T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM

With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs

Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks

For automating deburring of cast parts, this paper proposes a general method for estimating burr height using 3D vision sensor that is robust to missing data in the scans and sensor noise. Specifically, we present a novel data-driven method that learns features that can be used to align clean CAD models from a workpiece database to the noisy and incomplete geometry of a RGBD scan. Using the learned features with Random sample consensus (RANSAC) for CAD to scan registration, learned features improve registration result as compared to traditional approaches by (translation error (Δ18.47 mm) and rotation error(Δ43∘)) and accuracy(35%) respectively. Furthermore, a 3D-vision based automatic burr detection and height estimation technique is presented. The estimated burr heights were verified and compared with measurements from a high resolution industrial CT scanning machine. Together with registration, our burr height estimation approach is able to estimate burr height similar to high resolution CT scans with Z-statistic value (z=0.279).publishedVersio

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

An IoT based industry 4.0 architecture for integration of design and manufacturing systems.

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data through the Cloud/Web-based (CW) services and discusses an effective way to integrate them. In the 1st stage, a Radio-Frequency IDentification (RFID) technology containing Computer Aided Design (CAD) data/models of the product with the ability to design / redesign is scanned and sent to a secure Internet/Cloud Server (CS). Here the CAD models are auto identified and displayed in the Graphical User Interface (GUI) developed for the purpose. From the scanned RFID CAD data/models, the 2nd stage adopts unique machine learning technique(s) and identifies the design & manufacturing features information required for product manufacture. Once identified, the 3rd stage handles the necessary modelling changes as required to manufacture the part by verifying the suitability of process-based product design through user input from the GUI. Then, it performs a Computer Aided Process Planning (CAPP) sequence in a secure design cloud server designed using web-based scripting language. After this, the 4th stage generates Computer Aided Manufacturing (CAM) toolpaths by continuous data retrieval of design and tooling database in the web server by updating the RFID technology with all the information. The various processes involved the 3rd and 4th stages are completed by using ‘Agents’ (a smart program) which uses various search and find algorithms with the ability to handle the changes to the process plan as required. Finally, the 5th stage, approves the product manufacture instructions by completing the production plan with the approved sheets sent to the Computer Numerical Control (CNC) machine. In this article, the proposed architecture is explained through the concept of IoT data transfer to help industries driving towards Industry 4.0 by improving productivity, reducing lead time, protecting security and by maintaining internationals standards / regulations applied in their workplace

Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue

Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

An industry 4.0 framework for the quality inspection in gearboxes production

Nowadays, the development of Internet of Things (IoT) technologies have been enhancing the factory digitalization with several advantages in terms of production efficiency, product quality, and cost reduction. This opportunity encourages the implementation of digital twins related to physical systems for controlling the production workflow in real time. Firstly, the paper studies the enabling technologies for supporting the defect analysis in the context of Industry 4.0 for mechanical workpieces. Secondly, the approach aims to study the integration between the CAD geometry and the quality check process for the inspection planning. A Knowledge-Based tool has been proposed to support the configurations of the quality control chain for each CAD geometry. The test case is focused on the fragmented production of customized gearbox parts