Optimization of 5-Axis milling processes based on the process models with application to airfoil machining

5-axis milling is widely used in machining of complex surfaces such as airfoils. Improper selection of machining parameters may cause low productivity and undesired results during machining. There are several constraints such as available power and torque, chatter stability, tool breakage etc. In order to respect such constraints proper machining parameters should be determined. In this paper, methodologies for improving 5-axis milling processes are presented. Selection of machining parameters is performed using process simulations. The developed methodologies are presented on an example airfoil

Least Squares Fitting of Analytic Primitives on a GPU

Metrology systems take coordinate information directly from the surface of a manufactured part and generate millions of (X, Y, Z) data points. The inspection process often involves fitting analytic primitives such as sphere, cone, torus, cylinder and plane to these points which represent an object with the corresponding shape. Typically, a least squares fit of the parameters of the shape to the point set is performed. The least squares fit attempts to minimize the sum of the squares of the distances between the points and the primitive. The objective function however, cannot be solved in the closed form and numerical minimization techniques are required to obtain the solution. These techniques as applied to primitive fitting entail iteratively solving large systems of linear equations generally involving large floating point numbers until the solution has converged. The current problem in-process metrology faces is the large computational times for the analysis of these millions of streaming data points. This research addresses the bottleneck using the Graphical Processing Unit (GPU), primarily developed by the computer gaming industry, to optimize operations. The explosive growth in the programming capabilities and raw processing power of Graphical Processing Units has opened up new avenues for their use in non-graphic applications. The combination of large stream of data and the need for 3D vector operations make the primitive shape fit algorithms excellent candidates for processing via a GPU. The work presented in this research investigates the use of the parallel processing capabilities of the GPU in expediting specific computations involved in the fitting procedure. The least squares fit algorithms for the circle, sphere, cylinder, plane, cone and torus have been implemented on the GPU using NVIDIA\u27s Compute Unified Device Architecture (CUDA). The implementations are benchmarked against those on a CPU which are carried out using C++. The Gauss Newton minimization algorithm is used to obtain the best fit parameters for each of the aforementioned primitives. The computation times for the two implementations are compared. It is demonstrated that the GPU is about 3-4 times faster than the CPU for a relatively simple geometry such as the circle while the factor scales to about 14 for a torus which is more complex

Automated Digital Machining for Parallel Processors

When a process engineer creates a tool path a number of fixed decisions are made that inevitably produce sub-optimal results. This is because it is impossible to process all of the tradeoffs before generating the tool path. The research presents a methodology to support a process engineers attempt to generate optimal tool paths by performing automated digital machining and analysis. This methodology automatically generates and evaluates tool paths based on parallel processing of digital part models and generalized cutting geometry. Digital part models are created by voxelizing STL files and the resulting digital part surfaces are obtained based on casting rays into the part model. Tool paths are generated based on a general path template and updated based on generalized tool geometry and part surface information. The material removed by the generalized cutter as it follows the path is used to obtain path metrics. The paths are evaluated based on the path metrics of material removal rate, machining time, and amount of scallop. This methodology is a parallel processing accelerated framework suitable for generating tool paths in parallel enabling the process engineer to rank and select the best tool path for the job

Automated CNC Tool Path Planning and Machining Simulation on Highly Parallel Computing Architectures

This work has created a completely new geometry representation for the CAD/CAM area that was initially designed for highly parallel scalable environment. A methodology was also created for designing highly parallel and scalable algorithms that can use the developed geometry representation. The approach used in this work is to move parallel algorithm design complexity from an algorithm level to a data representation level. As a result the developed methodology allows an easy algorithm design without worrying too much about the underlying hardware. However, the developed algorithms are still highly parallel because the underlying geometry model is highly parallel. For validation purposes, the developed methodology and geometry representation were used for designing CNC machine simulation and tool path planning algorithms. Then these algorithms were implemented and tested on a multi-GPU system. Performance evaluation of developed algorithms has shown great parallelizability and scalability; and that main algorithm properties are required for modern highly parallel environment. It was also proved that GPUs are capable of performing work an order of magnitude faster than traditional central processors. The last part of the work demonstrates how high performance that comes with highly parallel hardware can be used for development of a next level of automated CNC tool path planning systems. As a proof of concept, a fully automated tool path planning system capable of generating valid G-code programs for 5-axis CNC milling machines was developed. For validation purposes, the developed system was used for generating tool paths for some parts and results were used for machining simulation and experimental machining. Experimental results have proved from one side that the developed system works. And from another side, that highly parallel hardware brings computational resources for algorithms that were not even considered before due to computational requirements, but can provide the next level of automation for modern manufacturing systems

Surface Partitioning for 3+2-axis Machining

Despite the inbuilt advantages offered by 5-axis machining, the manufacturing industry has not widely adopted this technology due to the high cost of machines and insufficient support from CAD/CAM systems. Companies are used to 3-axis machining and the operators are in many cases not yet ready for 5-axis machining in terms of training and programming. An effective solution for this 5 axis problem is a graduated migration through the use of 3+2-axis machining. The objective of this research is to develop and implement a machining technique that uses the simplicity of 3-axis tool positioning and the flexibility of 5-axis tool orientation, to machine complex surfaces. This technique, 3+2-axis machining, divides a surface into patches and then machines each patch using a fixed tool orientation. The tool orientation and section boundaries are determined to minimize the overall machining time. For each section the tool orientation is different but remains constant while machining this section. The number of patches selected for machining has a direct impact on the machining time. If the number of patches is small, the shape of the tool may vary greatly from that of the surface, which can result in smaller side-step distances. In contrast, a large number of patches leads to a better match between the tool and the workpiece, but it also leads to many re-orientations of the part as the tool moves between patches. Also, if the number of patches is large, the size of the patches will be reduced which will result in shorter tool passes that limit the tools ability to achieve the commanded feed rate. The optimum number of patches is a compromise between increasing the side step associated with large patches and the increase in time due to re-orientation of part and tool movement between patches. To find the optimal partition, a series of simulation tests are conducted to find the partition that would lead to the smallest machining time. This work presents the application of well known methods from Pattern Recognition and newly developed methods by the current author that were adapted for surface machining and boundary identification. This work also presents the methodology required to generate tool paths for 3+2-axis machining, which includes an explanation of the procedures required to determine an appropriate tool orientation, feed direction, tool path trajectory and tool parameters for patch-by-patch machining. These parameters are determined independently for each patch and aim at reducing the time required to machine a surface while maintaining the surface specifications. This work presents the surface partitioning scheme and the method of selecting optimum number of partitions along with actual machining experiments. Machining tests on four different surfaces were conducted to demonstrate the efficiency of the proposed technique. The results show that 3+2-axis machine reduced machining times over 3-axis ball nose machining and 5-axis machining using the “Sturz” method. Also, since the tool axis remains fixed during cutting, the tool offers constant feed rates and a better surface finish compared to simultaneous 5-axis

Mechanics and dynamics of multi-axis machining operations

Machining process with a single cutting tool is called multi-axis machining if more than 3-axis are involved in the operation. On the other hand, parallel machining processes where more than one cutting tool simultaneously cut a workpiece is also called multi-axis machining. 5-axis ball-end milling where a single cutting tool is employed, parallel turning and parallel milling processes with two cutting tools are in the scope of the thesis. Moreover, face-milling process with inserted tools is also modeled. 5-axis ball-end milling operations are common in several industries such as aerospace, automotive and die/mold for machining of complex sculptured surfaces. Additional two degree of freedoms, namely, lead and tilt angles make it possible to machine complex parts by providing extra flexibility in cutting tool orientation. However, they also complicate the geometry of the process. In these operations, productivity, dimensional tolerance integrity and surface quality are of utmost importance. Part and tool deflections under high cutting forces may result in unacceptable part quality, whereas using conservative cutting parameters results in decreased material removal rate. Process models can be used to determine the proper or optimal milling parameters for required quality with higher productivity. The majority of the existing milling models are for 3-axis operations, even the ones for ball-end mills. In the thesis, geometry, force and stability models are presented for 5-axis ball-end milling operations. The effect of lead and tilt angles on the process geometry, cutter and workpiece engagement limits, scallop height, and milling forces are analyzed in detail. In addition, tool deflections/form errors and stability limits are also formulated for 5-axis ball-end milling. The use of the model for selection of the process parameters such as lead and tilt angles which result in minimum cutting forces or maximum stability limits are demonstrated. The model predictions for cutting forces, form error and stability limits are compared and verified by experimental results. Parallel machining operations are advantageous in terms of productivity since there are more than one cutting tools in operation. Due to the increased number of cutting tools, they have the potential for considerable increase in productivity as a result of higher material removal rate (MRR). However, the dynamic interaction between these parallel tools may create additional stability problems and the advantage of parallel machining may not be utilized to full extent. For that reason, dynamics and stability of parallel machining processes need to be modeled. In the thesis, dynamics of parallel turning and parallel milling operations where two cutting tools cut a common workpiece are modeled. The predicted stability limits for parallel turning are also compared with experimental results where good agreement is demonstrated. Die manufacturing is a very critical part of the overall production chain in many industries. Depending on shape and size of a die, machining time can be very time consuming. Furthermore, since usually one die is manufactured, the chance for testing is very limited. Machining processes in die manufacturing can be limited by many factors. Process models can be used in order to select process conditions which will yield the required quality in the shortest possible time. In this study, force and chatter models are developed for face milling processes with inserted cutters. Using the developed models, process parameters are modified and their effects on productivity are demonstrated

A virus-evolutionary, multi-objective intelligent tool path optimisation methodology for sculptured surface CNC machining

Today’s production environment faces multiple challenges involving fast adaptation to modern technologies, flexibility in accommodating them to current industrial practices and cost reduction through automating repetitive tasks. At the same time the requirements for manufacturing functional, aesthetic and versatile products, turn these challenges to clear and present industrial problems that need to be solved by delivering at least semi-optimal results. Even though sculptured surfaces can meet such requirements when it comes to product design, a critical problem exists in terms of their machining operations owing to their arbitrary nature and complex geometrical features as opposed to prismatic surfaces. Current approaches for generating tool paths in computer-aided manufacturing (CAM) systems are still based on human intervention as well as trial-and-error experiments. These approaches neither can provide optimal tool paths nor can they establish a generic approach for an advantageous and profitable sculptured surface machining (SSM). Major goal of this PhD thesis is the development of an intelligent, automated and generic methodology for generating optimal 5-axis CNC tool paths to machine complex sculptured surfaces. The methodology considers the tool path parameters “cutting tool”, “stepover”, “lead angle”, “tilt angle” and “maximum discretisation step” as the independent variables for optimisation whilst the mean machining error, its mean distribution on the sculptured surface and the minimum number of tool positions are the crucial optimisation criteria formulating the generalized multi-objective sculptured surface CNC machining optimisation problem. The methodology is a two-fold programming framework comprising a virus-evolutionary genetic algorithm as the methodology’s intelligent part for performing the multi-objective optimisation and an automation function for driving the algorithm through its argument-passing elements directly related to CAM software, i.e., tool path computation utilities, objects for programmatically retrieving tool path parameters’ inputs, etc. These two modules (the intelligent algorithm and the automation function) interact and exchange information as needed towards the achievement of creating globally optimal tool paths for any sculptured surface. The methodology has been validated through simulation experiments and actual machining operations conducted to benchmark sculptured surfaces and corresponding results have been compared to those available from already existing tool path generation/optimisation approaches in the literature. The results have proven the methodology’s practical merits as well as its effectiveness for maintaining quality and productivity in sculptured surface 5-axis CNC machining