. To solve the issues, Boeing created the Production Integration Center. It sent engineers and production workers to its suppliers in several countries of the world to smooth the supply chain quality integration. Boeing supported its suppliers with its knowledge. Onsite cameras were used as digital audits. The Production Integration Center intervened when it estimated delays in the shipments. Boeing successfully solved the issues, but they cost the company a lot. Examining this supply chain restructuring case can help supply chain managers to diagnose and overcome issues in supply chain quality integrations.

Additive Manufacturing (AM) is a new generation manufacturing method and AM is using digital CAD data directed to the machine to manufacture. AM is therefore regarded as a direct digital manufacturing method. This research work presents the methodology for designing critical aerospace parts used under fatigue conditions for AM. Selected fatigue critical aerospace part was topologically optimized then re-designed for manufacturability. With this optimization study, 45 % mass saving was obtained while mechanical requirements were satisfied. Manufacturing simulations for thermal distortions are covered and the optimized part was manufactured with laser powder bed fusion (L-PBF) and secondary operations were applied.

Finding duplicates in phased-array ultrasound weld scans is typically done by certified analysts. Due to the vast amount of data involved, this task is time-consuming and error-prone. Some automated analysis software exists but are still inefficient. In this paper, two types of duplicates are defined. A method to detect each type in a database of three-dimensional ultrasound weld scans is presented. The duplicate detection process is implemented in a cloud-computing service enabling near real-time processing in industrial applications. Experiments are performed to expose the detection accuracy of both methods.

Principal manufacturers suffer from uncertainty as they lack up-to-date information on their orders in supply chains (SCs). The current era of digitalization offers many solutions to monitor order statuses through integrated information systems. However, the requirements to implement such systems are high for the companies and they still prefer personal interactions with suppliers. This paper proposes a QR code-based order monitoring system in a subcontractor manufacturer network. The objective of the study is to assess the technological and operative maturity of the system in real SCs. A prototype system was built and demonstrated to representatives from Finnish companies in a virtual workshop. The feasibility study showed that the QR code-based monitoring system is practical and promising. The companies, however, recognize that its implementation would be challenging from both the technical integration and subcontractor adaptation points of view. The results obtained give insights into modern information management in SCs and suggest areas of interest for further studies.

Thermoplastic composites (TCs) are a famous choice when it comes to high performance designs for industrial applications. Since the growing demand on the use of this material, it is important to be able to evaluate suitable processing technologies. One of those technologies is continuous ultrasonic welding (CUSW) which creates continuous joints, also called seams, between two or more TCs parts. In CUSW mechanical oscillations are applied to the material and result in melting and connecting of the welding parts.

The approach to predict joint strength (qualities) of continuous ultrasonic welded TCs by training different neural networks is investigated in this study. Quality class prediction around 72 % accuracy is achieved with a fully connected neural network. Concluding, quality prediction of welded TCs with the help of artificial intelligence seems to be a suitable approach for quality observation but more research could lead to more reliable neural networks for industrial applications.

This paper addresses the joint control problem in the context of a two-stage stochastic manufacturing/remanufacturing system, which involve both manufacturing and remanufacturing processes. Its operability is affected by frequent deterioration failures. Along with the condition of the system, the manufactured items are affected as well. Thus, the system obtains lesser revenues due to the low-quality products and the downtimes of the deteriorated system. For this purpose, the state and the condition of the systems and the manufactured products must be monitored dynamically so as to devise an optimal strategy for manufacturing, maintenance, and quality control. The present paper proposes a novel two-agent reinforcement learning framework that incorporates parametric production and maintenance activities. The aim is to improve the productivity of the system and keep the system operational with minimal maintenance activities so as to maximize the overall profitability. The performance of the presented approach is evaluated through experimental scenarios.

The aim of the paper is to review the trends on low-cost open-source Internet of Thing (IoT) in the Framework of Industry 4.0. We hypothesize that open source IoTs benefits small and medium sized (SME) manufacturing companies to help them tackle economical and technical barriers for technology adoption at factory floors. This research reviews the state of the art of open-source hardware and software and identifies the challenges of open-source IoT by compiling scientific literature and relevant online resources. The methodology is twofold: (i) we use a grey literature review including online information, and (ii) we complement the analysis by a structured keyword search using IEEE scientific repository. The study finds that open source is and will be an essential part of developing Industry 4.0 solutions and its integral constituent, Industrial Internet of Things systems (IIoT). However, there are challenges to overcome, such as interoperability and reliability.

With unbounding the manufacturing resources and capabilities from shopfloors and being available as on-demand services for everyone who needs, the service allocation problem becomes critical. Despite of private Cloud Manufacturing platforms in which the service allocation problem is an optimization matching problem, in public Cloud Manufacturing platforms the service allocation problem is a matching game between manufacturing providers and consumers. This is a complex form of games that has two sides, one side is the players that has manufacturing resources and capabilities and the other side is the players who want to receives such services. In this paper, we present a matching game model for public Cloud Manufacturing platforms, and discusses the stable solution of the game using Deferred Acceptance algorithm. Then, we examine the model using a case study from automotive spare parts manufacturing in Iran which was the main motivation of this study. The novelty of the contribution of this paper is in development of a stable and optimal matching mechanism for public platforms.