Selection Of Cutting Tools Research Articles

Analysis of Polyurethane Foam Machining Parameter Influence: Current Status and Perspective

Nowadays, polyurethane (PU) foam has successfully replaced various engineering materials in many applications due to its many advantages in mechanical and thermal properties. In various applications, PU foam must be formed into various 3-dimensional models according to user requirements by utilizing CAM software and CNC milling machining to manufacture a product. Thus, the selection of cutting parameters in the milling machining process in terms of the nature and character of the material and cutting tools is essential and significantly influences the geometric structure and surface roughness of the PU foam products produced. Based on the review of this article, several machining parameters must be appropriately considered, including spindle rotation speed, depth of cut, cutting tool selection, and feed rate. Vibration will increase as the spindle rotation speed increases, which brings cutting tools but results in better surface quality. Continuous chip formation can be achieved by selecting the proper cut depth and producing low surface roughness values. Selecting suitable cutting tools and geometries that match the material characteristics can reduce the risk of material damage during the machining process, thereby reducing the surface roughness value. Finally, a low cutting rate will minimize surface roughness but increase the risk of cutting-edge wear.

Cutting tool selection for machining metal matrix composites

Cutting tool selection for machining metal matrix composites

Performance of Multilayered Nanocoated Cutting Tools in High-Speed Machining: A Review

In machining processes, cutting tools play a dominant role in producing quality products. The quality of finished goods is directly related to the cutting tool condition. Several types of research have been carried out in cutting tool condition monitoring. On the other hand, the manufacturing industries should be aware of the cutting tool selection, operating conditions, and performance of cutting tools. This article emphasizes the performance of coated cutting tools and tool materials for various machining operations. Nowadays, the nanocoating of CNC tool inserts increases the wear resistance, vibration emissions, metal removal rate, etc. These coating techniques influence the manufacturing industry to increase the productivity and quality of the finished goods and reduce the machining cost. The performance of thin film multilayered coatings such as TiN, TiAlN, AlTiN, Ti, and TiCN on plain silicon carbide tool inserts is revealed by the researchers to guide the manufacturing industry for proper tool selection and standard machining inputs for metal removal operation. The influence of coating material such as TiBN, TiN, TiAlN, and CrAlSiN in cutting tools leads to increase the life time of the cutting tools, which decreases the material sticking and cutting forces. Titanium carbo nitride is wear-resistant and corrosion-resistant. Compared to TiCN, TiAlN is harder due to the higher hardness of 32 GPa. This article concludes the material selection based on the work piece material which yields good metal removal with less cutting forces. The article concludes the cutting material selection based on the work piece for machining operations.

Improving Cutting Tool Selection in Milling Processes Using Early Cost-Base Valuation

Selecting cutting tools for a milling process is crucial to determine the optimal cut. Minimizing milling process-cost is one of the most common optimization objectives, and thus it determines the best cutting tool to be used. However, the chosen cutting tool might not bring the optimal result based on the tool’s cost. Therefore, a valuation method based on the process and cutting-tool costs results were developed and analyzed to improve the cutting tool selection process. A specific rough-milling operation was entered to the quick cost-estimation and optimization application, and several cutting-tools were compared based on the process-cost by each tool. Using a weight-based analysis on both process-cost and tool-cost changes the cutting-tool options' initial rankings. This study showed that using different weight ratios altered the order of the most suitable cutting tools. Another finding revealed in this study is how deflection constraint affected the rank of cutting tool selection. Thus, knowing the proper limit of deflection is crucial to validate the cutting tool selection outcome.

A milling cutting tool selection method for machining features considering energy consumption in the STEP-NC framework

A milling cutting tool selection method for machining features considering energy consumption in the STEP-NC framework

A novel cutting tool selection approach based on a metal cutting process knowledge graph

With the wide and deep application of computer technologies in the field of metal cutting, many manufacturing enterprises have accumulated a large volume of data including workpieces, cutting tools, machine tools, processes, and materials. These data are very valuable resources for enterprises. In this paper, a novel tool selection approach is proposed to deeply mine the relationships hidden in these data. First, an ontology model of the metal cutting process is established with OWL (Web Ontology Language), and a metal cutting process knowledge graph (MCPKG) is constructed based on the ontology model. Then, a data model describing the relationship “structural feature-material-cutting tool” is designed to build a subgraph based on the MCPKG. Moreover, the personalized PageRank (PPR) algorithm is employed with the data model to recommend cutting tools for process planning, and the result of an illustrative example is discussed in detail to verify the algorithm. Finally, a cutting tool selection system with B/S (browser/server) structure based on .NET MVC (model view control) is developed. To recommend cutting tools, the presented approach utilizes the connectivity of the MCPKG to score and rank the usage effect of each tool, which is universally applicable to manufacturing enterprises and provides valuable insights into intelligent cutting tool selection.

Feature-based modelling and artificial intelligence-based computer assisted process planning systems for three axis milling components

Computer-assisted process planning systems help human planners to create better process plans. Feature-based modelling is the current trend in recognising part features. SolidWorks 2018 software is used for CAD modelling and storage of the part manufacturing details in STEP242 file format. This file type stores details such as material, size, stock, dimensional tolerance, and surface finish and interfaces with neural networks to figure out the required machining operation and cutting tool. Throughout this work, different prismatic features, such as a hole, slot, pocket, boss, chamfer, fillet, and face, were considered. A sample prismatic component with nine features was analysed and found to be highly effective. This research concentrates on neural networks-based manufacturing operation selection and cutting tool selection. Levenberg-Marquardt and scaled conjugate gradient neural networks have proven to be more effective in machining operation selection and cutting tool selection respectively. The process parameter selection is done using SolidCAM software followed by NC code generation.

Progress Toward Modeling and Optimization of Sustainable Machining Processes

AbstractThe topic of sustainable machining has in recent times emerged as a significant and impactful area of research focus as it directly deals with environmental health and protection, economic growth and prosperity, and societal wellbeing with greater health and wellness. More specifically, sustainable machining at product, process, and system levels deals with reducing negative environmental impact, offering improved energy and resource efficiency, generating a minimum quantity of wastes, providing operational safety, and offering improved personal health. This paper summarizes recent efforts by the world research community in sustainable machining with a systematic approach for the analysis of machining processes that are broadly classified as sustainable, beginning with dry machining, and then near-dry (also known as minimum quantity lubrication (MQL)) and cryogenic machining processes. The paper also extends its analysis to a hybrid mode of sustainable machining that effectively combines cryogenic and MQL machining processes for improved productivity and machining performance. While a significant part of this paper presents experimental analysis, the progress being made in modeling and optimization has also been discussed in the paper. In particular, major challenges involved in model development for practical implementation, with a view to selecting optimum cutting conditions and cutting tool selection, are primarily discussed in the paper. The need for continued modeling efforts for achieving deployable optimized conditions for sustainable machining is highly recognized, and further research is required in numerous fronts integrating the various convergent disciplines such as materials, mechanics, computational sciences, economics, environmental sciences.

Surgical Drill Bit Design and Thermomechanical Damage in Bone Drilling: A Review.

As drilling generates substantial bone thermomechanical damage due to inappropriate cutting tool selection, researchers have proposed various approaches to mitigate this problem. Among these, improving the drill bit design is one of the most feasible and economical solutions. The theory and applications in drill design have been progressing, and research has been published in various fields. However, pieces of information on drill design are dispersed, and no comprehensive review paper focusing on this topic. Systemizing this information is crucial and, therefore, the impetus of this review. Here, we review not only the state-of-the-art in drill bit designs-advances in surgical drill bit design-but also the influences of each drill bit geometries on bone damage. Also, this work provides future directions for this topic and guidelines for designing an improved surgical drill bit. The information in this paper would be useful as a one-stop document for clinicians, engineers, and researchers who require information related to the tool design in bone drilling surgery.

A Framework of Next Generation Adaptive CNC Controller

Modern manufacturing industries have increasingly demanded to bring comprehensive input data described using high-level languages such as STEP-NC, rather than outdated G&M codes into computer numerical control machine tool levels. In current dynamic shop floor environments, predefined numerical control (NC) command generated in early stages is regularly found unusable or unsuitable for the dedicated resources, causing useless efforts used up in the initial process planning and NC code generation. This research aims to propose a new structure of an adaptive CNC controller by taking the advantages of well-known IEC61499 and STEP-NC standards. For realising adaptive CNC controller capability, integration of the native process planning decision-making function into CNC controller will be established. The activities such as cutting tool selection, machining parameter determination and toolpath generation will be issued automatically by the controller itself that subject to available online machine resources. The generic STEP-NC file is employed as data input and arranged accordingly in the IEC 61499 function block software editor. The system is developed in the JAVA environment by using proposed language.

Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection

Cutting tool selection plays an important role in achieving reliable quality and high productivity work, and for controlling the total cost of manufacturing. However, it is complicated for process planners to choose the optimal cutting tool when faced with the choice of multiple cutting tools, multiple conflict criteria, and uncertain information. This paper presents an effective method for automatically selecting a cutting tool based on the machining feature characteristics. The optimal cutting tool type is first selected using a proposed multicriteria decision-making method with integrated fuzzy analytical hierarchy process (AHP). The inputs of this process are the feature dimensions, workpiece stability, feature quality, specific machining type, and tool access direction, which determine the cutting tool type priority after evaluating many criteria, such as the material removal capacity, tool cost, power requirement, and flexibility. Expert judgments on the criteria or attributes are collected to determine their weights. The cutting tool types are ranked in ascending order by priority. Then, the rule-based method is applied to determine other specific characteristics of the cutting tool. Cutting tool data are collected from world-leading cutting tool manufacturer, Sandvik, among others. An expert system is established, and an example is given to describe the method and its effectiveness.

A Study on Partitioning Freeform Surface and Tool Selection Method for 3-Axes CNC Machining

Freeform surfaces are widely used in various industries. However, they require much time to be machined because of complicated geometry. To increase machining productivity, partitioning methods for freeform surface have been proposed by several previous studies regarding selection of cutting tools and tool-path. This paper proposes a new partitioning method based on Gaussian and mean curvatures to define boundaries of local patches using Freeman algorithm. Simulation results with a B-Spline surface show that the proposed method combined with cutting tool selection strategies can reduce machining time and surface roughness when compared with the non-partitioned method.

Deep learning enabled cutting tool selection for special-shaped machining features of complex products

Each complex product contains many special-shaped machining features required to be machined by the specific customized cutting tools. In this context, we propose a deep learning based cutting tool selection approach, which contributes to make it effective and efficiency for and also improves the intelligence of the process of cutting tool selection for special-shaped machining features of complex products. In this approach, one-to-one correspondence between each special-shaped machining feature and each cutting tool is first analyzed and established. Then, the problem of cutting tool selection could be transformed into a feature recognition problem. To this end, each special-shaped machining feature is represented by its multiple drawing views that contain rich information for differentiating each of these features. With numbers of these views as training set, a deep residual network (ResNet) is trained successfully for feature recognition, where the recognized feature's cutting tool could also be automatically selected based on the one-to-one correspondence. With the learned ResNet, engineers could use an engineering drawing to select cutting tools intelligently. Finally, the proposed approach is applied to the special-shaped machining features of a vortex shell workpiece to demonstrate its feasibility. The presented approach provides a valuable insight into the intelligent cutting tool selection for special-shaped machining features of complex products.

A big data analytics based machining optimisation approach

Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

Automatic Tool Selection System based on STEP_NC and ISO13399 Standards

Computer Aided Process Planning (CAPP) is considered as an essential component of Computer Integrated manufacturing (CIM) environment and it is an important interface between Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM). The purpose of the CAPP is to determine automatically the use of available resources, including machines, cutting inserts, holders, appropriate machining parameters such as cutting speed, feed rate, depth of cut, and generates automatic sequences of operations and instructions to convert a raw material into a required product,with good surface finish.The contribution of this work in CAPP field is the development of an automatic tool selection system based on STEP_NC and ISO 13399 standards for turning and milling processes. The paper presentsthe result of study and analysis of principal system functionalities to be considered. The system consist of four principal modules: a Tool database, feature recognition module, cutting tool selection module and a process optimization module.Finally, based on functional analysis results, the paper present the development of tool database and data tool extraction module from ISO 13399 File using oriented object programing.

A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining

This paper focuses on the development of a multi-axis post-processor engine with a curvature-based feed adaptation module, capable of extracting generic CNC data for high precision machining. The motivation of this work stems from the drawback of standard and commercial post-processors to modify their internal source codes so as to be implemented to newly-developed functions which integrate modern CNC units. The multi-axis post-processor proposed in this work operates as a stand-alone function of an artificial intelligent module that optimizes machining parameters for standard swept cut multi-axis surface tool-paths. The post-processor developed receives APT source files previously been optimized by means of a genetic algorithm that handles cutting tool selection; radial cut engagement; maximum discretization step; lead and tilt angles. The algorithm optimizes the aforementioned machining parameters towards the minimization of the number of cutter locations found in a specific APT source file as well as the surface machining error as a combined effect of chordal deviation and scallop height. The final APT output is then properly handled by the post-processor engine so as to extract the final ISO code for a double-pivoted head 5-axis CNC machine and compute optimal values for feed rate in each NC block considering the interpolation error and curvature analysis given the surface properties. To simulate and verify our proposals, the MAZAK Vortex 1000 gantry-type 5-axis CNC machine tool equipped with a Fanuc 15i CNC unit has been selected as the manufacturing resource corresponding to the final CNC output that the proposed post-processor computes. A benchmark sculptured part is created and used for the virtual material removal simulation in CATIA® V5 R18. For that part, both the proposed post-processor engine and a commercially available post-processor were employed to extract G-code data whilst it was shown that identical outputs were obtained.

An enriched machining feature based approach to cutting tool selection

ABSTRACTCutting tools, considered as a basic prerequisite machining resource, are generally selected according to the selected machining methods, which cannot fit in the current manufacturing environment where small- and medium-sized enterprises (SMEs) are the major manufacturers. For the survival of SMEs, it is critical to develop methods for selecting proper cutting tools and reducing machining cost according to product data. Therefore, this study proposes an enriched machining feature (MF)-based approach towards adaptive cutting tool and machining method selection, in which both machinability and machining cost of MF are considered. It includes a two-step workflow: filtering and optimisation. In the filtering process, cutting tools are filtered according to workpiece materials, geometries of MFs and cutting tool inventory, respectively. Here, MF geometries depend on Machining Limit Value decided by sizes and interference relationships of MFs. Also, the client is suggested to choose proper new cutting tools. In the optimisation process, the filtered cutting tools are considered for all the MFs, and machining costs are calculated for each option, in order to select the cheapest one. In particular, if similar cutting tools are required for different MFs, the cutting tool selection for these MFs should be performed altogether.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Crankshaft Mould CNC Machining Based on CAD/CAM Technique

Taking the CY4105 crankshaft mould as the research target, this paper introduced the CNC machining method based on CAD/CAM technique. At first, structure of the mould was introduced. Then, machining method was introduced, including blank setting method, cutting tool selection, machining method selection and technological parameters setting method. After that, generation method of the mould machining programs was introduced, including tool path and G codes generation. At last, the mould was machined by a vertical machining center, and the results show that the machining accuracy is high.

Parallel Kinematic Assisted Automated Aircraft Assembly

Aerospace enterprises are looking for flexible machines for automating aircraft assembly to revolutionize their existing cumbersome processes in order to lead the competitive edge. Recent study shows Parallel Kinematic Machine (PKM) could be the potential solution as it offers high flexibility, high stiffness and high accuracy that are required for the manufacturing system. This work presents an integrated method for high precision machining of large aero structures with a PKM based manufacturing system, which includes: development of the PKM solution with light weight supporting frame, machine transportation method in a large work area, dust extraction system design, cutting tool selection, and machining sequence design with in-process metrology support. Experiment results show that the new integrated solution is successful, which provides significant impetus for implementing the step change processes of aircraft assembly.