Advances in Industrial and Manufacturing Engineering最新文献

Flow-drill screwing is one of the key joining technologies for car body structures in multi-material lightweight design. In the course of technological developments and subsequent volume production of a product, different assets are used to obtain the same joints, assuming that similar processes yield the same joint characteristics. Since a simple transfer of the process parameters for joining the same materials is usually not possible, a remarkable experimental effort is required to meet manufacturing requirements. In this study the transition to an enhanced flow-drill screwing system and its effects on the joint is investigated. For this purpose, two flow-drill screwing systems typically used in the automotive industry are considered. An application-oriented approach for determining the joining parameters is shown. First, the optimal joining parameters for the target system were determined based on the process curves and parameters of the initial system by fulfilling the requirements for the joint. The joints were evaluated by using cross sections and single-lap shear tests. On this basis, the results of both flow-drill screwing systems were compared. Due to the further development of the flow-drill screwing system the process times can be significantly shortened while achieving the same mechanical properties and better process control at the same time.

Quality control in semiconductors is a crucial step to produce high quality microchips. During the last years, advances in artificial vision have significantly improved image quality control techniques. In the semiconductor industry, automated visual inspection is fundamental to avoid human intervention and keep the pipeline sanitized. Different types of images are collected during this process, feeding image databases that continually grow and cannot be labelled by humans in an exhaustive manner. Advances in image retrieval search methods are fundamental to develop more efficient techniques that meet user requirements.

In this work we propose a dimensionality reduction approach on the feature vectors computed by a classifying deep learning model, while keeping a high retrieval performance. To validate this technique, we evaluate four well-known reduction algorithms on a subset of the full database: Principal Component Analysis (PCA), Sparse Random Projection (SRP), Isomap, Locally Linear Embedding (LLE), in combination with three similarity metrics: Euclidian ($L_2$), cosine and inner product. As the number of components of the vectors is reduced, the performance of the image retrieval is measured by recall, time to search, and memory footprint of the database.

PCA offers the best results, allowing a significant reduction in search time and memory usage, while SRP becomes an option only when the cosine distance is used. With PCA, we were able to divide the memory footprint by a factor of 16, the search time by 6, while maintaining an average recall of 0.96.

In this paper, to have control over geometry specifications of rectangular bar-shaped layers in a robotic concrete 3D printing process, a real-time vision-based control framework is developed and proposed. The proposed control system is able to set the layer-width by automatically adjusting the velocity of an industrial manipulator during the 3D printing process of concrete based materials relying on a vision system feedback. Initially, details related to the control system, vision and processing units, and robotic platform are discussed. In continue, technical descriptions related to the printhead design, conversion process from a digital 3D drawing model to numerical motion control commands of an industrial manipulator and building material used in this work are reported. The reliability and responsiveness of the developed system is then evaluated through experimental tests by printing several single bar-shaped layers with different wideness by means of an unique printhead geometry and also by printing two layers with the same dimension centrally above another. Overall, the high accuracy and responsiveness of the developed system demonstrate a great potential for real-time vision-based control of industrial manipulators for layer-width setting in concrete 3D printing applications.

Nowadays, most supply chains prefer to exploit closed-loop strategies to reuse their previous products in a reproduction process to reduce their waste and costs. Fluctuations of the demand in a closed-loop supply chain can cause a destructive effect known as the bullwhip effect. This study aimed to investigate the factors that reduce the bullwhip effect in the closed-loop supply chain. For this purpose, first, using a survey on the relevant literature, ten related factors were identified. Then, the opinions of 21 experts were collected, and by applying the ISM technique, the interactions between the ten identified factors were categorized and developed. Finally, the structural equation modeling technique developed the conceptual fit model. The findings indicated that ten identified factors are structured in six general levels. The results also represented a positive and significant relationship among 16 identified relationships in the conceptual model. Based on our findings, “information sharing” is identified as the primary factor of the model. A shorter lead time return factor was also identified at the highest level of the model. The results lead to a comprehensive program to reduce the bullwhip effect in the closed-loop supply chain.

This work describes a joint initiative between PSG-College of technology (Coimbatore, India) and Newcastle University (UK) for the mapping, design, evaluation and roll out of technically rich ‘Digital Manufacturing’ curriculum which has been embraced with a two-fold objective: 1) to prepare final-year Engineering students for real-life industrial environments and 2) to promote the use of digital technologies across manufacturing-intensive Micro, Small and Medium Enterprises (MSMEs) that can directly benefit from their application at an engineering, managerial and shop-floor practical levels. The project started by considering both countries' national strategies for Industry 4.0 (MAKE-in India and Made Smarter, UK) to identify those areas marked as strategically critical and mapping them onto existing engineering curriculums across undergraduate engineering degrees.

Based on local industry partners with clearly defined ‘digitalization’ opportunities, four industrial case studies were selected and reproduced inside both University labs in the form of student projects that were made available to all final year mechanical engineering students at both institutions. The resulting pilot projects exhibited the potential to expose undergraduate students to engineering concepts and techniques not currently covered in the taught curriculums, while offering industry a ‘soft landing’ on the use of Industry 4.0 tools which have not yet been embraced, mainly due to the lack of qualified personnel or access to specific technology skillsets, such as 3D printing, Augmented Reality and Digital Twins.

The use of electron beam welding for dissimilar welding (DW) of commercially pure (CP) vanadium to Ti–6Al–4V has been investigated via ARCAM S12, an additive manufacturing powder-bed system. Investigations of bead-on-plate welds for Ti–6Al–4V were first conducted to identify the process parameters for full penetration welds with a minimum energy input of 37 mA at a traverse speed of 7 mm/s. Vanadium bead on plate welds produced a penetration of approximately 75%, which was enough to proceed onto DW experiments. Defect-free full penetration welds were produced. The DW weld zone microstructure revealed an elongated dendritic structure comprised of bulky βTi grains and a fine substructure of α' laths. Thermal imaging (TI) showed an increment in radiance temperature ahead of the melt pool, indicating that there is a minimum energy required before keyhole welding is present, confirming mathematical calculations. Mechanical characterisation finds a fair range of hardness across both base metals (BM), heat affected zones (HAZ) and fusion zones (FZ). With no yield plateau in tensile test curves, the material is confirmed to fail on the side with lower mechanical properties, i.e., vanadium, which draws a fair process window for dissimilar welding between Ti6Al4V and vanadium alloys.

Materials design advancements are now paramount to further the course of additive manufacturing (AM) of carbon-fiber-reinforced plastic (CFRP) composites. This is due to the increased prospect of such composites in a wide range of applications, ranging from space to automotive subjected to stringent mechanical performance requirements. A synergy of the high strength-to-weight ratio of the CFRP composites coupled with design freedoms inherent in AM techniques offers several interesting opportunities to customize and increase access to mechanical parts. However, several challenges are currently preventing the AM fabrication of the composites from realizing satisfactory mechanical properties compared to some of the traditional methods such as autoclave molding, extrusion molding, compression molding, etc. The challenges can be improved with a better understanding and appropriation of materials design factors that define the controllable material features which could be suitably varied to obtain desired mechanical performances. This paper reviews the literature on the material factors that influence the mechanical performance of parts composed of short-fiber CFRP composites fabricated through the AM technique. Thermoplastic matrix compositions, chain arrangements, and structural morphology effects are discussed in relation to the ease of processing and the final mechanical performance of fabricated composites. Operating environmental effects on mechanical performance were reviewed and also works of literature on the current state of development in the simulation modeling of material factors in the AM fabrication of CFRP composites were discussed.

The article presents development of a new heat transfer model for calculating temperature distribution in porous and non-porous materials during laser cutting. The novelty of this model lies in incorporating melting and vaporization progression of porous media during laser interaction. The modelling has been implemented through a transient finite difference scheme and the results have been validated against experimental data of cutting various materials by laser including rock and metals.

One important objective in steel pipe manufacturing is to avoid rejects. In order to adequately heat each individual pipe in the furnace, the surface temperature of all pipes after rolling must be predicted accurately. A fast model is needed that can provide this prediction quickly and repeatedly. To achieve this goal, artificial neural networks (ANN) were applied to the hot-rolling process used to create seamless steel pipes for the first time, and results are presented in this paper. Modelling the process is a complicated task, because a wide range of different geometries are manufactured, and the pipes can possibly be cooled after rolling. To address this issue, two ANN models were designed, with one model consisting of two coupled ANNs to increase its accuracy. This also represents a novel modelling approach. Both models were trained with data recorded during the production process. In general, the modelling results agree well with data collected by the in-plant measurement system for a wide range of different finished pipe geometries. The two models are compared, and differences in their behavior are discussed.

The function of radial sealing systems depends significantly on the shaft counterface. External cylindrical plunge grinding is considered the standard for the manufacturing of suitable shaft counterfaces. It creates a stochastic surface texture with many anisotropic groove-like grinding structures, oriented in the circumferential direction of the shaft. The structures are created by the grain engagement into the workpiece during the grinding process. This surface characteristic exhibits optimal properties for hydrodynamic lubrication between the seal and the shaft. Although there is no axial relative movement between grinding wheel and workpiece in plunge grinding, under unfavorable conditions grinding structures can be produced that deviate from the circumferential direction. These structures then transport fluid through the sealing during rotation. Structures, that cause fluid transportation because of inclined orientation to the circumferential direction, are referred to as micro lead. Especially for high rotational speeds, e.g. in electric powertrains, micro lead causes high pumping effects and therefore leakage and following failure of products. This publication presents findings on the influence of the dressing parameters on the formation of micro lead during external cylindrical plunge grinding. The experimental investigations show that especially negative dressing speed ratios lead to the formation of micro lead structures.