Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers' demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

An experimental investigation of chatter effects on tool life

Tool wear is one of the most important considerations in machining operations as it affects surface quality and integrity, productivity and cost. The most commonly used model for tool life analysis is the one proposed by F.W. Taylor about a century ago. Although the extended form of this equation includes the effects of important cutting conditions on tool wear, tool life studies are mostly performed under stable cutting conditions where the effect of chatter vibrations are not considered. This paper presents an empirical attempt to understand tool life under vibratory cutting conditions. Tool wear data are collected in turning and milling on different work materials under stable and chatter conditions. The effects of cutting conditions as well as severity of chatter on tool life are analyzed. The results indicate significant reduction in tool life due to chatter as expected. They also show that the severity of chatter, and thus the vibration amplitude, strongly reduces the life of cutting tools. These results can be useful in evaluating the real cost of chatter by including the reduced tool life. They can also be useful in justifying the cost of chatter suppression and more rigid machining systems

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

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

Analytical models for high performance milling. Part I: cutting forces, structural deformations and tolerance integrity

Milling is one of the most common manufacturing processes in industry. Despite recent advances in machining technology, productivity in milling is usually reduced due to the process limitations such as high cutting forces and stability. If milling conditions are not selected properly, the process may result in violations of machine limitations and part quality, or reduced productivity. The usual practice in machining operations is to use experience-based selection of cutting parameters which may not yield optimum conditions. In this two-part paper, milling force, part and tool deection, form error and stability models are presented. These methods can be used to check the process constraints as well as optimal selection of the cutting conditions for high performance milling. The use of the models in optimizing the process variables such as feed, depth of cut and spindle speed are demonstrated by simulations and experiments

A reliable turning process by the early use of a deep simulation model at several manufacturing stages

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy, and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on the equations describing the physical laws of the machining processes; however, additional efforts are needed to overcome the gap between scientific research and real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on "deep-knowledge and models" that aid machine designers, production engineers, or machinists to get the most out of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system and process as input data, and generates results that help in the proper decision-making and machining plan. Direct benefits can be found in (a) the fixture/ clamping optimal design; (b) the machine tool configuration; (c) the definition of chatter-free optimum cutting conditions and (d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.The work presented in this paper was supported in some sections within the project entitled MuProD-Innovative Proactive Quality Control System for In-Process Multi-Stage Defect Reduction- of the Seventh Framework Program of the European Union [FoF.NMP.2011-5] and UPV/EHU under program UFI 11/29. Thanks are given to Tecnalia, for collaboration in testing, and especially to Ainhoa Gorrotxategi and Ander Jimenez for the sound work in the project. Thanks to Gamesa Eolica and Guruzpe, for the time, technical advices, and efforts during the analysis in examples

Precision Machining

The work included in this book focuses on precision machining and grinding processes, including milling, laser machining and polishing on various materials for high-end applications. These processes are in the forefront of contemporary technology, with significant industrial applications. Their importance is also made clear by the important works that are included in the research that is presented in the book. Some important aspects of these processes are investigated, and process parameters are optimized. This is performed in the presented works with significant experimental and modelling work, incorporating modern tools of analysis and measurements

Drilling Process in γ-TiAl Intermetallic Alloys

Gamma titanium aluminides (gamma-TiAl) present an excellent behavior under high temperature conditions, being a feasible alternative to nickel-based superalloy components in the aeroengine sector. However, considered as a difficult to cut material, process cutting parameters require special study to guarantee component quality. In this work, a developed drilling mechanistic model is a useful tool in order to predict drilling force (Fz) and torque (Tc) for optimal drilling conditions. The model is a helping tool to select operational parameters for the material to cut by providing the programmer predicted drilling forces (Fz) and torque (Tc) values. This will allow the avoidance of operational parameters that will cause excessively high force and torque values that could damage quality. The model is validated for three types of Gamma-TiAl alloys. Integral hard metal end-drilling tools and different cutting parameters (feeds and cutting speeds) are tested for three different sized holes for each alloy.RTC-2014-1861-4, INNPACTO DESAFIO II. Spanish Governmen