Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki

The important aspect of computer-aided process planning (CAPP) is to recognize part’s surfaces and features to aid downstream intelligent manufacturing. The automatic recognition of surfaces and features will lead to successful attainment of generative CAPP. Feature recognition works performed so far do not recognize all regular form and freeform volumetric features, and do not generate delta volume (DV) for the recognized features. The works do not address the classification of freeform volumetric features. So there is a need for novel classification of features and approach to auto-recognize features so as to auto-generate DV for each recognized feature for the attainment of generative CAPP. An effort has been made to novel classify the features into regular form and freeform features which are further sub-classified into surface features and volumetric features. The overall delta volume (ODV) is classified into SDVF, SDVT, SDVF filled region, SDV-VF, and SDVR. Algorithm is developed to auto-recognize surfaces of a milling part and auto-generate ODV. The algorithm auto-generates exploded view of ODV, auto-labels the sub-delta volumes (SDVs) and determines the level of complexity to manufacture a part. The generated ODV is validated by percentage error (%) and machining of parts. The algorithm selects the type of machining operation to be performed and auto-allocates each SDV-VF to the face it belongs to. The surface and volumetric features of a part are successfully auto-recognized and estimated DV, results table are auto-generated. The SDVT developed contiguous to SDVF for freeform faces, overcomes the complex DV for roughing process. The DV discontinuity and overlap limitation that occurred in few studies are eliminated. The designation of feature faces and colour coding of faces of SDV-VF expresses the type of feature present in a part. The validation of developed algorithm by percentage error (%) shows error less than 0.1% and the machine selection criteria suggests user the type of milling machine needed to manufacture a part based on level of complexity

A multi-perspective dynamic feature concept in adaptive NC machining of complex freeform surfaces

This paper presents a new concept of feature for freeform surface machining that defines the changes in feature status during real manufacturing situations which have not been sufficiently addressed by current international standards and previous research in feature technology. These changes are multi-perspective, including (i) changes in depth-of-cut: the geometry of a feature in the depth-of-cut direction changes during different machining operations such as roughing, semi-finishing and finishing; (ii) changes across the surface: a surface may be divided into different machining regions (effectively sub-features) for the selection of appropriate manufacturing methods for each region such as different cutting tools, parameters, set-ups or machine tools; and (iii) changes in resources or manufacturing capabilities may require the re-planning of depth-of-cuts, division of machining regions and manufacturing operations (machines, tools, set-ups and parameters). Adding the above dynamic information to the part information models in current CAD systems (which only represent the final state of parts) would significantly improve the accuracy, efficiency and timeliness of manufacturing planning and optimisation, especially for the integrated NC machining planning for complex freeform surfaces. A case study in an aircraft manufacturing company will be included in this paper

Development of Feature Recognition Algorithm for Automated Identification of Duplicate Geometries in CAD Models

This research presents a feature recognition algorithm for the automated identification of duplicate geometries in the CAD assembly. The duplicate geometry is one of the seven indicators of the lazy parts mass reduction method. The lazy parts method is a light weight engineering method that is used for analyzing parts with the mass reduction potential. The duplicate geometry is defined as any geometries lying equal to or within the threshold distance with the user-defined orientation between them and have the percentage similarity that is equal to or greater than the threshold value. The feature recognition system developed in this research for the identification of duplicate geometries is also extended to retrieve the weighted bipartite graph of part connections for the assembly time estimation. The weighted bipartite graph is used as input for the part connectivity based assembly time estimation method. The SolidWorks API software development kit is used in this research to develop a feature recognition system in SolidWorks CAD software package using C++ programming language. The feature recognition system built in the SolidWorks CAD software uses a combination of topology and geometric data for the evaluation of duplicate geometry. The measurement of distances between the sampling points strategy is used for the duplicate geometry feature recognition. The feature recognition algorithm has three phases of evaluation: first, is the evaluation for threshold distance condition of parts in the CAD assembly. Second, the part pairs that have satisfied the threshold distance condition are evaluated for the orientation condition. The threshold distance and orientation are the necessary but not the sufficient conditions for duplicate geometries. In the third phase, the geometries that have satisfied orientation condition are evaluated for the percentage similarity condition. The geometries that satisfy the percentage similarity condition are highlighted in order to help designers review the results of the duplicate geometry analysis. The test cases are used to validate the algorithm against the requirements list. The test cases are designed to check the performance of the algorithm for the evaluation of the threshold distance, orientation, and percentage similarity condition. The results indicate that the duplicate geometry algorithm is able to successfully conduct all the three phases of evaluation. The algorithm is independent of the geometric type and is able to analyze planar, cylindrical, conical, spherical, freeform, and toroidal shapes. The number of sampling points generated on the faces of parts for the orientation and percentage similarity evaluation has the significant effect on the analysis time. The worst case complexity of the algorithm is the big O (nC2x m12 x m22x p4), where n = the number of parts in the assembly m1 = the number of faces in the parts that meet the threshold distance condition m2 = the number of faces that meet the orientation condition p = the number of sampling points on the face The duplicate geometry feature recognition approach is used to demonstrate the applicability in the extraction of assembly relations for the part connectivity based assembly time estimation method. The algorithm is also able to extract part connectivity information for the patterns. Further research is required to automate the identification of other laziness indicators in order to make the lazy parts method a completely automated tool. With regards to the complete automation of part connectivity based assembly time estimation method, the duplicate geometry feature recognition system needs integration with the algorithm for the computation of bipartite graph of part connections for the prediction of assembly time

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

NON-CLASSICAL REVERSE ENGINEERING CONCEPT BASED ON MACHINING FEATURE RECOGNITION

This paper is focusing on the fact that technological reconstruction is more important than the obligate geometrical reconstruction. We refer in our study to cases of reconstruction of simple mechanical parts with mainly analytical surfaces. This is because technological reconstruction can provide us with the most important information for/about CAPP (Computer-Aided Process Planning). This information is very helpful in the tasks of a designer in operation planning and also in an operation pass planning level

Development Of Generative Computer-Aided Process Planning System For Lathe Machining

Computer Aided Process Planning (CAPP) is the bridge between computer-aided design (CAD) and computer-aided manufacturing (CAM). CAPP functions as the recognizer of the geometric input from CAD and analyse it into specific function for manufacturing purpose in CAM. These functions always create irregular data descriptions in current CAD and CAM system supply and demand. This study attempts to solve this problem by recognizing the part model’s features via its geometrical based and produce sub-delta volumes that can later be used to generate manufacturing feature-based data for CAM in a single system via generations of algorithm through open source 3D CAD modeller. To map the generated sub-delta volume and respective machining process, part model complexity (PMC) is introduced. Errors of the overall delta volume (ΔODV) were calculated and verification of the proposed PMC is done. Furthermore, to minimize unit production cost, machining parameters including cutting speed (CS), feed rate (f) and depth of cut (d) were optimized for regular form surfaces by using firefly algorithm (FA). These parameters were then useful for tooling selections and tool-path planning. The results from the automatic feature recognitions show less than 0.02% of error in comparison of algorithm overall delta volume, (ODValg) and the manual calculation ODV, (ODVmanual). To validate the generated tool-path, G-codes generated in media package file (MPF) file format and verified through CNC lathe machine. Indeed, the developed algorithm was able to determine the minimum unit production cost of lathe machining part model. Therefore, a single automatic system that able to transfer CAD data into machining readable data through CAM data had been developed

Computer aided process planning for multi-axis CNC machining using feature free polygonal CAD models

This dissertation provides new methods for the general area of Computer Aided Process Planning, often referred to as CAPP. It specifically focuses on 3 challenging problems in the area of multi-axis CNC machining process using feature free polygonal CAD models. The first research problem involves a new method for the rapid machining of Multi-Surface Parts. These types of parts typically have different requirements for each surface, for example, surface finish, accuracy, or functionality. The CAPP algorithms developed for this problem ensure the complete rapid machining of multi surface parts by providing better setup orientations to machine each surface. The second research problem is related to a new method for discrete multi-axis CNC machining of part models using feature free polygonal CAD models. This problem specifically considers a generic 3-axis CNC machining process for which CAPP algorithms are developed. These algorithms allow the rapid machining of a wide variety of parts with higher geometric accuracy by enabling access to visible surfaces through the choice of appropriate machine tool configurations (i.e. number of axes). The third research problem addresses challenges with geometric singularities that can occur when 2D slice models are used in process planning. The conversion from CAD to slice model results in the loss of model surface information, the consequence of which could be suboptimal or incorrect process planning. The algorithms developed here facilitate transfer of complete surface geometry information from CAD to slice models. The work of this dissertation will aid in developing the next generation of CAPP tools and result in lower cost and more accurately machined components

Process planning for the subtractive rapid manufacturing of heterogeneous materials: Applications for automated bone implant manufacturing

This research presents a subtractive rapid manufacturing process for heterogeneous materials, in particular for custom shaped bone implants. Natural bone implants are widely used in the treatment of severe fractures or in tumor removal. In order for the human body to accept the bone implant material and heal properly, it is essential that the bone implant should be both mechanically and biologically compatible. Currently, the challenge of having correctly shaped natural bone implants created from an appropriate material is met through hand-shaping done by a surgeon. CNC-RP is a rapid machining method and software that can realize a fully automated Subtractive Rapid Prototyping (RP) process, using a 3-axis milling machine with a 4th axis indexer for multiple setup orientations. It is capable of creating accurate bone implants from different clinically relevant material including natural bone. However, there are major challenges that need to be overcome in order to implement automated shape machining of natural bones. They are summarized as follows: (1) Unlike homogeneous source materials for which a part can be machined from any arbitrary location within the original stock, for the case of donor bones, the site and orientation of implant harvest need to consider the nature of the heterogeneous internal bony architecture. (2) For the engineered materials, the source machining stock is in the convenient form of geometrically regular shapes such as cylinders or rectangular blocks and the entities of sacrificial supports can connect the part to the remaining stock material. However, irregularly-shaped bones and the heterogeneity of bone make the design of a fixture system for machining much more complicated. In this dissertation, two major areas of research are presented to overcome these challenges and enable automated process planning for a new rapid manufacturing technique for natural bone implants. Firstly, a new method for representing heterogeneous materials using nested STL shells is proposed. The nested shells model is called the Matryoshka mode, based in particular on the density distribution of human bone. The Matryoshka model is generated via an iterative process of thresholding the Hounsfield Unit (HU) data from a computed tomography (CT) scan, thereby delineating regions of progressively increasing bone density. Then a harvesting algorithm is developed to determine a suitable location to generate the bone implant from within the donor bone is presented. In this harvesting algorithm, a density score and similarity score are calculated to evaluate the overall effectiveness of that harvest site. In the second research area, an automated fixturing system is proposed for securing the bone implant during the machining process. The proposed method uses a variant of sacrificial supports (stainless surgical screws) to drill into appropriate locations and orientations through the free-form shaped donor bone, terminating at proper locations inside the solid part model of the implant. This automated fixturing system has been applied to machine several bone implants from surrogate bones to 3D printed Matryoshka models. Finally, the algorithms that are developed for setup planning are implemented in a CAD/CAM software add-on called CNC-RPbio . The results of this research could lead to a clinically relevant rapid machining process for custom shaped bone implants, which could create unique implants at the touch of a button. The implication of such high accuracy implants is that patients could benefit from more accurate reconstructions of trauma sites, with better fixation stability; leading to potentially shorter surgeries, less revisions, shorter recovery times and less likelihood of post-traumatic osteoarthritis, to name a few