Procedia manufacturing最新文献

Conventional processes like shearing dispose a lack in efficiency and formability to manufacture functional components with a high geometric variety. A possibility to allow the efficient and sustainable manufacturing is the application of innovative forming operations like orbital forming. Thus, a strain-hardened component with a load-adapted thickness profile can be manufactured in a single-stage process. Thereby, the control of the material flow could be identified as major challenge due to the three-dimensional stress and strain state. In order to reduce the parts weight and simultaneously guarantee the same level of performance, conventional steel is substituted by lightweight materials such as precipitation hardenable aluminum alloys. However, new challenges arise due to the reduced formability of aluminum compared to conventional steel. In recent research, the potential of a short-term heat treatment to enlarge the formability could be shown. The presented process locally reduces the materials strength, thus allowing a control of the material flow by the interaction between softened areas and areas, which offer the initial conditions. In this contribution, the influence on the formability of orbital formed components manufactured out of the precipitation hardenable aluminum alloy EN AW 6016 by a local short-term heat treatment is investigated. Different geometry-based heat treatment layouts are applied in order to maximize the thickening on different radial positions. The potential to enhance the formability is evaluated by comparing geometrical and mechanical properties of the components manufactured in the conventional process route and with a previous heat treatment. The analysis of radial cross-sections and the hardness distribution reveals the positive influence of the heat treatment but also points out the geometrical dependency of the effect due to the characteristic material flow in orbital forming.

Laser based directed energy deposition (DED) is a competitive method for repairing and remanufacturing metallic parts used in numerous industries including aerospace and biomedical. However, the numerous dynamic phenomena associated with the DED process often result in defects such as entrapped-gas pores, lack of fusion, and undesirable anisotropic properties. The entrapped-gas pore, being one of the most common issues, not only influences melt-pool dynamics but also reduces the fabrication quality and mechanical properties of parts fabricated by the DED process. To reduce and further understand this issue, the real-time observation of the pore formation process needs to be studied first. To directly observe the phenomena in the melt pool, high-speed techniques are needed because rapid solidification leads to rapid pore formation and movement. In-situ high-speed X-ray has been proven to be an effective method in investigating the melt pool dynamics and pore formation mechanisms in the laser powder bed fusion process, in which the fabrication process is quite different from that in DED. Here, the high-speed X-ray method is extended to study the formation of entrapped-gas pores. The real-time formation and quantitative analysis of pores under each set of processing parameters (particle velocity, laser power, and spot welding dwelling time of stationary laser) in the DED process are investigated. We found that the DED with a higher particle velocity (3.19 m/s) produced a smaller average pore size of 27.8 µm and a lower pore area fraction of 0.52%. The DED under lower laser power (156 W) generated a smaller average pore size of 20.3 µm and a lower pore area fraction of 1.94%. The shorter dwelling time (10 ms) benefited the decrease of both average pore size and pore area fraction.

Deep hole drilling process is extensively used in demanding applications, namely plastic injection moulding, oil and gas industry and automobile engines. This drilling process encounters problems such as improper chip evacuation, poor surface finish, roundness variation, and high tool wear, which affects the hole quality. To overcome these limitations, ultrasonic vibration-assisted deep hole drilling (UVADD) is performed in this work. The high-frequency vibrations were imparted into the ASTM A36 steel (workpiece) to induce reciprocating motion, which is mounted over a transducer. A special fixture arrangement was fabricated to hold the transducer in the drill bed. This helps to transfer the vibration to the workpiece. The machining performance of UVADD was analyzed by means of cutting force, tool wear, torque, hole quality, surface roughness, machining time and chip morphology, and the performances were also compared with the conventional deep hole drilling (CDD) process. Results suggested that a reduction in cutting force and torque was observed for UVADD. This is due to small discontinuous chips generated. Hole quality showed a drastic decrease in burr formation and surface roughness along with a uniform radius around the periphery. From the tool wear examination, it was also evident that no built-up edge formation occurred along the cutting edge owing to effective penetration of coolant and reduction in cutting temperature. This comprehensive analysis revealed that UVADD provided an enhancement in machining performance as compared to CDD.

Part distortion during additive manufacturing (AM) may lead to catastrophic failure and significant waste of resources. Existing work often focuses on identification and detection of individual root causes such as melt pool geometries or extruder clogging to prevent part failures. Since the end-effect of major print failures can be the result of multiple error sources (including unknowns), relying on detection of individual root causes may misclassify some failed prints as successful. Instead, detecting end-effects or part distortion could provide early warning of major failures regardless of potential error sources. Distortion detection, however, currently involves computationally expensive simulation and analysis of sensing data. One promising solution is to adopt digital twin strategy to quickly compare model prediction to features extracted from in situ sensing data. This study extends the digital twin strategy to major distortion detection by developing (1) a multi-view optical sensing system for movable print beds and (2) failure detection methods by analyzing multi-view of part images layer by layer. Since the digital twin of actual prints at specific layers are generated offline, delay can be reduced to determine if a significant enough quality departure has occurred to justify termination of the print. In the experimental evaluation of this approach for a FDM machine with a moving print bed, failure was rapidly detected in two of the three test prints, while in the remaining print, failure was successfully detected after a short delay.

The industry invests significant resources towards qualification of its individual processes and machines to assure quality and productivity of the process chain. Process qualification traditionally involves employing elaborate experimental methods to find the response surface mapping the response to various process parameters and measurements. Most of the existing methods are passive experimental designs which take into account the limits of the parameter space and a design method (CCD, Taguchi, orthogonal etc.) to identify the points in the parameter space. More often than not, these methods need a lot of experiments to be conducted and do not take into account how the response changes with each experiment. Also, the number of experiments increase combinatorically to get a desired estimate of the response surface. The formulation of mathematical models for complex, high dimensional, inherently nonstationary, and stochastic systems like abrasive machining process and also catering to the process-machine interactions is challenging. In this work, to address the other alternative for cost-effective experimentation: we adapt a Query by Committee (QBC) based active learning approach where we sequentially find the next best experimental point to reduce the uncertainty of prediction of surface roughness over the sample space. The method uses a carefully curated list of committee members, (i.e., models) which predict the response surface at each instant and selects the next experimental point based on a metric called prediction deviation. We used a real-world dataset from a cylindrical plunge grinding platform to test if the QBC approach performs better than a passive CCD design. The machine tool used is the next generation precision grinder (NGPG) from IIT Madras which is capable to finishing components to an IT3 tolerance grade. We compared the QBC based active learning model to a previous random forest model built on a dataset which gave a test accuracy (R²) of 85% using 178 experimental points. It is demonstrated that similar prediction accuracies can be achieved by reducing the number of experiments by about 65%. The merits of the model in the choice of the members of the committee and the advantage of the current experimental design compared to random experimentation were presented.

The material pairing metal-plastic offers advantages such as a reduced weight and the ability to be operated in dry conditions, but the application is limited due to the occurring wear. The properties of the metallic gearing have a significant influence on the wear behavior. From previous investigations on the application of steel within the material pairing, the influence of the surface topography and the load case is known. With regard to further weight reduction, the application of light metals such as aluminum is promising. However, the low strength of aluminum poses a challenge due to the low wear resistance. The properties of the metallic gearing are not only determined by the choice of material but also by the manufacturing process. In this context, cold extrusion offers potential for the production of ready-to-use gears. Sufficient geometrical properties and a substantial increase in tooth flank hardness are achieved. In this contribution, the influence of the forming induced hardening on the wear behavior of aluminum gears was investigated. Wire eroded aluminum and steel gears were used for comparison. The low hardness of conventionally manufactured aluminum gears was identified as a major challenge. Subsequently, gears with significantly higher tooth flank hardness were manufactured in an adapted full-forward extrusion process and the improved wear behavior was verified. Finally, functional correlations regarding the hardness of the metal pinion and the resulting wear behavior were derived.

Industrial Big Data (IBD) and Artificial Intelligence (AI) are propelling the new era of manufacturing - smart manufacturing. Manufacturing companies can competitively position themselves amongst the most advanced and influential companies by successfully implementing Quality 4.0 practices. Despite the global impact of COVID-19 and the low deployment success rate, industrialization of the AI mega-trend has dominated the business landscape in 2020. Although these technologies have the potential to advance quality standards, it is not a trivial task. A significant portion of quality leaders do not yet have a clear deployment strategy and universally cite difficulty in harnessing such technologies. The lack of people power is one of the biggest challenges. From a career development standpoint, the higher-educated employees (such as engineers) are the most exposed to, and thus affected by, these new technologies. 79% of young professionals have reported receiving training outside of formal schooling to acquire the necessary skills for Industry 4.0. Strategically investing in training is thus important for manufacturing companies to generate value from IBD and AI. Following the path traced by Six Sigma, this article presents a certification curricula for Green, Black, and Master Black Belts. The proposed curriculum combines six areas of knowledge: statistics, quality, manufacturing, programming, learning, and optimization. These areas, along with an ad hoc 7-step problem solving strategy, must be mastered to obtain a certification. Certified professionals will be well positioned to deploy Quality 4.0 technologies and strategies. They will have the capacity to identify engineering intractable problems that can be formulated as machine learning problems and successfully solve them. These certifications are an efficient and effective way for professionals to advance in their career and thrive in Industry 4.0.

Decompressive craniectomy (DC) is a surgical procedure where a portion of the skull (flap) is removed to relieve the built-up pressure from the patient’s brain due to swelling of the brain tissue after a traumatic injury to the head. Subsequently, another surgical procedure called cranioplasty is carried out to fix an implant or bone flap in patients who have undergone DC. In this paper, an automatic design methodology for additive manufacturing of a PSCI (patient-specific cranial implant) has been proposed. The input is the DICOM digital data from a CT scan and the output is the STL file geometry of the cranial implant. The proposed method has been tested and validated using real de-identified DICOM data, and the resultant implant was 3D printed and fit to the skull of a cadaver. The key contribution made in this paper is the complete automation of the design of a PSCI based on the skull’s unique geometry using a combination of image-processing and computational geometry techniques. Another important characteristic of the proposed method is that medical professionals need not have any technical expertise in additive manufacturing or part design for generating a PSCI.

Proper functioning of rolling element bearings is critical to ensuring reliable and safe power transmission. The ability to automatically recognize fault-related characteristics is key to enabling intelligent bearing fault recognition. While many techniques have been developed, effective bearing fault recognition under non-stationary conditions remains a challenge. In this paper, a hybrid method that integrates generalized demodulation and artificial neural network is presented that has shown to improve the fault recognition accuracy. Based on the modulation characteristics of bearing vibration signals, a phase function is designed, which allows the mapping of the time-varying modulation rotating frequencies and fault characteristic frequencies into constant frequency components in the demodulation spectrums, thereby eliminating the effect of non-stationarity and facilitating physics-based feature extraction. The features are subsequently classified by an artificial neural network for fault recognition. The physical nature of the features provides the basis for the network to generalize well for unseen non-stationary conditions, and the method has shown to outperform a variety of existing bearing fault recognition techniques in experimental evaluations.

The advancing digitalisation of production processes increases data availability and enables real-time information sharing between value-adding partners. However, despite the technological feasibility, information relevant for production control to initiate target-oriented countermeasures against disturbances are rarely shared with value-adding partners. To create incentives for companies to exchange information for short-term production control, the respective values of information must be determined. This publication presents a method for quantifying the value of information for production control in cross-company value-adding networks on a monetary basis. The input for the required performance measurement system is derived from a simulation model of the production system. Applying the approach to a simulative scenario validates the method’s functionality and the potential of information sharing to reduce disturbance cost.