Elasto-geometrical modeling and calibration of robot manipulators: Application to machining and forming applications

International audienceThis paper proposes an original elasto-geometrical calibration method to improve the static pose accuracy of industrial robots involved in machining, forming or assembly applications. Two approaches are presented respectively based on an analytical parametric modeling and a Takagi-Sugeno fuzzy inference system. These are described and then discussed. This allows to list the main drawbacks and advantages of each of them with respect to the task and the user requirements. The Fuzzy Logic model is used in a model-based compensation scheme to increase significantly the robot static pose accuracy in a context of incremental forming application. Experimental results show the efficiency of the Fuzzy Logic model while minimizing development and computational resources

Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control

Modeling of cutting forces in micro milling including run-out

Ankara : The Department of Mechanical Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 62-66.Micro milling is widely used in precision manufacturing industry which is suitable for producing micro scale parts having three dimensional surfaces made from engineering materials. High material removal rate is its main advantage over other micro manufacturing technologies such as lithography, micro EDM, laser ablation etc. Modeling of micro milling process is essential to maximize material removal rate and to obtain desired surface quality at the end of the process. The first step in predicting the performance of micro milling process is an accurate model for machining forces. Machining forces are directly related to machine tool characteristics where the process is performed. The spindle and the micro milling tool affects the machining forces. In this thesis, the influence of runout of the spindle system on micro milling forces is investigated. Two different spindle systems with different levels of runout are considered and necessary modifications are introduced to model the trajectory of the tool center for better prediction of process outputs in the presence of runout. A modified mechanistic force modeling technique has been used to model meso/micro scale milling forces. Detailed micro milling experiments have been performed to calculate the cutting and edge force coefficients for micro end mills having diameters of 2, 0.6, and 0.4 mm while machining titanium alloy Ti6AL4V. Good agreements have been observed between the predicted and measured forces. It is found that statically measured runout values do not translate into dynamic machining conditions due to machining forces acting on the end mill.Kanlı, MuammerM.S

Mechanical and dynamical process model for general milling tools in multi-axis machining

Multi-axis milling operations are widely used in many industries such as aerospace, automotive and die-mold for machining intricate sculptured surfaces. The most important aspects in machining operations are the dimensional integrity, surface quality and productivity. Process models are employed in order to predict feasible and proper process conditions without relying on empirical methods based on trial and error cutting and adaptation of previous experiences. However, previously developed process models are often case specific where the model can only be employed for some particular milling tools or they are not applicable for multi-axis operations. In many cases, custom tools with intricate profile geometries are compatible with the surface profile to be machined. On the other hand, for more robust and stable cutting operations, tools with wavy cutting edge profiles and varying geometric edge distributions are utilized. In this thesis, a complete numerical mechanic and dynamic process model is proposed where the tool is modeled as a point cloud in the cylindrical coordinates along the tool axis. The tool geometry is extracted from CAD data enabling to form a model for any custom tool. In addition, the variation in the cutting edge geometry, where serrated and variable helix/pitch cutting edges can be adapted for any milling tool is taken into account. The cutting engagement boundaries are identified numerically using a Boolean intersection scheme. Moreover, a Z-mapping algorithm is integrated in the proposed multi-axis mechanistic force model to predict cutting forces for a continuous process. As for the multi-axis milling dynamics, previous single-frequency stability models are extended to encompass all possible tool geometries taking the time delay variation introduced by irregular cutting edge geometries. The proposed model is experimentally verified with different tool geometries investigating cutting forces and also predicting the stable cutting conditions

Characterising the porosity of multi-component mixtures in rotary mills

Porosity, or fractional void volume, is a simple but powerful quantity used in describing porous media. Characterising this very important parameter is vital to understanding key processes that occur in porous media, such as fluid transport. This is because porosity is strongly related to the permeability of porous media

DESIGN OF A CUSTOM SOFTWARE APPLICATION TO MONITOR AND COMMUNICATE CNC MACHINING PROCESS INFORMATION TO AID IN CHATTER IDENTIFICATION

In any manufacturing environment, it is important to be able to monitor the Computer Numerical Control (CNC) machining process so that high quality parts can be produced in the least amount of time in order to be profitable. This involves acquiring the proper parameters needed from the machine\u27s controller, which can prove to be difficult with proprietary machine tools that tend to limit access to the internal data collected by the controller. This closed approach to controller design also means that many technological advances that have recently become prevalent in society are not being adopted in the manufacturing industry, preventing the interoperability between hardware and software components and adding to the shortcomings in communicating the necessary machining parameters to machine operators. The project described in this thesis offers a solution to some of the communication, productivity, and part quality problems in the American manufacturing industry by providing a custom software application that integrates MTConnect, an emerging interoperable data communication standard, with proprietary data acquisition tools and custom sensors to monitor and communicate CNC machining process information. The application described in this thesis was designed to aid in the identification of chatter conditions to the machine operator and to other users to take action for chatter suppression and avoidance. Chatter is an undesirable phenomenon that can reduce part quality and increase tool wear. These consequences result in higher costs to replace damaged parts and tools as well as increasing the amount of machine downtime which can reduce a company\u27s overall productivity. Once chatter is detected in the audible frequency range, damage to the workpiece has already occurred. Therefore, an early identification and communication method with the machine tool is warranted to easily monitor the machine in the event of impending dynamic part damage. This application was developed to provide a means to monitor cutting conditions to reduce and prevent chatter in the machining process and to aid in analysis to avoid subsequent unstable operating conditions. Preserving part quality and productivity in manufacturing is also dependent on accurate information provided about the specific parts involved in the machining process. In addition to monitoring the process, this application facilitates the communication of part-specific information by improving the input and tracking of part numbers, and organizes the machining process information in a central location according to the specific part. Improving the part tracking process can aid in the organization of data to analyze the machining process for increased quality in future operations. The application can also be customized for other implementations, which can benefit many different industrial manufacturing facilities as well as academics in performing experimental research. It is important for the manufacturing industry and its partners in academia to be able to bridge the communication gap to increase the knowledge of the machining process and therefore manufacturing productivity and profitability

Development of a Pavement Maintenance and Rehabilitation Project Formation and Prioritization Methodology that Reflects Agency Priorities and Improves Network Condition

Methodical maintenance and renewal of infrastructure systems is critical due to the rapid deterioration of infrastructure assets under increasing loads and environmental effects and the scarcity of resources allocated for their preservation. A crucial step in pavement management is the formation and prioritization of maintenance and rehabilitation (M&R) projects that compete for limited funding for inclusion in the agency’s multiyear pavement management plan (PMPs). In general, many highway agencies perform this task subjectively, and thus a more rational and objective approach is desired to produce sound and justifiable PMPs. Specifically, such methodology should take into account the multiple factors that are considered by engineers in prioritizing M&R projects. This research addresses this need by developing a methodology for use by the Texas Department of Transportation (TxDOT) in preparing their four-year PMPs. Several key decision factors were considered and TxDOT decision makers were surveyed to weigh these factors as to their influence on prioritizing M&R projects. These were then used to develop a priority score for each candidate M&R project. Since TxDOT collects and stores data for individual 0.5-mile pavement sections, these sections must be grouped in a logical scheme to form realistic candidate M&R projects. The incremental benefit-cost analysis was performed on the candidate M&R projects to identify a set of M&R projects that maximizes network’s priority score under budgetary constraint. Future pavement condition was projected using performance prediction models and the process is repeated throughout the planning horizon to produce a multi-year pavement management plan. Data from Bryan district, which consists of 7,075 lane-miles of roadway, were used to develop and validate the PMP methodology. Comparison with the actual PMP (produced by TxDOT) shows some disagreements with the PMP generated by the methodology though the latter was shown to produce more cost-effective and defendable pavement management plans. Since the methodology is founded on TxDOT engineers’ decision criteria and preferences, they can be assured that the PMPs produced by this methodology are in line with their goals and priorities

Magnetoencephalographic studies of neural systems associated with higher order processes in humans

This thesis has been concerned with the neuromagnetic fields associated with the processing of faces and sentences in humans. In four, largely independent sub-projects, results were obtained using novel methods of analysis to extract neurophysiologically relevant information from magnetoencephalographic MEG readings. Using the MEG facility of the Helsinki University of Technology, Finland, the research has led to four main suggestions: a) there are early latency face-specific neural systems in humans that are predominantly in right inferior occipito-temporal cortex, b) MEG recordings are useful in the study of autism, in that autistic subjects exhibit different responses to normal subjects following face presentation, c) phase-locked y-band activity has a specific role in semantic processing of sentences in normal subjects, and d) the late components of responses to face images are modified by endogenous priming, which is detectable before stimulus arrival in normal subjects. In order to pursue these neuroscience objectives, new methods for treating MEG data were developed, implemented and used. These comprise: a) an improved parameterisation of signal power over regions of interest, b) the use of re-sampling strategies to achieve statistical assessment of spectral coefficients within subjects, and c) a prestimulus method for the study of face processing using a tailored state-space representation approach

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown

Sequence Determinants of the Individual and Collective Behaviour of Intrinsically Disordered Proteins

Intrinsically disordered proteins and protein regions (IDPs) represent around thirty percent of the eukaryotic proteome. IDPs do not fold into a set three dimensional structure, but instead exist in an ensemble of inter-converting states. Despite being disordered, IDPs are decidedly not random; well-defined - albeit transient - local and long-range interactions give rise to an ensemble with distinct statistical biases over many length-scales. Among a variety of cellular roles, IDPs drive and modulate the formation of phase separated intracellular condensates, non-stoichiometric assemblies of protein and nucleic acid that serve many functions. In this work, we have explored how the amino acid sequence of IDPs determines their conformational behaviour, and how sequence and single chain behaviour influence their collective behaviour in the context of phase separation. In part I, in a series of studies, we used simulation, theory, and statistical analysis coupled with a wide range of experimental approaches to uncover novel rules that further explore how primary sequence and local structure influence the global and local behaviour of disordered proteins, with direct implications for protein function and evolution. We found that amino acid sidechains counteract the intrinsic collapse of the peptide backbone, priming the backbone for interaction and providing a fully reconciliatory explanation for the mechanism of action associated with the denaturants urea and GdmCl. We discovered that proline can engender a conformational buffering effect in IDPs to counteract standard electrostatic effects, and that the patterning those proline residues can be a crucial determinant of the conformational ensemble. We developed a series of tools for analysing primary sequences on a proteome wide scale and used them to discover that different organisms can have substantially different average sequence properties. Finally, we determined that for the normally folded protein NTL9, the unfolded state under folding conditions is relatively expanded but has well defined native and non-native structural preferences. In part II, we identified a novel mode of phase separation in biology, and explored how this could be tuned through sequence design. We discovered that phase separated liquids can be many orders of magnitude more dilute than simple mean-field theories would predict, and developed an analytic framework to explain and understand this phenomenon. Finally, we designed, developed and implemented a novel lattice-based simulation engine (PIMMS) to provide sequence-specific insight into the determinants of conformational behaviour and phase separation. PIMMS allows us to accurately and rapidly generate sequence-specific conformational ensembles and run simulations of hundreds of polymers with the goal of allowing us to systematically elucidate the link between primary sequence of phase separation