Procedia manufacturing最新文献

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.

This paper presents a performed study to develop and improve the process of after sales of a Latvian company specialized in manufacturing fish processing equipment. The project was developed based on an action- research methodology. During the first stage of the study, the current after sales process was analysed to identify the issues and possible improvement opportunities that could be implemented later on. Data were collected through surveys and analysed, resulting in a series of improvement proposals discussed with the board of the company. At a later stage of the study, improvements were implemented such as the introduction of new services, development of new process diagrams and improvements of the spare parts management strategy. To conclude the study, the new services were offered and presented to customers and the trial period for the new after sales methodology was started.

Global supply chains shift to meet exceeding expectations in supply chains lead to the need for digital technologies in supply chain management. Industry 4.0 has emerged in sustainable supply chain systems, blockchain is prominent, with the potential of immutable transparent data. Blockchains present potential to disrupt supply chains through digital transformation by enabling provenance, visibility, relationships, collaboration, lower costs, and enabling real-time trusted data. Scholars are increasingly investigating blockchain adoption, with an emphasis on technology acceptance modelling. Current research is valuable to understand potentials of blockchain technology in supply chains however, there is limited work on the building blocks for blockchain adoption in digital transformation of supply chains. This study aims to investigate through a systematic literature review and case studies, the building blocks for blockchain adoption in digital transformation of sustainable supply chains. Blockchain adoption in supply chain management is receiving increasing attention, along with highlights of critical factors. This paper offers a building block model that is in three main phases; pre-adoption, adoption, and the post adoption. The model indicates that adoption context, blockchain technology platform offerings, strategic responses, and adoption readiness are some key building blocks, particularly at the pre-adoption phase. Trust and supply chain network, firm resources, and blockchain costs are all considered critical in the considerations of the building blocks. In addition, aligning supply chain objectives with blockchain systems is critical and so is blockchain compatibility. Law and governance are amongst the two prominent challenges in blockchain adoption and should be considered as part of the building blocks for blockchain adoption.

Modular production systems provide the opportunity to react more flexibly to customer-individual requirements resulting in smaller batch sizes. The implementation of capabilities in production systems leads to a skill-based production which manifests on the modular level. Electrical energy consumption can be analyzed more in detail if the consumption can be linked to the respective skill. The aim of this paper is to show the conception of an energy consumption interface in a modular skill-based production environment with the Asset Administration Shell. The current state of the art of modular skill-based production and modular energy consumption measurement with Infrastructure Nodes and Asset Administration Shell is introduced. The role of the modular level for an energy consumption interface and conceptional approaches of this interface to access and collect consumption data is presented on this basis. Furthermore, the prototypical implementation of the energy related Asset Administration Shell submodel with the BaSyx-Middleware is presented.