Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art

Information contributes to the improvement of decision-making, process improvement, error detection, and prevention. The new requirements of the coming Industry 4.0 will make these new information technologies help in the improvement and decision-making of industrial processes. In case of the welding processes, several techniques have been used. Welding processes can be analyzed as a stochastic system with several inputs and outputs. This allows a study with a data analysis perspective. Data mining processes, machine learning, deep learning, and reinforcement learning techniques have had good results in the analysis and control of systems as complex as the welding process. The increase of information acquisition and information quality by sensors developed at present, allows a large volume of data that benefits the analysis of these techniques. This research aims to make a bibliographic analysis of the techniques used in the welding area, the advantages that these new techniques can provide, and how some researchers are already using them. The chapter is organized according to some stages of the data mining process. This was defined with the objective of highlighting evolution and potential for each stage for welding processes

Utilizing RepRap Style 3D Printers for the Manufacturing of Composite Heat Exchangers

The low cost 3D printing market is currently dominated by the application of RepRap (self-replicating rapid-prototyper) variants. Presented in this document are practical utilizations of RepRap technology. Developed are innovative processes to manufacture composite materials systems for thermal management solutions. First, a laser polymer welder system is validated by quantifying maximum peak load and weld width of linear low density polyethylene (LLDPE) lap welds as a function of linear energy density. The development of practical engineering data, in this application, is critical to producing mechanically durable welds. Developed laser and printer parameter sets allow for manufacturing of LLDPE multi-layered heat exchangers Second, newly introduced metal-polymer composite materials (e.g. copper-PLA, bronze-PLA, iron-PLA and stainless steel-PLA) were shown to influence the thermal conductivity (W/m·K) of the composite matrix. Increased volume percentage of metallic constituent was shown to increase thermal conductivity. Air void fraction, a resultant of the manufacturing process, reduced the bulk composite 3D printed component. No significant effects were realized dependent upon the metallic constituent morphology (i.e. flake-like vs. spherical). Third, development and fabrication of a large format multi-head RepRap 3D printer displays the ability of large-scale manufacturing potential. Energy efficiencies are realized upon utilization of all hot-ends (i.e. the embodied energy of each printer movement (X, Y and Z)) and are simultaneously shown at each hot-end. Furthermore, multi-head format printers are proven to develop composite components. Utilizing a novel weaving and layering method 1000-series aluminum wire is embedded into a polyethylene terephthalate glycol modified (PETG) matrix. Parametric customized gcode commands allow for innovative manufacturing. In total, laser parameter development, material characterization, custom machine fabrication and printing process development are quantified. The three presented projects demonstrate the engineering advancement of RepRap technology in application to thermal management solutions and composite material development

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Preliminary machine learning analysis and high-speed thermographic visualization of the laser polishing process

11th CIRP Conference on Photonic Technologies [LANE 2020], September 7-10, 202

Feasibility study of imaging spectroscopy to monitor the quality of online welding

An online welding quality system based on the use of imaging spectroscopy is proposed and discussed. Plasma optical spectroscopy has already been successfully applied in this context by establishing a direct correlation between some spectroscopic parameters, e.g., the plasma electronic temperature and the resulting seam quality. Given that the use of the so-called hyperspectral devices provides both spatial and spectral information, we propose their use for the particular case of arc welding quality monitoring in an attempt to determine whether this technique would be suitable for this industrial situation. Experimental welding tests are presented, and the ability of the proposed solution to identify simulated defects is proved. Detailed spatial analyses suggest that this additional dimension can be used to improve the performance of the entire system

Real-time detection of the aluminium contribution during laser welding of Usibor1500 tailor-welded blanks

The identification and intensity estimation of some aluminium emission lines have been proposed to perform an on-line quantification of the Al contribution to the laser-welding process of Usibor blanks. This boron steel is protected by an Al-Si coating that is removed by laser ablation before welding. If this process fails to remove Al from the joint surface, its contribution may affect the final properties of the resulting seams, therefore compromising their quality. Experimental tests have been performed, some of them in a real production scenario. They have been analysed and compared to the results of welding test specimens, analysis of the associated tensile properties and fracture locations and seam macrographs. These studies have indicated that on-line quantification of the Al contribution to the process is feasible and that a correlation can be established between the Al content estimated in real-time and the results derived from the off-line tests considered.The authors would like to thank the staff of Autotech Engineering and Solblank (both Gestamp companies) for their valuable help during the design, implementation and test of the monitoring system. This work has been supported by the project TEC2013- 47264-C2-1-

New Trends and Applications in Femtosecond Laser Micromachining

This book contains the scientific contributions to the Special Issue entitled: "New Trends and Applications in Femtosecond Laser Micromachining". It covers an array of subjects, from the basics of femtosecond laser micromachining to specific applications in a broad spectra of fields such biology, photonics and medicine