R. J. Urbanic

An Integrated Systematic Design Recovery Framework

Reverse engineering aims at reproducing an existing object by analyzing its dimensions, features, form, and properties. Reversing geometry has traditionally been emphasized in this process. The collected data and information must be transformed into pertinent product knowledge at both the detail and embodiment levels. A thorough analysis of the environment must be conducted in order determine the functional requirements, infer the original needs, and deduce the form and fit features. An integrated approach that blends techniques such as IDEF modeling, scanning, and physical measurements, least-squares methods, and statistics used for process capability analysis in an innovative manner can lead to a more complete model, as no one set of tools can provide a complete, comprehensive engineering representation. An integrated and systematic framework for design recovery of mechanical parts is proposed. Forward engineering techniques should be applied appropriately throughout and integrated with the reverse engineering process to heal the knowledge gaps. Examples are presented that illustrate the application of the proposed integrated approach and highlight its merits.

Fused Deposition Modeling Design Rules for Building Large, Complex Components

ABSTRACTThe Fused Deposition Modeling (FDM) process is a bead deposition based additive manufacturing (AM) process that builds a product from thin layers of molten thermoplastic filaments. The ongoing goal of this research is to develop methodologies for designing and fabricating large complex parts such as complex beta testing prototypes, or sand casting patterns. The unique capabilities of the FDM process are leveraged when designing components and assemblies. Complex geometry can be readily manufactured allowing designers to incorporate non-standard component features, and consider unique solutions; however, there are size, surface finish, and accuracy limitations. Rules are developed to leverage the process characteristics and address the observed limitations. Case studies are presented to highlight the benefits and challenges when using the FDM process.

Design recovery of internal and external features for mechanical components

Contemporary reverse engineering (RE) tools focus on generation of free-form shapes from point cloud data collected from scanning systems. The final model contains a set of surfaces and curves that have no functional meaning, and noise due to manufacturing variations or wear are contained within the model. A different approach is required in order to create a more suitable model for engineered components because of these issues. To meet these challenges, a systematic approach is adopted in a comprehensive manner to extract the relevant information and transform it into pertinent design knowledge. A modular design recovery framework is presented that captures the components structure, function and feature information at varying perspectives. To complement the framework, form recovery algorithms have been developed to transform point cloud data into wire frame geometry consisting of standard line and arc elements. Once the points are converted into curve primitives, adjustments are made to capture the design intent using heuristics, and common shapes and two-dimensional (2D) patterns are detected. From this geometry, a surface or solid model can be constructed using established geometry creation tools. Several case studies are presented that illustrate the form recovery algorithms to highlight their merits.

Collaborative design recovery framework

Full design recovery aims at reproducing an existing object by analysing its dimensions, features, form and properties. The collected data and information must be transformed into pertinent product knowledge at the system, embodiment and detail levels. This requires a coordinated, collaborative effort to collect and analyse the data and other available information. A thorough analysis of the environment must be conducted in order to determine the functional requirements, infer the original needs, and deduce the form and fit features for the part of interest. Cases may exist where the existing part needs to be based on a new set of manufacturing or operating constraints. The modular structure of the design recovery framework can be extended to capture data from several components, so that key information can be extracted and utilized in the design and manufacture of the replacement component. To address these issues, an integrated and systematic framework for design recovery of mechanical parts is proposed. An example is presented that illustrates the application of the proposed approach.

Functional Work Space Estimation of a Robot Using Forward Kinematics, D-H Parameters, and Shape Analyses

The functional work space for a given orientation is a subset of the work envelope and is not intuitive to define for 6 axis industrial robots. A 2D boundary curve is derived for each desired end effector orientation and tool vector. This is done via a geometric analysis and using the Denavit-Hartenberg notation for the forward kinematic representation. The feasible region for all orientations is determined by the use of Boolean intersections. Disjoint regions may occur. Assessing these elements establishes the boundary limits for subsequent evaluation and optimization tasks. An ABB IRB 140 robot is used to highlight the methodology.Copyright

Using a Modified Failure Modes and Effects Analysis within the Structured Design Recovery Framework

A structured design recovery framework has been designed to meet the challenges associated with creating a robust engineering model for mechanical components. To assist with the testing and verification phase of the design recovery process, a matrix based modified failure modes and effects analysis (FMEA) has been developed, which targets tolerance variations, in order to diagnose potential problems. The information within the design recovery framework is extracted for the modified FMEA analysis. From the FMEA results, testing strategies are suggested based on the component characteristics. An example illustrates the modified FMEA methodology and highlights its merits.

A comprehensive assessment on the impact of post-processing variables on tensile, compressive and bending characteristics for 3D printed components

Purpose The purpose of this paper is to characterize mechanical properties (tensile, compressive and flexural) for the three-dimensional printing (3DP) process, using various common recommended infiltrate materials and post-processing conditions. Design/methodology/approach A literature review is conducted to assess the information available related to the mechanical properties, as well as the experimental methodologies which have been used when investigating the 3D printing process characteristics. Test samples are designed, and a methodology to measure infiltrate depths is presented. A full factorial experiment is conducted to collect the tensile, compressive and bending forces for a set of infiltrates and build orientations. The impact of the infiltrate type and depth with respect to the observed strength characteristics is evaluated. Findings For most brittle materials, the ultimate compression strength is much larger than the ultimate tensile strength, which is shown in this work. Unique stress–strain curves are generated from the infiltrate and build orientation conditions; however, the compressive strength trends are more consistent in behavior compared to the tensile and flexural results. This comprehensive study shows that infiltrates can significantly improve the mechanical characteristics, but performance degradation can also occur, which occurred with the Epsom salts infiltrates. Research limitations/implications More experimental research needs to be performed to develop predictive models for design and fabrication optimization. The material-infiltrate performance characteristics vary per build orientation; hence, experimental testing should be performed on intermediate angles, and a double angle experiment set should also be conducted. By conducting multiple test scenarios, it is now understood that this base material-infiltrate combination does not react similar to other materials, and any performance characteristics cannot be easily predicted from just one study. Practical implications These results provide a foundation for a process design and post-processing configuration database, and downstream design and optimization models. This research illustrates that there is no “best” solution when considering material costs, processing options, safety issues and strength considerations. This research also shows that specific testing is required for new machine–material–infiltrate combinations to calibrate a performance model. Originality/value There is limited published data with respect to the strength characteristics that can be achieved using the 3DP process. No published data with respect to stress–strain curves are available. This research presents tensile, compressive and flexural strength and strain behaviors for a wide variety of infiltrates, and post-processing conditions. A simple, unique process is presented to measure infiltrate depths. The observed behaviors are non-linear and unpredictable.

Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry

Purpose The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions. Design/methodology/approach Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches. Findings There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN. Originality/value Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

From thought to thing: using the fused deposition modeling and 3D printing processes for undergraduate design projects

ABSTRACTThis paper describes the processes used to integrate two additive manufacturing (AM) processes into various engineering courses, which contain a design element, at the University of Windsor. The 3D printing (3DP) and fused deposition modeling (FDM) AM processes are used to fabricate components. There are unique design and post processing opportunities and issues associated with these processes, which are described in detail. Sophisticated projects can be fabricated and tested by personnel with limited technical skill sets, as the process planning is uncomplicated, and minimal supervision is required while the part is being built. A selection of student projects is presented to highlight the opportunities that can be realized, and to discuss the post processing challenges.

Minimizing voids for a material extrusion-based process

Purpose After experimental testing, it was recognized that a component’s strength relationship with respect to the volume material usage is inconsistent and that failures occurred in regions of voids. The purpose of this study is to present an optimal toolpath for a material extrusion process to minimize voids and discontinuities using standard parameters and settings available for any given machine. Design/methodology/approach To carry out this study, a literature review was performed to understand the influence of the build parameters. Then, an analysis of valid parameter settings to be targeted was performed for a commercial system. Fortus 400 machine build parameters are used for the case studies presented here. Optimal relationships are established based on the geometry and are to be applied on a layer-by-layer or sub-region basis and available machine build options. The component geometry is analyzed and decomposed into build regions. Matlab® is used to determine a standard (available) toolpath parameters with optimal variables (bead height, bead width, raster angle and the airgap) for each layer/build region. Findings It was found that the unwanted voids are decreased by up to 8 per cent with the new model. The final component will contain multiple bead widths and overlap conditions, but all are feasible as the available machine solutions are used to seed the model. Practical implications Unwanted voids can create failure points. Introducing an optimization solution for a maximized material fill strategy using existing build options will reduce the presence of voids and will eliminate “chimneys” or a void present in every layer of the component. This solution can be implemented using existing machine-toolpath solutions. Originality/value This study demonstrates that existing build settings and toolpath strategies can be used to improve the interior fill by performing targeted optimization strategies for the build parameters.