Machining Process Planning Research Articles

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

To accommodate the production and manufacture of complex and customized marine components and to avoid the empirical nature of process planning, machining operations can be automatically sequenced and optimized using ant colony algorithms. However, traditional ant colony algorithms exhibit issues in the context of machining process planning. In this study, an improved ant colony algorithm is proposed to address these challenges. The introduction of a tiered distribution of initial pheromones mitigates the blindness of initial searches. By incorporating the number of iterations into the expectation heuristic function and introducing a ‘reward–penalty system’ for pheromones, the contradictions between convergence speed and the tendency to fall into local optima are avoided. Applying the improved ant colony algorithm to the process planning of large container ship propeller shaft machining, this study constructs a ‘distance’ model for each machining unit and develops a process constraint table. The results show significant improvements in initial search capabilities and convergence speed with the improved ant colony algorithm while also resolving the contradiction between convergence speed and optimal solutions. This verifies the feasibility and effectiveness of the improved ant colony algorithm in intelligent process planning for ships.

Совершенствование принципов автоматизированного планирования многономенклатурных технологических процессов механообработки

The scientific principles of improving the system of automated planning of technical processes in the conditions of multiproduct machining industries are viewed. The study of modern approaches to automation of design and implementation of machining processes taking into account the production situation, is carried out. The use of the system of automated planning of multiproduct machining processes is justified, because its basic procedure includes a possibility of real-time interaction between subsystems design and implementation of technical processes, where the key place is occupied by a database on the technological capabilities of equipment and tooling. The interrelation of the system of automated planning of multiproduct machining processes with subsystems for assessing production manufacturability, monitoring technological equipment state, control and measuring procedures and assembly, is shown. The principle of designing multiproduct machining processes, generated for a given range of parts, taking into account the condition and capabilities of the production system, is presented. It is proposed to use the value of the total manufacturing time of a given range of parts as a generalized criterion for the efficiency of the production system. Ranking technique of design procedures according to the degree of influence of the decisions made on the generalized criterion of efficiency of the production system, is described. A model, based on genetic algorithms, is characterized, allowing the system being automatically adjusted to the conditions, changing during the design process. As a result, the efficiency of technological preparation of diversified production increases due to the rational distribution of manufacturing process for machining operation of parts to existing production facilities. The development of the work is aimed at increasing the level of design automation of machining processes and obtaining feedback on the current state of the production system.

An effective process design intent inference method of process data via integrating deep learning and grammar parsing

With the constantly generation of process data in industries, process knowledge reuse and learning from process dataset has shown promising potential in machining process planning. However, existing methods cannot effectively extract different granularity process intents, and thus the process data cannot be effectively utilized. In this paper, an effective process design intent inference method of process data via integrating deep learning and grammar parsing is proposed. First, based on the projection of cutter location segments, an association approach between machining operations and machining features is proposed to extract the process intent of each machining operation. Then, a deep learning based working step process intent sequence decision-making model is proposed to estimate the probability distributions of alternative working step process intents at each time. Moreover, according to the working step process intent sequences of existing parts in an industry, the process knowledge And-Or graph (PK-AOG) is constructed by introducing the probabilistic grammar graph model. Finally, the optimal process intents of each working step are jointly obtained based on grammar parsing by taking the PK-AOG as a guide. In the experiments, the effectiveness of our approach is validated by developing a prototype system, and the results show that our approach is practical in process intents extraction.

Data-Driven and Knowledge-Guided Approach for NC Machining Process Planning

With the wide applications of computer-aided technologies in industries, plenty of process data composed of 3D CAD models and CAM models are increasingly generated, which are the direct and effective carriers of knowledge, intelligence, and experience of skilled designers. However, existing methods cannot effectively learn and mine the implicit and explicit knowledge embedded in process data, and the process scheme generated is difficult to satisfy the manufacturing logics of a part in an industry. In this paper, a data-driven and knowledge-guided approach for NC machining process planning is proposed. First, a multi-level structured NC machining process model based on sub-machining regions is introduced to represent the relations between the 3D CAD model and the CAM model in process data. Then, an attention-based working step sequence generation model is proposed to predict the candidate working step and its associated sub-machining regions. Moreover, a context-free grammar describing the semantic and temporal relationships between working steps is extracted to construct a process knowledge And-Or graph with hierarchy and compositionality (PK-AOG). Finally, guided by PK-AOG, an NC machining process scheme generation method based on grammar parser is proposed to find a joint optimal solution according to the probabilities of the candidate working steps iteratively. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed method.

Optimal Manufacturing Configuration Selection: Sequential Decision Making and Optimization using Reinforcement Learning

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations.

Automatic machining feature recognition based on MBD and process semantics

Automatic feature recognition (AFR) bridges CAD and CAPP systems. Existing AFR methods mainly consider feature geometries, and the recognized machining features have limited process attributes for process planning. With the model-based definition (MBD) becoming more advanced in the industry, AFR methods need to be improved and upgraded. This manuscript investigates a systematic AFR method for machining parts that converts the part MBD model to a structuralized feature model (SFM) based on process semantics. At its core, process semantics is the abstraction of machining objects and methods, which represents the machining requirements of machining position, machining type, machining accuracy and other process elements. Distinguished from existing AFR methods, the method in this study employs a part MBD model as the data source and provides a new definition of machining features from a process semantic perspective. Detailed data extraction and preprocessing methods of the MBD model are furnished. To identify feature geometries, a new classification methodology of machining features for defining the stable topology structure (SAF) and semi-stable topology structure (SSF) features is presented. An improved center-subgraph method is further proposed to incorporate hint-based and rule-based methods to enhance the performance of the traditional graph method. Furthermore, the generalized GAAG has been presented with an implementation method to address the completeness of graph representation and to reduce the computational effort of graph search. In addition, to extend the feature domain, the recognition methods for high-level composite features are discussed in detail. The feature attributes were solved based on the achieved feature subgraph, and the extraction methods were detailed according to process planning requirements. The feature recognition results can be directly applied for downstream machining process planning.

Combining deep learning with knowledge graph for macro process planning

The macro process, as the core process content of the whole machining process for a design part, serves as a guide for the complete machining process planning. It determines the final machining quality and machining cost of the part to a large extent. However, due to the various bottlenecks in machining process knowledge representation, matching, and inference, macro process planning still depends heavily on the knowledge and experience of process designers. In this paper, a macro process decision-making approach combining knowledge graph and deep learning technology is proposed. Firstly, based on deep learning, the macro process reasoning function is learned to model the mapping relationship between the process elements of the design part and macro process analysis rules. Then the process elements of the design part are fed into the reasoning function to activate the applicable process analysis rules of the process knowledge graph. Next, according to the association relationship between the activated process analysis rules and the predefined machining methods of the macro process graph, a series of working steps nodes and the directed edges are activated, which constitutes the feasible solution space for the macro process. Finally, the swarm intelligence algorithm is applied to search for an effective and low-cost macro process scheme from the feasible macro process solution space. In experimental studies, the slot cavity parts are taken as examples to verify the feasibility and effectiveness of the proposed approach.

Process design and optimization Of Two-Stage Axial Micro Turbine Air motor

Based on the general machining center, a high-efficiency, mass-produced micro-turbine processing technology and a process scheme for improving metal removal rate are proposed to improve and optimize the metal removal rate of micro-machining. Taking the two-stage axial micro turbine air motor as the research object, the machining route of clamping the nozzle rotor is developed, and the metal removal rates of the turbine with the expandable surface when using the rounded end mill and the ball end mill are compared by the single factor analysis method. In the three processes of rough milling blade passage, finish milling blade and finish milling wheel hub, the metal removal rate of the rounded end mill is 2.96 times, 10 times and 2 times higher than the ball end mill, respectively. The Swarf finishing strategy of Powermill software is used to processing the side and bottom of the blade both roughly and accurately, and the suitable cutting parameters and side edge machining parameters are obtained. The surface roughness Ra of the side and bottom surfaces of the blade is tested to be 0.4-0.8μm using a surface roughness measuring instrument SJ-410. With the trigger type probe of the model Omp60, the dimensional tolerance of the blade is within 0.015 mm, and the coaxiality is from 0.002 to 0.003mm. The two-stage axial-type micro turbine air motor is assembled by the hot-packing method, and the torque and rotor speed are tested by a special platform. The results show that when the compressed air inlet pressure is 300KPa, the designed maximum speed of 100000rpm is satisfied, which meets the design requirement of the turbine motor and verifies that the numerical control machining process plan is feasible. Besides, the side edge machining of the blade using the cylindrical milling cutter is efficient to improve the metal removal rate. It also lays a foundation for the mass production of the two-stage axial-type micro turbine air motor.

Based on PowerMill and K2X8 Five Axis CNC Machine of Realization of Integral Impeller Machining

Based on the powermill2019 software platform, taking the integral shunt impeller as an example, this paper analyzes the overall structure characteristics and machining difficulties of the integral shunt impeller, reasonably formulates the NC machining process plan, and uses the powermill2019 impeller machining module to plan the tool path, so as to generate the tool path file, and verifies the correctness of the tool path by simulation machining. Through the special post processor of powermill2019 five axis machining center, the NC program of integral shunt impeller is generated and uploaded to k2x8 five axis CNCmachining center to complete the actual processing of integral shunt impeller. Through the inspection of impeller, the results show that the impeller fully meets the design and use requirements.

Non-productive time optimization for 5-axis EDM drilling using HVNTS algorithm

This paper presents a hybrid variable neighbourhood search/tabu search (HVNTS) method and a neighbourhood generation strategy called pairwise inter-reshuffle (PIR) for process planning of 5-axis electrical discharge machining (EDM) drilling processes, which aims to minimize the total non-productive time of the machining process, including tool travelling time, tool switching time and Z-axis compensation moving time. To obtain a mathematical model of the non-productive time, a kinematic transformation and a Chebyshev distance function are then utilized. To solve the mathematical model efficiently, an HVNTS algorithm is applied as the model has a large number of 0–1variables. To improve the solution quality, the PIR method and a dynamic neighbourhood strategy are applied simultaneously and verified. The obtained simulation results demonstrate that the proposed basic and the improved HVNTS have a significant contribution in optimizing the non-productive time of the 5-axis EDM drilling process effectively.

A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.

A multi-objective decision-making method for machining process plan and an application

The processing parameters/plan decision of the machine tool can effectively realize the coordinated optimization of production efficiency, processing quality, resource consumption and environmental emissions. However, the current research has problems such as single decision-making objectives, high degree of subjective decision-making, and large error in decision-making results. Against this backdrop, our research proposes a multi-objective decision-making method for a machining process plan. Firstly, the resource consumption and environmental emissions of machining process are analyzed based on an input-process-output (IPO) model. Then, in view of the complex interplay between traditional indicators and environmental impact indicators, we propose a combination weight determined by an analytic hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) method, which considers subjective weight and objective weight. Thirdly, we set positive and negative reference sequences using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, and the gray correlation and Euclidean distance are used for sorting each experimental group (processing parameters). Finally, an experimental case study is performed on a spindle sleeve made of GCr15, the machining type include turning, grinding, and drilling. The optimal processing parameters are determined to achieve coordinated optimization of resource consumption, processing quality, and production efficiency.

Multi-criteria decision for machining process plan evaluation using fuzzy logic modeling and feature based method

Machining is mostly used to transform raw material into a finished part before assembly to become benefit products and machinery. CAD/CAM is used to assist an engineer to create a part and tool part together with an NC code for a CNC machine. Process planning plays a key role in machining efficiency and optimization. This paper purposes a multi-criteria decision for machining process plan evaluation using fuzzy logic modeling and feature-based design representation with the condition of time, cost, tool life, precision, and surface. The results showed the alternative of the best machining process plan.

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

Process evaluation is widely accepted as an effective strategy to improve product quality and shorten its development cycle. However, there has been very little research on how to evaluate the process plan with the dynamic change of the machining condition and uncertain available manufacturing resources. This paper proposes a novel process evaluation method based on digital twin technology. Three core technologies embodied in the proposed method are illustrated in details: 1) real-time mapping mechanism between the collected data in machining and the process design information; 2) construction of the digital twin-based machining process evaluation (DT-MPPE) framework; and 3) process evaluation driven by digital twin data. To elaborate on how to apply the proposed method to the reality, we present a detailed implementation process of the proposed DT-MPPE method for the key parts of the marine diesel engine. Meanwhile, the future work to completely fulfill digital twin-based smart process planning for complex products is discussed.

Shape distribution-based approach to comparing 3D CAD assembly models

As a key technology for three-dimensional (3D) model retrieval from model databases, the ability to perform comparisons of 3D models is important. In particular, during the product design phase, 3D CAD models should be robustly retrieved for design reuse or machining process planning. However, typical retrieval methods that are based on model names, codes, or annotations are limited in terms of their usability and robustness. Therefore, there are increasing requirements for shape-based 3D model retrieval techniques, as well as the ability to perform 3D model comparisons. In this paper, we propose a shape distribution-based 3D CAD assembly comparison method that has the ability to identify dissimilarities in 3D CAD assembly models by differentiating between assembly relationships and part-shape dissimilarities. This is different from existing methods, which are limited to only shape dissimilarities. We present experimental results for various test cases by comparing our proposed method and existing methods. Based on our experiment results, we found that our method can enable a comprehensive comparison of 3D CAD assembly models including assembly relationship dissimilarities, part-shape dissimilarities, and overall model dissimilarities.

Reconfigurable machining process planning for part variety in new manufacturing paradigms: Definitions, models and framework

Abstract Conventional machining process planning approaches are inefficient to handle the process planning complexity induced by part variety. Reconfigurable process planning is a new process planning approach which has been well recognized as a key enabler for current manufacturing paradigms. However, in the literature, there is neither a comprehensive part variety representation model to support reconfigurable process planning nor a global solution framework to instruct the generation of the feasible process plans for a specific part variant. Therefore, this paper extends the concept of reconfigurable process planning to a concept of reconfigurable machining process planning which targets the process plan generation for a part family. A solution framework is developed for reconfigurable machining process planning. In this framework, a feature-based part variety model is proposed to represent a part family; A reconfigurable machining process plan is defined as a set of modular components which can be configured/reconfigured into the machining process plans for any part variant in the family; a novel configuration approach is proposed to generate the process plan components for a specific part variant while configuring this part variant from the family. The feasibility and effectiveness of the proposed framework and models are tested in a real case study.

Intelligent Adaptation of Axisymmetric Solid Parts in Machining Process Planning by Case Based Reasoning

Intelligent Adaptation of Axisymmetric Solid Parts in Machining Process Planning by Case Based Reasoning

Off-line nominal path generation of 6-DoF robotic manipulator for edge finishing and inspection processes

This paper deals with the development of a Computer-Aided Robotic Machining Process Planning package based on SolidWorks. The main aim of the package is to generate an efficient, collision-free, nominal tool path needed for edge finishing and inspection by utilizing a 6-DoF robotic arm. The overall nominal tool path involves analyzing the geometry of the workpiece, determining and designing an efficient collision-free tool path and finally generating the tool path data for the robot and verifying it. This package significantly simplifies the complexity of planning an efficient path for performing edge finishing and inspection. ABB IRB2000 robot is chosen for executing the generated tool path. Ultimately, such path is to be utilized as the nominal tool path by any control strategy present in the literature for a complete automatic edge finishing process.

A novel approach for in-situ detection of machining defects

Integrating inspection procedures in the machining process contributes to process optimization. The use of in situ measurement allows a better reactivity for corrective actions. However, to be highly efficient, Machining and Inspection Process Planning must reach a high level of integration. It is here essential to focus on the compromise measurement time vs. precision: the time dedicated to inspection must be limited, but not to the detriment of measurement quality. A measurement process for in situ machining defect detection is proposed based on a stereo-DIC. The proposed method allows the direct comparison between images of the machined part and the CAD model by means of a CAD model-based calibration method which links the camera frame to the CAM frame. Therefore, starting from a meshed-model representation of the part, local regions of interest are defined corresponding to the projection of each facet onto the two images. An optimization of each facet configuration is then performed in order to minimize a cross-correlation coefficient, and the obtained facet displacement is used to detect machining defects. The robustness of the method is assessed through an illustration of measurement in the machine-tool environment.

Numerical prediction of distortions during machining of large aluminium aeronautical parts

Aluminium alloy structural aerospace parts are usually large monolithic parts machined from rolled plates or preformed parts. Previous to machining, several manufacturing steps (forming, heat treatments and mechanical stress relief operations) are performed, resulting in the creation of residual stresses. During machining, a redistribution of the residual stresses occurs due to the material removal. This residual stress redistribution is the main reason for machining errors such as dimensional variations and post‐machining distortions. In order to predict the mechanical behaviour of the workpiece during machining and the final part quality in taking into consideration both the machining parameters (fixture layout and machining sequence) and the residual stress redistribution, a specific numerical tool has been developed [1]. Using this numerical tool, an analysis of the influence of the initial residual stresses as well as of the fixture layout and the machining sequences (tool path) on the machining quality of parts machined from an AIRWARE®2050‐T84 alloy rolled plate is performed. The numerical results are compared with experimental ones in terms of both post‐machining distortions and dimensional errors. Similar results are obtained for both, showing that the developed numerical tool allows to predict dimensional and geometrical errors due to the redistribution of the residual stresses during machining. The machining of these cases and results obtained are then analysed, demonstrating the feasibility to adapt and to optimize the machining process plan to ensure conformity of the part with the tolerance specifications.