Procedia CIRP最新文献

A manufacturing process includes inspecting the product to verify it meets its quality standards. Such steps, however, are time-consuming and, depending on the means, prone to errors. If not identified in time, defects occurring at an early step of a manufacturing process may result in significant waste, especially if the product is not easy to re-work. Today, however, the combination of AI with computer vision technologies can enable manufacturers to transform quality inspection by automating the detection of defects. This study discusses the use of products’ 3D shape for inline surface defect detection, facilitating the adoption of proactive control strategies facilitating the reduction of waste. The product's 3D shape, represented by a point cloud is acquired by two fixed laser triangulation sensors orthogonally arranged. The K-means method is adopted for the point cloud data analysis, while Voxel Grid filters are used for downsampling to reduce computational time. The proposed approach has been evaluated in a use case related to the production of steel parts, with the findings supporting that an in-line implementation can facilitate the detection of surface or geometry defects, which, in turn, may facilitate the reduction of waste, by avoiding further processing of the defective product.

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management.

The changing properties of thermoplastic injection molding compounds during mechanical recycling complicate their use in structural lightweight engineering applications. Above that, an impact of the often unknown processing history on the recycled materials can be assumed, since it influences the material structure and degradation. In the present work it is investigated, how the shear load in plastification affects the properties of recycled glass fiber reinforced polypropylene during multiple processing cycles. Based on a design of experiments five processing cycles with different screw speeds are performed resulting in 47 different processing variants (screw speed, processing cycle). The samples undergo mechanical (tensile, impact), rheological (Melt Volume Rate) and morphological (fiber length distribution) tests. It was determined that the influence of the processing cycle is crucial for material degradation and properties. Also the screw speed setting directly affects the processing behaviour since it influences the melt temperature and viscosity. However, for the chosen process parameters, no clear influence of the screw speed on the resulting mechanical, rheological and morphological properties is found. The study suggests that knowledge of the screw speed setting is not essential for circular economy scenarios, as long it does not exceed the common processing recommendations.

The green energy revolution has propelled the use of batteries, necessitating holistic lifecycle monitoring and data sharing among stakeholders involved in the supply chain and in the whole lifecycle. The European Union mandates digital passports through the Battery Regulation (EU) 2023/1542. Recognizing the challenges in today's landscape – from fragmented software to limited collaboration and the array of battery types – this paper unveils a standardized battery passport data model aimed at overcoming the challenges posed. It organizes techno-environmental data into a tree structure, ensuring compliance with European regulations and offering a Life Cycle Inventory-ready structure for the purpose of supporting Life Cycle Assessment studies. The future implementation on a centralized platform has the potential to drive eco-friendly battery infrastructure growth.

The megatrend sustainability is changing the way industrial companies view their products and services and how they interact with the environment. In combination with digitalization, the way their systems (products and services) evolve is undergoing a significant shift, offering huge potential for optimization in favor of sustainability. Large volumes of data from the systems-in-use, software tools, and databases reshape how the system life cycle unfolds. This results into complex interrelationships between the life cycle phases and the created and used artifacts (i.e. models or production reports). The challenge for industrial companies is to get a structured overview of data sources, and data flows along the system life cycle. To address these challenges, a system life cycle data map has been developed utilizing design science. The data map illustrates the system life cycle phases, the related data sources and associated artifacts, and their interrelationships, i.e., the data flows between different artifacts. Applying the data map for a given system enhances data clarity and opens new opportunities for improving the system life cycle, its design, and potential for data-driven applications.

The production of photovoltaic (PV) systems is subject to continuous technological development and geographical shifts in manufacturing, leading to changes in economic aspects and environmental impacts. This work focuses on evaluating changes in environmental impacts over time using life cycle assessment (LCA). Several existing LCA studies and life-cycle inventory datasets for multi-crystalline silicon (multi-Si), single-crystalline silicon (single-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIS) were compared, and the reasons for deviations were analyzed.

The comparison was carried out at the plant level for panel slanted roof systems, and the impact assessment results were compared to their counterparts from the ecoinvent v3.3 life cycle inventory cut-off database for individual datasets.

The study found that technological advances in PV manufacturing have reduced environmental impacts. The differences between cell types converged over time, with CdTe having the lowest environmental impact during production. However, this may be relativized when considering the lower life time of CdTe cells in a cradle-to-grave assessment. Single-Si has a higher environmental impact during manufacturing, but it is just about compensated for compared to multi-Si. If a dataset for PV must be selected without cell-type knowledge, multi-Si plants are recommended as a proxy. The analysis provides insights into the environmental impacts of different PV systems over time and highlights the importance of regular updates of life-cycle inventories and consistent modeling.

This study delves into the static and dynamic load testing of 3D-printed gears, addressing the potential of utilizing FDM 3D printing for repairing household appliances. The failure of electronic household appliances attributed to plastic gear malfunction has prompted an alarming number of products to be prematurely discarded due to a lack of available spare parts. The research emphasizes the transformative potential of 3D printing to streamline gear repairs, enabling rapid local production of gear replacements, and reducing costs and downtimes associated with conventional spare parts. PLA and ABS, the two most widely used 3D printing materials, underwent exhaustive static tests to determine maximum load-bearing capacities and accelerated fatigue tests to assess long-term performance. Results underscore the potential of the 3D printing process itself, highlighted by flexibility and shortened production timelines. However, limitations arise from the inferior mechanical properties of PLA and ABS as compared to traditional plastic gear materials such as nylon. PLA demonstrates promise under controlled temperatures but only with lubrication or metal contact, while ABS reveals inadequate mechanical and fatigue characteristics at higher torques.

Cryogenic machining gained interest as a clean, and effective cooling technique for machining hard-to-cut materials. Hardened H13 steel is widely used in tool and die applications due to its superior properties at high temperatures. However, its low machinability and post machining tensile residual stress remains a major challenge. A comparative techno-economic and environmental analysis was performed to evaluate the performance of cryogenic and flood machining of H13. Process simulation was performed to select the optimum cutting conditions. Cryogenic showed significant improvement in the overall process productivity, part integrity, tool life and environmental performance as compared to flood.

Road vehicle fuel combustion emits large quantities of air pollutants such as respirable suspended particles. They are deemed to have severe impacts on human health. Therefore, concentration measurements to identify vehicles with malfunctioning after-treatment systems are crucial. This study assesses the environmental impacts of the production and use phase of a device detecting and quantifying those particle emissions caused by automobiles, also considering associated services such as regular calibration. The focus is on the comparison of environmental impacts of this product-service system and its possible emission avoidance by ensuring stipulated limits. For this purpose, the environmental performance is assessed by means of an adjusted break-even point method. Our work shows that the identification and replacement of 21 improperly working exhaust filters already offset the environmental burden of one device in at least one impact category.

Despite stricter emission controls and the planned reduction of newly sold Internal Combustion Engine (ICE) cars, adequate repairing and recycling of old ICE cars remains vital, reflected also by the recent EURO7 standard that explicitly refers to repairability. Digital Product Passports (DPPs) have gained attention as enablers of transparent and traceable value chains and improved product lifecycle management. While the focus has been put on creating DPPs for electric vehicle batteries, research on the potentials and requirements for DPPs for the ICE and its well-known components, like the exhaust gas turbocharger, does not exist. Thus, this study systematically investigates what information such DPP needs to contain to support value chain actors’ sustainability/circularity-related decision-making. A mixed-method approach is applied. The Supply Chain Oriented Process to Identify Stakeholders (SCOPIS) method, as well as two expert workshops (N = 8 and N = 6) and an expert survey (N = 8) served for identifying the stakeholders and their most relevant information requirements. The results consist of a refined and prioritized list of information requirements, a conceptual model of a DPP for the turbocharger, and the presentation of two use cases: refurbishment and recycling. This contribution serves as the foundation for handling ICEs sustainably and circularly.