Advances in Industrial and Manufacturing Engineering最新文献

Manganese sulphide inclusions are commonly found in steels and known to facilitate the formation of deformation-induced damage sites in the form of voids during cold forming. These damage sites either exist as cracks, splitting the inclusion in two parts, or as delamination, separating the inclusion from the surrounding steel matrix. Both negatively influence the longevity of components, especially under cyclic loading. The analysis of damage is inherently scale-bridging, ranging from deteriorated global mechanical properties of the finished part, over the damage behaviour of individual inclusions, to the local description of individual voids. In this work, we set out to devise an analysis approach gathering information on all these scales. To this end, we conducted in-situ tensile tests while acquiring high resolution SEM panoramic images and analysed them with two neural networks, trained for this work, to detect damage sites with respect to the inclusions at which they nucleated. We find that the main damage mechanism during tensile deformation parallel to the length of inclusions is cracking and that damage evolution is equally influenced by void nucleation and void growth in the observed range of deformation. By focussing on the damaging behaviour of different inclusions, we show that the position of inclusions in the microstructure influences the resulting damage evolution and that the vicinity of pearlite bands leads to decreased damage formation.

Steels with high carbon content can hardly or not at all be welded, but are of great interest for cladding applications due to their high hardness. In this study, the influence of process parameters on weld seam geometry and process stability is investigated when welding AISI 52100 bearing steel using the laser hot-wire cladding process. Process stability is evaluated using actual and set values for the wire feed rate and current parameters to determine a process window for a stable welding process. Weld seams are measured and analyzed in terms of width, height, contact angle, and shape. The effect of the process parameters on the weld seam geometry is investigated and appropriate mathematical functions to describe the geometry are determined. Process parameter sets in the range of 1-2 m/min wire feed rate and 45-75 A hot wire current were investigated. Unstable parameter sets occur clustered at high wire feed rate of 2 m/min for all hot wire currents. In addition, the process is unstable at high hot wire current of 75 A and low wire feed speed of 1 m/min. The remaining parameter sets resulted in a stable process. The investigated functions parabolic, cosinusoidal and circular arc for the mathematical description of the weld seam geometry, no clearly significant result could be determined. Only a trend towards the circular arc function and the parabolic function is apparent.

This paper presents a parallel zigzag (raster) tool-path generation method for Additive Manufacturing (AM). Based on the analysis of some ordinary serial algorithms, it was observed that some compute-intensive operations could be parallelized by using a Graphics Processing Unit (GPU) architecture. However, to achieve this, many challenges were faced and solved by designing a method to work concurrently with individual contour segments on multiple layers while keeping the data organized. The method’s ability to solve the zigzag generation problem was verified, and its performance was measured by running an exhaustive search for optimal raster angles to reduce manufacturing time. The results showed that the method was effective and generated relevant computational gain, being up to 9 times faster than its serial counterpart. In the tool-path optimization, the simulations found configurations yielding an average length of raster lines up to 38% longer, which, in turn, can reduce manufacturing time.

Additive Manufacturing (AM) is one of the key technologies of Industry 4.0, offering unique advantages and capabilities. The interest in AM has been steadily increasing, leading to its rapid recent growth and improvement in all its aspects. However, its wider adoption is hindered by various barriers, the most important of which are the relatively high initial investment cost, part quality issues, limited material choices, and lack of expertise. The research community, AM machine developers, and larger enterprises are continuously contributing to the improvement of the first three factors. Nonetheless, the same cannot be stated for the barrier of limited expertise, leading the industrial sector to a perpetual lack of knowledge and, therefore, reluctance for a potential AM uptake. This study is addressing the need of the industrial sector for structured and organized expertise training for the fruitful exploitation of AM, paving the road for its wider application. The guidelines for an industrial-oriented AM training curriculum are set through the development of an AM training framework. The different AM thematic areas are classified into educational modules, which are separately analyzed, considering the participants’ active role and hands-on practice. The proposed step-by-step approach builds up from introductory to more advanced concepts, ensuring flexibility and simultaneously encompassing the needs of all industrial stakeholders (engineers, designers, managers, operators). Additionally, strategies corroborating the accessibility of the proposed framework are discussed, as well as dissemination policies and tools to facilitate its industrial endorsement.

The industrial progress made throughout these years has led to great results in terms of producing fast and with good quality. However, the impacts related to that production, whether these are environmental, economic, or social have been, at times, neglected. The manufacturing sector, as one of the most polluting sector, felt the urge to adapt to this industrial progress and find ways to produce with improved sustainability goals without compromising the quality of the final product and the production time. Industry easily understood the benefits of this greener approach, and, with this, new sustainable technologies started to emerge. Additive Manufacturing (AM) is one of those technologies that provide alternative sustainable paths to traditional manufacturing. In order to generalize the benefits of AM production in terms of sustainability, when compared to traditional processes, further investigations must be conducted. In this sense, the proposed work has the intention of finding the environmental impacts associated with a particular AM technique for the fabrication of metal parts, Wire Arc Additive Manufacturing (WAAM). A practical work based on the production of three different complexity metal parts considering an additive (WAAM) and a subtractive (Computer Numerical Control (CNC) Milling) manufacturing process is developed. To quantify the environmental impacts of both processes, the author resorts to the Life Cycle Assessment (LCA) methodology. The assessment is conducted in the SimaPro 9.2 software, accordingly to ISO 14044:2006 standard. The results allow a comparison between both types of manufacturing and enable the suggestion of measures to decrease the environmental footprint of WAAM. It was found that WAAM approach leads to a material saving ranging between 40% and 70% and an environmental impact reduction in the range of 12%–47%, compared to the subtractive approach for fabricating the 3 geometries considered in this study. The conclusions obtained are specific to this particular application and, once more, it is acknowledged that in order to reach a global understanding relative to this technology's environmental implications, extra research still needs to be made.

The cost-effectiveness and the environmental impact of Additive Manufacturing (AM) are nowadays two of the hottest process-related industrial and research topics. Energy efficiency is a strong claim, and so is the demand for durable and functional 3D-printed workpieces. These contradictory aspects usually require flexibility and compromises. Especially for Material Extrusion (MEX) 3D printing, the plurality of the control parameters makes such optimizations complicated. This research explores the effect of seven generic and machine-independent control factors (e.g., Raster Deposition Angle; Orientation Angle; Layer Thickness; Infill Density; Nozzle Temperature; Bed Temperature, and Printing Speed) on energy consumption of Polylactic Acid over the compressive response of MEX 3D printed specimens. To make it possible, a three-level L27 orthogonal array was compiled. Each experimental run included five specimen replicas (after the ASTM D695-02a standard) summing up 135 experiments. The fabrication time and the energy consumption were determined by the stopwatch method, whereas the compressive strength, elasticity modulus, and toughness were derived with compressive tests. The Taguchi analysis ranked the impact of each control parameter on each response metric. The printing speed and the layer thickness were the most influential control parameters on energy consumption. Furthermore, the infill density and the orientation angle were found as the most dominant factors in the compressive strength. Finally, Quadratic Regression Model (QRM) equations for each response metric over the seven control parameters were compiled and validated. Hereto, the best settlement between energy efficiency and mechanical strength is now possible, an option with great technological and industrial merit.

Lightweight constructions made of different materials are becoming increasingly important and joining of metal-plastic hybrids is a major challenge in this context. This paper investigates experimentally the electromagnetic joining of tubes made of aluminum alloy 6082 and thermoplastic polycarbonate. Therefore, electromagnetic joining tests, combined with destructive push-out tests and non-destructive computer tomography scans were conducted. The investigations showed a fundamental dependence of the joint strength on the diameter ratio of the inner joining partner. By increasing the ratio of inner to outer diameter, the transferable push-out force was increased by factor fifteen. Furthermore, for lower ratios, macroscopic cracks were detected that limited the transferable forces.

The automotive industry is on the brink of transitioning to autonomous vehicles (AVs). This will require highly flexible assembly systems. This paper focuses on exploiting the capabilities of the technology base, e.g., sensors and image recognition, of AVs as assembly items and employing their self-driving function in assembly systems. This fundamentally new approach to matrix manufacturing systems based on autonomously navigating automated guided vehicles (AGVs) and the elimination of set assembly sequences is a growing topic of discussion. This study develops a conceptual framework, based on a systematic literature review and interviews with fifteen experts from three carmakers, for exploring the field of research and assessing the feasibility of employing the technology base of autonomous driving instead of AGVs. This study is intended for assembly planners and researchers of assembly systems in automotive manufacturing.

Electric vehicles driven by batteries are a key part of a sustainable mobility sector. Unfortunately, battery cell production is still associated with various negative environmental impacts, the use of critical raw materials and high manufacturing costs. The rising battery demand forces automotive original equipment manufacturers to drastically increase their capabilities over the next decades while fulfilling economical and ecological requirements. Continuous production technologies bear the potential to meet future battery cell demands by enabling higher throughputs compared to established batch processes. The control and optimization of continuous battery cell production steps with respect to product quality, manufacturing costs and environmental impacts is challenging due to high parameter spaces as well as temporal dependencies of production processes. Therefore, this study develops a controller that performs real-time optimization by proposing set parameters leading to desired quality, minimal costs and impacts of manufacturing activity. The controller is implemented using a deep learning model incorporating sequential information of the production process. A continuous mixing process with data acquired from a battery cell pilot line is used to validate the outlined controller. As result, the implementation for this use case achieves a relative error of 7.63% across all controllable parameters.

There is a lack of a universal Augmented Reality platform which can be used in manufacturing engineering and other education fields to display models, processes, animations and simulations alike. Such a platform has been developed as part of this contribution and enables instructors to manage online courses, teaching units and even entire study programs. To enhance the teaching in the classroom by using Augmented Reality visualizations, a new application has been developed, which runs on iOS as well as Android systems and displays the various objects along with additional information uploaded by the instructors. A novel storage format was devised which reduces the storage size of various models significantly wherefore performance on the phone's end is improved. Various common 3D file formats, such as STL, OFF and OBJ, can be imported and automatically converted to this new format. The same applies for results from FEM software Abaqus, MoldFlow and HyperXtrude. Results formatted to be analyzed by the popular pre-/post-processor GiD can also be uploaded at no additional expense. The users of the smartphone app can view, inspect and interact with the models and animations. The platform and app are designed for an easy-to-use setup by the educators and an intuitive use by the students.