Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

Atomic Spectra Database

A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Use of sensors in robotic welding for controlling the weld quality leads to replacement of manual welding operation in dangerous work environment in presence of high temperature and fumes even in small or medium scale enterprises. The seam tracking operation is very essential for extracting weld seam position which can be fed to robot controller for instructing robot along the weld seam path. The seam tracking operation can be executed by different types of sensors having their own merits and demerits. In this paper, different sensors and techniques used for seam tracking task in robotic welding have been discussed in detail. Each sensor has different method or technology of weld seam feature extraction which have been described by different authors in different ways. The chief tasks for seam tracking have been found to be weld starting and end point detection, weld edge detection, joint width measurement and weld path position determination with respect to robot co-ordinate frame. Thus sensors have a very important role in robotic welding for fully automating the system with in process real time monitoring of weld process parameters with the sensor feedback. In further discussion the practical use of different sensors in industries with a comparison of their advantages and disadvantages have been discussed. This Paper presents the role of sensors in robotic welding and a detail study of methodologies of weld seam position and geometry feature extraction by different sensors typically used for weld seam tracking.

Advances in weld seam tracking techniques for robotic welding: A review

Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ?0.17mm and ?0.3mm in robotic GTAW and GMAW, respectively. Using computer vision technology, we have solved some problems of seam tracking in robotic welding automation.We have designed a set of special vision sensor system for seam tracking and it has worked very well.A new improved Canny edge detection algorithm was proposed to detect the edges of weld image.We have further improved the precision of image processing in seam tracking.This paper systematically studied the application of computer vision technology in robotic GTAW and GMAW.

Computer vision technology for seam tracking in robotic GTAW and GMAW

Wire arc additive manufacturing (WAAM) is an important metal 3D printing method, which has many advantages, such as rapid deposition rate, low cost, and suitability for large complex metal components manufacturing, and it has received extensive attention. This paper summarizes the research developments of WAAM in recent years, including the WAAM-suitable metal materials and processing technology, deposition strategy optimization including slicing and path planning algorithm, multi-sensor monitoring and intelligent control, and the large complex metal components manufacturing technology. The promising development directions of WAAM are prospected, including the research of new materials and new technology, composite manufacturing, multi-sensor and real-time monitoring, algorithmic optimization of metal filling strategy, and the application of artificial intelligence technology in WAAM, etc.

Wire and arc additive manufacturing of metal components: a review of recent research developments

Intelligent robotic welding is an indispensable part of modern welding manufacturing, and vision-based seam tracking is one of the key technologies to realize intelligent welding. However, the adaptability and robustness of most image processing algorithms are deficient during welding practice. To address this problem, an adaptive feature extraction algorithm based on laser vision sensor is proposed. According to laser stripe images, typical welding seams are classified into continuous and discontinuous welding seams. A Faster R-CNN model is trained to identify welding seam type and locate laser stripe ROI automatically. Before welding, initial welding point is determined through point cloud processing to realize welding guidance. During seam tracking process, the seam edges are achieved by a two-step extraction algorithm, and the laser stripe is detected by Steger algorithm. Based on the characteristics of two kinds of welding seams, the corresponding seam center extraction algorithms are designed. And a prior model is proposed to ensure the stability of the algorithms. Test results prove that the algorithm has good adaptability for multiple typical welding seams and can maintain satisfying robustness and precision even under complex working conditions.

An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding

Automatic multi-pass route planning is one of key technologies for thick plate in robotic metal active gas (MAG) arc welding. In this research, a scheme for the extracting feature points of the weld seam profile to implement automatic multi-pass route planning, and guidance of the initial welding position in each layer during MAG arc welding, is presented. It consists of two steps: first a vision sensor based on structured light is employed to capture laser stripes and molten pools simultaneously within the same frame, and the laser stripe, forming the weld seam profile is detected by a visual attention model based on saliency. Then a methodology of polynomial fitting plus derivatives for feature point extraction of the weld seam profile is suggested. With respect to the effectiveness of highlighting the laser stripe, the proposed model is much better than the classic ones in this field, whereas the feature point extraction methodology in this paper outperforms typical template matching. Finally, the performance of the proposed scheme is demonstrated on different weld seam images captured in different layers and different welding experiments. New vision sensor captures laser stripes and molten pools simultaneously.A visual attention model for detecting weld seam profiles is proposed.Three kinds of feature points of weld seam profiles can be extracted.Different images captured in different layers validate the proposed methods.The extracted feature points can be used for multi-pass route planning.

Yanling Xu

Papers

Computer vision technology for seam tracking in robotic GTAW and GMAW

Wire and arc additive manufacturing of metal components: a review of recent research developments

An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding

Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model

Automated control of welding penetration based on audio sensing technology