Advances in Industrial and Manufacturing Engineering最新文献

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.

Continuous production processes are often highly complex and involve machine failures as well as unscheduled process downtimes. Failures result in the production of waste and in high opportunity costs, but their causes are not always apparent to machine operators. As a result, identifying failure root causes and avoiding risky process states is of high interest for producers. This work presents an approach for a data-driven failure risk assessment that is validated on real-world process data of a nonwovens production line. In this approach, association rule mining is adapted to continuous processes for producing highly interpretable results in the form of association rules that represent the main causes for failures. The methodology includes data preparation, modelling of production states and the evaluation of root causes using an associative classification algorithm. The result of this paper is a method for an interpretable risk assessment in continuous production processes. By using the method in live production, causes of failures can be detected and interpreted. The universal structure of the developed method supports applications in many other continuous production processes.

For manually operated machine tools, the accuracy of the machine tool structure limits the accuracy of the parts produced. Such is not necessarily the case with computer numerically controlled (CNC) machine tools. This concept may not be immediately obvious to the engineering undergraduate. The method of error compensation is presented here in a manner that is accessible to the undergraduate engineering student. A homogeneous transformation matrix (HTM) model quantifies the geometric errors of a machine tool, which can be compensated for in software. The mathematical treatment is reduced to only essential elements to emphasize physical understanding. A key feature of this presentation is the application of the model to a three-axis milling machine. This illustration enables the undergraduate student to grasp the concept with ease. Another feature is that the entire model is developed from first principles, which does not require the student to invoke any empirical relationships. Three solved numerical problems illustrate the application of the model to practical situations. Information provided here may be used by the teacher as a template to introduce this subject at the undergraduate level.

Machine learning (ML) has become a key technology to leverage the potential of large data amounts that are generated in the context of digitized and connected production processes. In projects for developing ML solutions for production applications, the selection of hyperparameter optimization (HPO) techniques is a key task that significantly impacts the performance of the resulting ML solution. However, selecting the best suitable HPO technique for an ML use case is challenging, since HPO techniques have individual strengths and weaknesses and ML use cases in production are highly individual in terms of their application areas, objectives, and resources. This makes the selection of HPO techniques in production a very complex task that requires decision support. Thus, we present a structured approach for benchmarking HPO techniques and for integrating the empirical data generated within benchmarking experiments into decision support systems. Based on the data generated within a large-scale benchmarking study, the validation results prove that the usage of benchmarking data improves decision-making in HPO technique selection and thus helps to exploit the full potential of ML solutions in production applications.

The Wire and Arc Additive Manufacturing has called attention due to its potential in allowing the buildup of high integrity metallic parts using the commonly available welding robots in the industry. However, such a technology still presents some challenges, mainly related to obtaining optimal deposition parameters, which result in consistent layer geometry which leads to the robot and the welding power source programming. In this sense, the objective of this work is to analyze the influence of the parameters in bead and multi-layer wall geometries fabricated by the Cold Metal Transfer process to select the configurations that result in the best deposition control. The study was carried out in four steps: (i) deposition of single beads on plate, varying wire feed speed and travel speed that would result in stable and sound beads; (ii) deposition of five layer walls, considering both unidirectional and bidirectional path strategies, with and without stops between layers; (iii) deposition of ten and twenty layer walls, refining deposition parameters; and (iv) deposition of a one hundred layer wall, with the best parameter configuration among the previously tested, with bidirectional continuous strategy. The results showed that the geometry produced with a mean current of 62 A and torch travel speed of 8 mm/s along each layer and 24 mm/s on the transition between layers generated the best results, considering the natural cooling conditions. Also, the bidirectional path deposition presented the most regular geometries, when compared to the unidirectional strategy.

In the material extrusion (MEX) Additive Manufacturing (AM) technology, the layer-by-layer nature of the fabricated parts, induces specific features which affect their quality and may restrict their operating performance. Critical quality indicators with distinct technological and industrial impact are surface roughness, dimensional accuracy, and porosity, among others. Their achieving scores can be optimized by adjusting the 3D printing process parameters. The effect of six (6) 3D printing control parameters, i.e., raster deposition angle, infill density, nozzle temperature, bed temperature, printing speed, and layer thickness, on the aforementioned quality indicators is investigated herein. Optical Microscopy, Optical Profilometry, and Micro Χ-Ray Computed Tomography were employed to investigate and document these quality characteristics. Experimental data were processed with Robust Design Theory. An L25 Taguchi orthogonal array (twenty-five runs) was compiled, for the six control parameters with five levels for each one of them. The predictive quadratic regression models were then validated with two additional confirmation runs, with five replicas each. For the first time, the surface quality features, as well as the geometrical and structural characteristics were investigated in such depth (>500 GB of raw experimental data were produced and processed). A deep insight into the quality of the MEX 3D printed parts is provided allowing the control parameters’ ranking and optimization. Prediction equations for the quality features as functions of the control parameters are introduced herein, with merit in the market-driven practice.