Advances in Industrial and Manufacturing Engineering最新文献

Climate change and normative requirements are forcing car manufacturers to make their products ever lighter despite the highest safety requirements. For this case study a high-strength EN AW-7075 alloy is used as an example of how hot formed aluminium components for vehicle bodies can be produced on existing steel hot forming lines. Reusing production plants not only shortens the start-up time for mass production of hot formed and high-strength aluminium components, but also makes a significant contribution to the sustainable use of existing plant technologies and thus to improving the carbon footprint of manufacturing companies.

In addition to the presentation of the process control and the necessary conversion measures for the modification of a hot forming process from steel to aluminium, the most significant technological properties and the production costs of hot formed components made from both materials are compared. To be able to show the economic viability of a reallocation of existing and depreciated manufacturing plants, investigations of possible manufacturing parameters are carried out and presented.

The case study shows that by converting the furnace technology to Jet-Heating, significantly higher heating rates can be realised when compared with using radiation furnaces. As the furnace holding time was found to have no influence on the final strength in T4 and T6 state of EN AW-7075 specimens, the overall furnace cycle time is significantly lower than for press-hardened steels. Although the modifications necessary for the forming of aluminium alloys result in changed requirements for existing production plants, the additional costs per component for the plant conversion are marginal in relation to the assumed delivery volumes. It is demonstrated that for components with low material input, the use of 7000 series aluminium alloy components can become economically viable on a larger scale for OEMs.

Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.

The tensile test always delivers an in-depth understanding of true stress-strain relationship. However, it is not easy for the researchers to understand and evaluate the tensile properties of micro-specimens. This paper presents a research work aiming at the design and manufacturing of a small universal test machine (UTM) for measuring the mechanical properties of the miniaturised samples. The newly developed machine is sensitive to small loads and permits to obtain the stress-strain curves for thin materials. This portable UTM consists of a stepper motor, a load cell, a linear variable differential transformer (LVDT), a load cell amplifier and a data acquisition system. Copper based small and thin (50 μm) tensile test samples were tested on this machine at room temperature, and the calculated results were compared with the test results derived from a commercial UTM (METEX - 1 kN) to justify the validation of the developed apparatus. The obtained mechanical properties are in good agreement with the values obtained from a commercial UTM. To confirm the possibility of in-situ micro-observation, the surface roughness analysis has been conducted on the developed apparatus for pure copper foils under 3D laser-confocal microscope. Finally, it is concluded that this kind of testing apparatus could be manufactured within a manageable budget.

Additive manufacturing of ceramics stands to transform applications requiring wear resistance in severe environments (including high temperatures and pressures, harsh chemicals, and biomedical implants, among many other uses). However, applications in electromagnetics are gaining increased attention as newly-available materials like zirconia provide very low electromagnetic loss and also provide the highest permittivity possible in 3D printing with near full density. By 3D printing zirconia lattices, the density can be modulated spatially by varying strut and beam thicknesses at arbitrary positions (such as when following a spatial function). As the effective permittivity is related to the density, the speed of electromagnetic radiation (the speed of light, c) can be controlled in the 3D space. As a preliminary investigation to understand processing limits and mechanical performance, this effort has focused on evaluating the compression and flexural strength of both 3D printed solid and lattice structures with millimeter-scale unit cells post-processed with different conditions. Non-destructive computer tomography was included to identify and validate remediation of internal delamination with hot isostatic pressing. Although zirconia lattices fabricated with NanoParticle Jetting™ were relatively delicate, millimeter periodic features were possible and provided sufficient strength to maintain structural integrity for non-critical loading.

When sheets of aluminum alloys are pierced or trimmed, tool failure occurs by the transfer of material from the sheet to the surface of the tool. This phenomenon referred to as galling adversely affects sheared edge quality and increases energy consumption. An instrumented pneumatic press was designed and built to conduct shear–punch tests on 2 mm-thick AA5754-O sheets and to investigate the progression of galling to AISI M2 steel punching tools during dry and lubricated punching. The punching tests were performed using a clearance of 2.0% of the lower die diameter. Cumulative galling volumes were measured using a non-contact optical surface profilometer, and the rate of material transfer (the galling rate) was estimated for both dry and lubricated punching. The galling initially occurred at a high rate, and for dry punching, it was reduced to $74.6\times {10}^4\mu m^3/stroke$ between 20th and 125th strokes. Lubricating the aluminum sheet with an oil-based lubricant mitigated the material transfer, and the galling rate after the 20th stroke was reduced to $3.1\times {10}^4\mu m^3/stroke$. Punching force-displacement curves indicated a higher amount of energy expended to shear AA5754-O sheets in the dry punching compared to the lubricated punching that is suggested to be due to the higher galling resulting in higher friction forces at the interface.

In this work, aluminum matrix composite (AMC) was successfully fabricated by multi-pass friction stir processing (MPFSP) with nanoparticles SiC. A constant rotational tool speed of 1350, feed rate of 65 mm/min, tilt angle of 2° was used to enhance the microstructure and mechanical properties of multi-pass FSP/SiC of AA6082-T6. It can be observed that Nanoparticles SiC were fragmented totally and uniformly distributed in fifth pass FSP. Agglomeration of SiC decreases with increases in the number of passes. The ultimate tensile strength (UTS) of base metal AA6082 exhibited 215.54 MPa, and % strain of 24.91. After implementing multi-pass FSP with nanoparticles of SiC on the AA6082, the UTS was enhanced simultaneously as the FSP pass increases. The UTS of 1st pass, 2nd pass, 3rd pass, 4th pass, and 5th pass was observed as 223.61 MPa, 238.37 MPa, 255.63 MPa, 281.79 MPa and 296.86 MPa, respectively caused by strain-free fine grains during dynamic recrystallization (DRX) mechanism. In contrast, Vickers's hardness value along the centerline (stir zone) was observed as 89, 101, 119, 125, 133 HV with 1st pass, 2nd pass, 3rd pass, 4th pass, and 5th pass respectively.

The implementation of human-robot collaborative systems in industrial environments have widely extended during the last five years, from manufacturing applications reproduced in laboratory facilities or digital simulations to real automotive shop floors. Commonly, one way to guide their design has been through the adoption of international standards focused solely on the safe operation of collaborative robots. The main objective of this paper is the identification of basic components comprising human-robot collaborative systems design. This is supported by two steps, 1) Provide an extensive compendium of current applications and components within a varied set of manufacturing sectors and tasks. 2) Based on the latter, propose a selection of “structural components” for collaborative work. We conceptualized structural components as the organizational and technological alternatives necessary to fulfil the basic requirements and functionalities of human-robot collaborative systems. This document presents a systematic literature review that includes 50 exemplary case studies implemented in different manufacturing environments throughout the last five years praxis (2016–2020). Four structural components were identified in this paper: interaction levels, work roles, communication interfaces and safety control modes. Furthermore, it was found that physical contact-based collaboration for screwing assembly of small-sized parts and material handling of heavyweight objects are suitable applications for the automotive industry. Moreover, certified augmented and virtual reality devices were highlighted as convenient assistive technologies for safety and training manufacturing needs. The presented categorization will allow practitioners on selecting settings of compatible structural components that could respond better to trendy manufacturing requirements searching for highly personalized products.

The fourth industrial revolution comprises the digital transformation of manufacturing by means of an intensive integration of advanced information and communication technologies. The possibility of creating interactive virtual replicas of the physical entities to predict and detect physical issues and optimize processes is one of the key benefits of the manufacturing digitization. In the oil and gas industry, one of the essential activities that can take advantage of innovative digital technologies is the geometrical quality assurance of pipe spools. In this sense, this work focuses on the development of digital twins of pipe spools that embrace attributes of their physical counterparts to manage dimensional variation and to assist the geometrical quality assurance process. Based on data available in the design stage of the product realization loop, i.e., dimensional tolerance, it was possible to estimate the process capability and to predict, by sensitivity analysis, the behavior of the spool elements when assembling them to each other.