Non-Destructive Testing Inspection for Metal Components Produced Using Wire and Arc Additive Manufacturing

Funding Information: JPO acknowledges funding by national funds from FCT—Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020. This activity has received funding from the European Institute of Innovation and Technology (EIT)—Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. This body of the European Union receives support from the European Union’s Horizon 2020 research and innovation programme. Publisher Copyright: © 2023 by the authors.The wire and arc additive manufacturing (WAAM) process enables the creation and repair of complex structures based on the successive deposition of fed metal in the form of a wire that is fused with an electric arc and then solidifies. The high number of depositions required to create or repair parts increases the likelihood of defect formation. If these are reliably detected during manufacturing, timely correction is possible. However, high temperatures and surface irregularity make inspection difficult. Furthermore, depending on the size, morphology, and location of the defect, the part can be rejected. Recent studies have shown that non-destructive testing (NDT) based on different physical phenomena for the timely, reliable, and customized detection of defects can significantly reduce the rejection rate and allow in-line repair, which consequently reduces waste and rework. This paper presents the latest developments in NDT for WAAM and its limitations and potential.publishersversionpublishe

A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards

In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s

QR codes decoder

The aim of this project is to develop a device that demonstrates the ability to read barcodes, and is also capable of detecting and analysing QR codes from a picture

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Spring contact probes: wear characteristics testing for electrical and mechanical parameters

The study considers the development and evaluation of spring contact probes used for automated testing of printed circuit boards (PCBs) and assemblies. It considers the evolution of circuit technology which originated from the introduction of the thermionic valve at the beginning of the century. Since the introduction of the integrated circuit in the 1960's, the industry has seen considerable advances in integrated and printed circuit miniaturisation with its associated effect on the testability of the completed assembly. The close spacing between the tracks and pads within the printed circuit board, which is possibly loaded on both sides with integrated circuits and other components with fine pitch termination spacings, has initiated the rapid development of a specialised electronic test industry to ensure product quality. [Continues.

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Internal works quality assessment for wall evenness using vision-based sensor on a mecanum-wheeled mobile robot

Robotics in the construction industry has been used for a few decades up to this present time. There are various advanced robotics mechanisms or technologies developed for specific construction task to assist construction. However, not many researches have been found on the quality assessment of the finished structures. This research proposes a quality assessment robot that will assist in performing the assessment of the internal works of a building by assessing a quality assessment criterion in the Malaysian Construction Industry Standards. There are various assessment criteria such as hollowness, cracks and damages, finishing and jointing. This paper will focus on the wall evenness using a camera mounted on a mobile robot with a Mecanum wheel design. The wall evenness assessment was done via projecting a laser leveler on the wall and capturing the images by using a camera, which is later processed by a central controller. Results show that the deviation calculation method can be used to differentiate between even and uneven walls. Pixel deviations for even walls show values of less than 15 while uneven walls show values of more than 20 pixels