Advances in Industrial and Manufacturing Engineering最新文献

Flux assisted tungsten inert gas welding (FATIG) welding is a modified version of tungsten inert gas (TIG) welding to achieve a higher depth of penetration. In the present work, nanoparticles SiO₂, Al₂O_3, Fe₂O₃, and CuO mix with acetone and coated on the joint before welding. Bead on plate welding using different variants of FATIG welding performed on Hastelloy C-22. A comparative study of these variants called Activated tungsten inert gas (ATIG) and Flux bound tungsten inert gas (FBTIG) welding was conducted to find out their effects on depth of penetration, depth to width (D//W) ratio, surface appearance, and slag detachability. In addition, the influence of acidic and basic nature of flux on weld bead geometry and surface appearance are analyzed. Acidic fluxes produce a smoother weld surface than basic oxide fluxes; additionally, acidic flux slag is less sticky than basic flux slag. activated TIG (ATIG) welding with SiO₂ flux increases penetration and D/W ratio by 125% and 190%, respectively compared to normal TIG welding.

Additive Manufacturing (AM) offers new design and manufacturing opportunities of thin-wall microchannel heat exchangers for aerospace and industrial applications. Laser Powder Directed Energy Deposition (LP-DED) is an AM process providing large scale manufacturing of thin-wall microchannel heat exchangers. Successful industrialization of the LP-DED process requires critical quantification and understanding of the metallurgical, geometric, and process limitations. Specifically, understanding the as-built surface texture, inclusive of roughness and waviness, is significant due to its effects on the friction factor and pressure drop within a heat exchanger. This experimental study completed a design of experiments (DOE) to determine the critical build parameters that impact surface texture for enclosed thin-wall samples. This study summarizes the characterization work of the LP-DED process for 1 mm enclosed walls with an Fe–Ni–Cr (NASA HR-1) alloy. The LP-DED parameters including laser power, powder feedrate, travel speed, layer height, and rotary atomized powder feedstock were modified in the experiment. An evaluation of the DOE samples and resulting surface texture is provided along with conclusions from these experiments. Results indicate that 3D areal and 2D profile (directional) surface texture is estimated by 2x the powder diameter that becomes captured or partially melted on the trailing edge of the melt pool. The fine powder showed a higher sensitivity to parameter changes but resulted in a higher density material and 23% reduction in roughness. Surface texture was also shown to vary between closed channel shapes (internal) due to ricochets, recirculation, and higher volume of powder available to bond compared to external (outer) surfaces. The understanding of the LP-DED process as-built surface texture is essential to fluid flow applications such as heat exchanges and can modify performance for enhanced heat transfer or can be a detriment to pressure drop.

Polymer blending is one of the popular methods for producing tailor-made materials by combining the properties of individual polymers. Binary polymer blends have quite commonly been used over the past few decades. Recently, researchers have shifted their focus towards ternary polymer blends and this study aims to investigate a ternary polymer blend system consisting of low-density polyethylene (LDPE), polystyrene (PS) and polymethyl methacrylate (PMMA). The LDPE/PS/PMMA blend was processed by melt blending using a twin-screw extruder. The effects of the extrusion process parameters (i.e., screw speed and barrel set temperatures) and the blend composition on the mechanical, rheological and thermal properties of the polymer blend and the degree of crystallinity of the LDPE matrix were studied. Three different screw speeds (i.e., 50 rpm, 100 rpm and 150 rpm), two different barrel set temperatures (i.e., 200 °C and 220 °C), and two different component mass ratios (i.e., 70/10/20 and 70/20/10) were studied. The results showed that the tensile properties of the LDPE/PS/PMMA blend were significantly influenced by its microstructure. Yield strength and Young's modulus decreased at first and then increased with increasing screw speed. The blend processed at a barrel set temperature of 220 °C was found to have better tensile properties than the blend processed at 200 °C. Furthermore, the blend with a PS content of 10 wt% possessed better tensile properties than the blend with a PS content of 20 wt%. Regardless of the blend compositions and the process settings, the LDPE/PS/PMMA blends reported better mechanical properties than those of pure LDPE with a Young's Modulus of 240 MPa and a yield stress of 10.47 MPa. The rheology of the blend was also significantly affected by the process parameters and the blend composition. However, different process parameters and mass ratios did not indicate a significant influence on the melting temperature (around 109.5 °C) and the degradation initiation temperature (around 252.3 °C) of the LDPE/PS/PMMA blend, but both the melting temperature and the degradation initiation temperature of the ternary blend were found to be slightly lower than those of pure LDPE. The degree of crystallinity of the LDPE matrix was also affected by both the screw speed and the barrel set temperature. The results revealed that, better mechanical properties can be achieved by blending PS and PMMA with LDPE without significantly affecting the thermal properties compared to those of pure LDPE.

Factories are complex systems, which are characterized by interlinked and overlapping life cycles of the constituent factory elements. Within this context, the heterogeneity of these life cycles results in life cycle complexity and corresponding conflicts and trade-offs that need to be addressed in decision situations during the planning and operation of factory systems. Also with respect to the transformation need towards environmental sustainability, there is a need for methods and tools for life cycle oriented factory planning and operation. This paper systematically reviews existing life cycle concepts of factory systems as well as frameworks, models and methods for the evaluation and engineering of factory life cycles. In order to respond to the above challenges, a general understanding about the factory life cycle, e.g. life cycle stages, related activities and interdependencies, is developed and action areas of life cycle engineering are discussed that could supplement factory planning. Following that, the paper presents an integrated, model-based evaluation and engineering framework of factory life cycles.

Development and modernization in joining and welding techniques of plastic materials have attracted much attention in the field of scientific research and industry. Friction stir welding and friction stir spot welding, nowadays, have gained a significant advantage over other joining techniques due to the ease of operation, with no need for any adhesives or external heaters, and the use of non-consumable tools. In this research, the friction stir spot welding process of similar and dissimilar polymeric materials (polypropylene and high density polyethylene) was investigated experimentally. The effect of tool rotational speed and dwell time on the lap shear strength was studied, while the tool plunging rate, plunging depth, and tool geometry were kept constant during all tests.

The welding of dissimilar polypropylene to high density polyethylene was successfully performed within the range of 1400–3500 rpm rotational speeds; however, it was unsuccessful with 800 rpm.

Moreover, the friction stir spot welding of similar polyethylene and polypropylene was studied, and the optimum welding conditions were 3500 rpm with a dwell time of 40 s, and 800 rpm with a dwell time of 120 s for polypropylene and polyethylene respectively.

Additive manufacturing parameters of high-performance polymers greatly affect the thermal history and consequently quality of the end-part. For fused deposition modeling (FDM), this may include printing speed, filament size, nozzle, and chamber temperatures, as well as build plate temperature. In this study, the effect of thermal convection inside a commercial 3D printer on thermal history and crystalline morphology of polyetheretherketone (PEEK) was investigated using a combined experimental and numerical approach. Using digital scanning calorimetry (DSC) and polarized optical microscopy (POM), crystallinity of PEEK samples was studied as a function of thermal history. In addition, using finite element (FE) simulations of heat transfer, which were calibrated using thermocouple measurements, thermal history of parts during virtual 3D printing was evaluated. By correlating the experimental and numerical results, the effect of printing parameters and convection on thermal history and PEEK crystalline morphology was established. It was found that the high melting temperature of PEEK, results in fast melt cooling rates followed by short annealing times during printing, leading to relatively low degree of crystallinity (DOC) and small crystalline morphology.

Machine tools are increasingly being equipped with edge computing solutions to record internal drive signals with high frequency. A large amount of available data may be used to develop new data-driven approaches to process optimization and quality monitoring. This paper presents a new approach to predict the quality of finished workpieces for three-axis milling processes with end mills. For this purpose, internal machine tool data provided by an edge computing solution was recorded and used to develop a Machine Learning based method for quality prediction. For the preparation of the data, an introduced domain knowledge-based slicing algorithm is applied, which allows the recorded data to be automatically and precisely assigned to the corresponding geometric elements on the workpiece. During data-driven modeling, 9 Machine Learning algorithms are compared to 4 Deep Learning architectures for multivariate time series classification. The results show that ensemble methods like Random Forest and Extra Trees as well as the Deep Learning algorithms InceptionTime and ResNet reach the best performances for the use case of data-based quality prediction.

Battery cells are central components of electric vehicles determining their operational characteristics, such as driving range, power output, and safety. Automotive OEMs undertake the necessary efforts to ensure the integration of safe and high-performance battery cells in their electrified fleets. In addition, an increased sustainable awareness of their customers and governmental policies force them to not only focus on operational goals, but rather on environmental aspects as well. Especially, battery cell manufacturing is associated with various negative environmental impacts (e.g. carbon dioxide emission). Therefore, this study develops a concept facilitating the development of novel continuous processes in battery cell manufacturing by enabling virtual experiments and an automatic optimization of economic and ecologic targets. Virtual experiments are enabled by training data-driven models that transfer the gained knowledge from development to large-scale production. The concept includes an inline-capable controller adjusting set points of process parameters with respect to a cost model quantifying product quality and environmental aspects. The validity of the proposed concept is demonstrated with data acquired from real battery cell production chain covering a continuous mixing process.

Faced with globalization, high competition, and the demands of a market in constant dynamism, companies strive to adopt measures for increasing their productivity, among which Lean Manufacturing stands out. Although this set of strategies allows optimizing the production by reducing waste, the literature review showed that, in several organizations, the implementation of Lean does not reflect positive impacts on productivity. It is frequently related to the superficial nature of the approach: the tools and methods are applied, but the repercussions on the workers are commonly neglected. In response, companies seek to implement Risk Management policies to assess injury risk factors for operators during task execution. This study highlights the importance of integrating Lean Manufacturing and Ergonomics principles into organizations to increase productivity and improve working conditions simultaneously. Therefore, by identifying improvement opportunities using the VSM tool, this work aims to implement an innovative and systematic intervention model, which enables the integrated application of Single-Minute Exchange of Dies (SMED) and ergonomic analysis in a metallurgical factory. To this end, the innovative Ergonomic SMED (ESMED) Model is proposed, comprising six steps, which, in this study, focus on the setup operations of a molding machine and by including Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Job Strain Index (JSI), Key Indicator Methods (KIM), and Shoaf's Model methods. Based on the results obtained, it is possible to evidence the usefulness and effectiveness of the proposed model in this scenario, emphasizing the 55% reduction in setup time and the extreme attenuation of the level of Work-Related Musculoskeletal Disorders (WMSDs) risk in workers.