Identifying parameters of a broaching design using non-linear optimisation

Broaching is one of the most recognised machining processes that can yield high productivity and high quality when applied properly. One big disadvantage of broaching is that all process parameters, except cutting speed, are built into the broaching tools. Therefore, it is not possible to modify the cutting conditions during the process once the tool is manufactured. Optimal design of broaching tools has a significant impact to increase the productivity and to obtain high quality products. In this paper, an optimisation model for broaching design is presented. The model results in a non-linear non-convex optimisation problem. Analysis of the model structure indicates that the model can be decomposed into smaller problems. The model is applied to a turbine disc broaching problem which is considered as one of the most complex broaching operations

Infrared monitoring of aluminium milling processes for reduction of environmental impacts

In modern manufacturing contexts, process monitoring is an important tool aimed at ensuring quality standard fulfilment whilst maximising throughput. In this work, a monitoring system comprised of an infrared (IR) camera was employed for tool state identification and surface roughness assessment with the objective of reducing environmental impacts of a milling process. Two data processing techniques, based on statistical parameters and polynomial fitting, were applied to the temperature signal acquired from the IR camera during milling operations in order to extract significant features. These features were inputted to two different neural network based procedures: pattern recognition and fitting, for decision making support on tool condition and surface roughness evaluation respectively. These capabilities are discussed in terms of reducing waste products and energy consumption whilst further improving productivity

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

Proceedings of the Summer School / Graduate School 1483, Process Chains in Production - Interaction, Modelling and Assessment of Process Zones (KIT Scientific Reports ; 7611)

In the last years the meaning of simulation of manufacturing technologies becomes more and more important. Strategies to simulate single manufacturing processes are already developed and often successfully implemented. Now it is necessary to link the individual simulation steps, in order to be able to reliably simulate complete process chains from the semi-finished material to the complete part. This is the central research idea of the Graduate School 1483

Aggregate process planning and manufacturing assessment for concurrent engineering

The introduction of concurrent engineering has led to a need to perform product development tasks with reduced information detail. Decisions taken during the early design stages will have the greatest influence on the cost of manufacture. The manufacturing requirements for alternative design options should therefore be considered at this time. Existing tools for product manufacture assessment are either too detailed, requiring the results of detailed design information, or too abstract, unable to consider small changes in design configuration. There is a need for an intermediate level of assessment which will make use of additional design detail where available, whilst allowing assessment of early designs. This thesis develops the concept of aggregate process planning as a methodology for supporting concurrent engineering. A methodology for performing aggregate process planning of early product designs is presented. Process and resources alternatives are identified for each feature of the component and production plans are generated from these options. Alternative production plans are assessed in terms of cost, quality and production time. A computer based system (CESS, Concurrent Engineering Support System) has been developed to implement the proposed methodology. The system employs object oriented modelling techniques to represent designs, manufacturing resources and process planning knowledge. A product model suitable for the representation of component designs at varying levels of detail is presented. An aggregate process planning functionality has been developed to allow the generation of sets of alternative plans for a component in a given factory. Manufacturing cost is calculated from the cost of processing, set-ups, transport, material and quality. Processing times are calculated using process specific methods which are based on standard cutting data. Process quality cost is estimated from a statistical analysis of historical SPC data stored for similar operations performed in the factory, where available. The aggregate process planning functionality has been tested with example component designs drawn from industry

A comprehensive ship weather routing system using CMEMS products and A* algorithm

We describe the implementation of a comprehensive software for Ship Weather Routing referred to as SIM- ROUTE. The A* pathfinding algorithm is used to optimize the sailing route as a function of the wave action. The aim of the software is to provide a comprehensive, open and easy tool including pre- and post-processing for ship weather routing simulations. The software is constructed considering the Copernicus Marine Environment Monitoring Service (CMEMS) wave predictions systems which are available for free use. The code provides the optimized route and the minimum distance route together with additional modules to compute ship emission and safety on navigation monitoring. SIMROUTE has been tested in several cases using different CMEMS products over short and long distances. The comprehensive structure of the code enables it to be easily modified to include additional ship wave resistance models and the effect of the water currents and winds on navigation. SIMROUTE is also used for academic purposes, providing skills for ship routing optimization in the framework of standards of training, certification and watchkeeping (STCW) for competence-based maritime education and training. Due to the simplicity of its use, SIMROUTE is a good candidate for benchmarking strategies and inter-comparison exercises with advanced methods for ship weather routing. This contribution highlights the technical aspects, code organization and structure behind SIMROUTE, demonstrating its capabilities through examples of route optimization.Postprint (published version

Geometrical optimization of the broaching tools by leveling of the cutting forces

Subtractive machining has been one of the most extensively used manufacturing methods since the industrial revolution and the broaching operation is one of the ideal and oldest machining processes for accomplishing various applications such as turbine disc fir-tree slots, non-circular internal holes and keyways. Since the broaching operation accomplished by the linear cutting motion. Although broaching process is the only machining operation in order to machine complicated profiles without using a rotary motion, it is one of the least studied one in the literature. Due to nature of the broaching process, the broach tool design is the most important step during this operation since except cutting speed there is no any other flexibility. Therefore, modeling of the cutting process and predicting critical parameters before the design stage is crucial for optimum tool design. In previous studies, an optimized model without considering constant cutting forces for broaching tool design was presented. However, developing a method to generate an optimized broach tool design based on constant cutting forces can eliminate potential problems (i.e. reduced tool life and chipping, tooth breakage, poor surface quality etc.) while decreasing its length. In this study, a method is developed in order to minimize the length of the broach, increase tool life and quality of the final part leading to reduction of whole process cost by leveling of the cutting forces in each broaching process cycle, i.e. roughing, semi-finishing and finishing

Innovative Method dedicated to the development of a ferrite-pearlite grade regarding its MAChinability (IMMAC): final report

Ferrite-pearlite (FP) steels are the most common material for engineering and automotive industries (gear box parts, crankshaft, connecting rods, injection parts…). Without any extensive research, considering the different morphology of ferrite-perlite possible to achieve, it may be assumed that the machining performances are highly dependent on the FP parameters. Nevertheless, even now, we observe larger tolerances on requirements specification on FP steels which cause variability on microstructure morphology not always perceptible with standard metallurgical characterizations. In some case, the technical specification causes complex customer complaints between steelmakers and their customers: the microstructure seems as expected but unacceptable variability in machinability is observed. IMMAC project aims to develop a numerical method to predict the machining performances of designed FP steels depending on their microstructural parameters; and to use this method as a flexible steel development strategy to better design the machinability-improved grades tailored according to the part and its machining range. Three cutting technologies were studied: turning, drilling and broaching. The figure below shows a scheme of the research approach with proposed work packages (WP) interrelation. D0, D1 and D2 are main deliverables of the project