Procedia manufacturing最新文献

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.

In the past two decades, the field of additive manufacturing (AM) has seen tremendous growth, especially in the production of functional parts. Unfortunately, improving the dimensional accuracy of these printed parts to the point where they can be used for a broad range of applications has proven challenging. Several methodologies to improve the dimensional accuracy of 3D printed parts have been proposed in the literature. One approach that has seen a considerable amount of work in recent years is product design adjustment based on predictive modeling. Under this approach, predictions of geometric deviations across the surface of a part are used to modify the shape of a part before printing so as to counteract or compensate for the predicted deviations. However, a majority of compensation methods aim at minimizing expected geometric and dimensional error, with a lack of consideration of cost and uncertainty. This study presents a new strategy based on multi-attribute utility theory to account for cost and inherent uncertainty associated with a compensation decision. By establishing manufacturer preferences and prior beliefs about the efficacy of a predictive model, the proposed decision-making strategy for compensation significantly increases the value of a given print to a manufacturer under simulated preferences.

Additive manufacturing (AM) machines have developed more rapidly than standardized frameworks needed for the qualification of their geometric capabilities. While some manufacturer-specific methods exist to test capabilities and perform some calibration tasks, standardization efforts have only recently been undertaken in the form of ISO/ASTM 52902. In this study, the recommended methodology prescribed by the standard was implemented by building geometric artifacts with a laser powder bed fusion (LPBF) system and performing dimensional inspection with a coordinate measurement machine (CMM), amongst other methods. Typical dimensional capabilities of the LPBF system are identified and commentary is made on applying metrology methods, detecting geometric error, and diagnosing base causes in the LPBF system. In doing so, favored metrology practices and measurement analysis methods auxiliary to the standard are proposed. Artifact measurements were used to characterize beam positioning error and beam offset error. Methods for decoupling the effects of error sources are proposed. Difficulties in the inspection of AM components are identified, and the effects of various CMM measurement strategies are evaluated. Insights on the application of the new standard are presented, along with commentary as to its fitness for the LPBF process.

Surface integrity refers to the condition of the workpiece surface modified by a manufacturing process. Surface integrity plays an equally important role in the functional performance and quality of the components as the dimensional accuracy, especially for applications such as load-bearing components and energy absorbers. With the rise in additive manufacturing technologies, the components where surface characteristics play an important role are being manufactured by a combination of additive manufacturing process followed by a finishing process. A secondary finishing process such as machining is essential for components produced by additive manufacturing or powder metallurgy as these processes exhibit poor surface finish, dimensional inaccuracies, and other internal/surface defects such as porosity. High strain rates and cutting temperatures involved in the machining process affect the surface characteristics of the machined component. It is envisaged that the cutting mechanism of porous parts produced by additive manufacturing could be significantly different from that of the continuous material. Therefore, the machining parameters, depending on their interaction with the inherent part porosity, could give rise to different surface characteristics and could be critical in determining the functional performance of the part. This work investigates the effect of micro-machining on the surface integrity and cutting force of additive manufactured Ti6Al4V alloy. Experiments are carried out at different levels of porosity and depth of cut to explore their effects on the cutting force, roughness, micro-hardness, residual stress of the resulting surface. It is seen that the mean cutting force was minimum during machining of porous materials and was maximum during machining of continuous materials. As the depth of cut increased, cutting force increased for continuous and porous materials. Whereas cutting force decreased as the porosity increased and was minimum during machining of most porous material (46 % porous). The surface finish was better for wrought alloy compared to additively manufactured Ti6Al4V alloy. As the porosity increased, the surface finish reduced initially and improved later.

In this paper, we explore the applications and practices that Stereolithography enables for an enhanced manufacturing process of a nitrate assay chamber for an underwater Point of Care device. Features such as engraved identifiers, creating post for inlet/outlet connections, embedding electromechanical components and optimizing the spatial distribution, were tested as a case of study for an underwater vehicle component. This study is a first approach in proposing a framework for manufacturing practices toward a new generation of customized devices for the application. The case study is validated using Computational Fluid Dynamics for the design and manufacture of an array of micromixing enhanced nitrite assay sensors that employ and exploits the enabling technology of stereolithography.

In Additive Manufacturing (AM), auditing layer-by-layer images can detect infill defective attacks effectively. However, the auditing process itself can become a target of inside or outside attackers in Cyber-Physical Manufacturing System (CPMS) environments because pervasive connection through various types of computer networks in CPMS opens new doors for adversaries to compromise various components in an attack detection system. To maintain an effective attack detection system and prevent data from malicious data injection, this paper presents a Layer Image Auditing System (LIAS) secured by the Blockchain technology in CPMS. LIAS consists of a pre-processing system and a Multilayer Perceptron Neural Network (MLP). To evaluate the prediction accuracy of LIAS, a set of simulated infill images and physical images were used for training and testing. The effectiveness of the Blockchain implementation is demonstrated by presenting the comparative performance analysis of the proposed attack detection system with and without the Blockchain.

This paper proposes a dual function fatigue tester that can perform both the traditional four-point rotate bending and the new cantilever rotate bending fatigue test. The linear profile of the cantilever specimen, that is simplified from the theoretical cubic profile, is subjected to uniform stress along the entire gage length and alternating between tension and compression modes until fracture. The system is designed to operate at 50-60 Hz, which is much faster than the traditional 5-10Hz in traditional push-pull fatigue testing. Preliminary fatigue testing on wrought Inconel 718 cantilever specimen shows random fracture along the specimen gage length, and successfully separation of fatigue fractured surfaces for subsequent fractography analysis. The system will be utilized for studying fatigue of selective laser melted Inconel 718 and the effect of different post processing parameters.

The goal of this work was to determine which standard particle size metric derived from optical analysis most closely approximates the sieved weight percent of irregularly shaped powder intended to be used for directed energy deposition. In this investigation, equivalent circle diameter, maximum diameter, minimum diameter, and perimeter were used as metrics to “virtually sieve” the particles in samples of irregularly shaped powder into the following particle size bins: <45 µm, 45 µm – 150 µm, and >150 µm. The percentage in the 45 µm – 150 µm bin were then compared to the weight percent of the powder mechanically sieved into this size range. The absolute difference between the virtually sieved percentage and the mechanically sieved percentage was assessed for 81 samples of mechanically-generated stainless steel 316L powder, all produced under different processing conditions in an oscillation ball mill. This difference was found to be on average, the least with the minimum diameter assessed as an area percentage, followed by the equivalent diameter assessed as an area percentage, and then the maximum diameter assessed as a percentage of the total number of particles ranked third. These findings and the methodology used to obtain them may be used by powder production process engineers and quality assurance personnel to assist in process control as more diverse additive manufacturing feedstocks become utilized.

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.