SWARM Optimization of Force Model Parameters in Micromilling

Because of the improvement of machine-tool and tool performances in micro cutting field, the interest on these processes is increasing. Therefore, researchers involved in micro manufacturing processes focused their attention on these types of processes with the aim of improving the knowledge on the phenomena occurring during micro cutting operations. The objective of this work is to develop a modelling procedure for forecasting cutting forces in micromilling considering the tool run-out and the cutting tool geometry. The designed modelling procedure combines information coming from a force model, an optimization strategy and some experimental tests. The implemented force model is based on specific cutting pressure and actual instantaneous chip section. The tool run-out and the cutting tool geometry were considered in the analytical model. The adopted optimization strategy was based on the Particles Swarm strategy due to its suitability in solving analytical non-linear models. The experimental tests consisted in realizing micro slots on a sample made of Ti6Al4V. The comparison between experimental and analytical data demonstrates the good ability of the proposed procedure in correctly defining the model parameters

Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO

End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach

Integrated process planning and scheduling for common prismatic parts in a 5-axis CNC environment

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Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively

Determination of Cost-Effective Range in Surface Finish for Single Pass Turning

Surface finish is considered a critical characteristic for manufacturing components when manufacturers strive to produce components with high-quality characteristics predefined by design engineers. The objective of this research is to provide a cost-effective range in surface finish for single pass turning that enables the design engineers to explore a wider spectrum of alternative solutions without significantly affecting the functionality of the part. Apart from the one optimal solution, the proposed methodology, which is based on Geometric Programming, would provide a range of cutting conditions solutions that satisfy the economic and functional needs for the designer. This can be achieved by switching cost reduction focus from tooling to labor cost, particularly by adjusting variables values such as spindle speed and feed. An algorithm has been developed to find the new variables values. In addition, a sensitivity analysis model, based on metaheuristic techniques, will also be developed to further give a set of possible solutions that are practically preferable to the practitioners. In addition, the developed methodology can be applied to other engineering applications. The proposed methodology will provide a tool that enhances the design for manufacturability for companies to become more competitive

Investigations of machining characteristics in upgraded MQL assisted turning of pure titanium alloy using evolutionary algorithms

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%

Analysis, optimization, FE simulation of micro-cutting processes and integration between Machining and Additive Manufacturing.

La seguente Tesi di Dottorato riguarda i processi di Micro-Machining (MM) applicati su materiali ottenuti per fabbricazione additiva. I processi MM sono un insieme di tecnologie di produzione utilizzate per fabbricare componenti o realizzare features di piccole dimensioni. In generale, i processi di taglio sono caratterizzati da un'interazione meccanica tra un pezzo e un utensile che avviene lungo una determinata traiettoria. Il contatto determina una rottura del materiale lungo un percorso definito, ottenendo diverse forme del pezzo. Più precisamente, la denominazione di microlavorazione indica solo le lavorazioni di taglio eseguite utilizzando un utensile di diametro inferiore a 1 mm. La riduzione della scala dimensionale del processo introduce alcune criticità non presenti negli analoghi processi su scala convenzionale, come l'effetto dimensionale, la formazione di bave, la rapida usura dell'utensile, le forze di taglio superiori alle attese e l'eccentricità del moto dell'utensile. Negli ultimi decenni, diversi ricercatori hanno affrontato problemi relativi alla microlavorazione, ma pochi di loro si sono concentrati sulla lavorabilità dei materiali prodotti per Additive Manufacturing (AM). L’AM è un insieme di processi di fabbricazione strato per strato che possono essere impiegati con successo utilizzando polimeri, ceramica e metalli. L'AM dei metalli si sta rapidamente diffondendo nella produzione industriale trovando applicazioni in diversi rami, come l'industria aerospaziale e biomedica. D’altro canto, la qualità del prodotto finale non è comparabile con gli standard ottenibili mediante i metodi convenzionali di rimozione del materiale. Lo svantaggio principale dei componenti realizzati mediante AM è la bassa qualità della finitura superficiale e l'elevata rugosità; pertanto, sono solitamente necessari ulteriori trattamenti superficiali post-processo per adeguare le superfici del prodotto ai requisiti di integrità superficiale. L'integrazione tra le due tecnologie manifatturiere offre opportunità rilevanti, ma la necessità di ulteriori studi e indagini è evidenziata dalla mancanza di pubblicazioni su questo argomento. Questa ricerca mira ad esplorare diversi problemi connessi alla microlavorazione di leghe metalliche prodotte mediante AM. Le prove sperimentali sono state eseguite utilizzando il centro di lavoro ultrapreciso a 5 assi “KERN Pyramid Nano”, mentre i campioni AM sono stati forniti da aziende e gruppi di ricerca. L'attrezzatura sperimentale è stata predisposta per eseguire la micro-fresatura e per monitorare il processo in linea misurando la forza di taglio. Il comportamento di rimozione del materiale è stato studiato e descritto per mezzo di modelli analitici e simulazioni FEM. I metodi FE sono stati utilizzati anche per eseguire un confronto tra le forze di taglio previste e i carichi sperimentali, con lo scopo finale di affinare la legge di flusso dei materiali lavorati. La ricerca futura sarà focalizzata sulla simulazione FE dell'usura dell'utensile e dell'integrità della superficie del pezzo.This thesis is focused on Micro-Machining (MM) processes applied on Additively Manufactured parts. MM processes are a class of manufacturing technology designed to produce small size components. In general, cutting processes are characterized by a mechanical interaction between a workpiece and a tool. The contact determines a material breakage along a defined path, obtaining different workpiece shapes. More specifically, the micro-machining designation indicates only the cutting processes performed by using a tool with a diameter lower than 1 mm. The reduction of the process scale introduces some critical issues, such as size effect, burr formation, rapid tool wear, higher than expected cutting forces and tool run-out. In the last decades, several researchers have tackled micro-machining related issues, but few of them focused on workability of Additive Manufactured materials. Additive Manufacturing (AM) is a collection of layer-by-layer building processes which can be successfully employed using polymers, ceramics and metals. AM of metals is rapidly spreading throughout the industrial manufacturing finding applications in several branches, such as aerospace and biomedical industries. Moreover, the final product quality is not comparable with the standards achievable through the conventional subtractive material removal methods. The main drawback of additively manufactured components in metals is the low quality of the surface finish and the high surface roughness, therefore further post-process surface treatments are usually required to finish and to refine the surfaces of the build product. The embedding between the two technologies offers relevant opportunities, but the necessity of further studies and investigation is highlighted by the lack of publication about this topic. This research aimed to explore several micro-machining issues with regards to Additive Manufactured metals. Experimental tests were performed by using the ultraprecision 5-axes machining center “KERN Pyramid Nano”, while the AM samples were provided by companies and research groups. The experimental equipment was set-up to perform micro-milling and to monitor the process online by measuring the cutting force. The material removal behavior was investigated and described by means of analytical models and FEM simulations. FE methods were employed also to perform a comparison between the predicted cutting forces and the experimental loads, with the final purpose of refining the flow stress law of the machined materials. The future research will be focused on the FE simulation of the tool wear and the workpiece surface integrity by means of specific subroutines

암석의 기계굴착 성능 예측을 위한 유전자발현프로그래밍과 입자군집최적화에 기초한 혼합형 진화 계산 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 에너지시스템공학부, 2022. 8. Seokwon Jeon.암반 기계 굴착 기술의 발전으로 기존의 발파 공법이 아닌 기계 굴착을 사용하여 지하 공간을 건설하는 사례가 증가하고 있다. 기계식 암석 굴착 분야에는 다양한 변수 간의 관계에 대한 상당한 수의 결정론적 해법이 있지만, 많은 경우 변수 간의 결정적 관계를 설정하는 것은 극히 어렵다. 그 결과 많은 연구자들이 회귀 분석을 사용하여 이러한 관계를 설명하려고 한다. 암석 파쇄 현상의 복잡하고 비선형적인 특성으로 인해 기존의 함수 피팅 기법에서 요구하는 통계 데이터에 부합하는 비선형 함수의 형태를 합리적으로 결정하기가 쉽지 않다. 따라서 본 연구에서는 기계 굴착 분야의 문제점을 해결하기 위해 유전자 발현 프로그래밍(GEP)과 입자 군집 최적화 (PSO)의 조합을 데이터 분석에 사용하였다. GEP 및 PSO는 진화적 계산 기술이며 GEP-PSO 알고리즘을 통해 데이터 세트에 맞는 비선형 함수의 형식과 상수를 자동으로 찾을 수 있다. 본 연구에서는 임팩트 해머에 대한 성능 예측 모델, 픽커터에 필요한 비에너지 예측 모델, 픽커터에 작용하는 절삭력, 수직력, 횡방향력 예측 모델을 개발하기 위해 알고리즘을 사용하였다. 모든 경우에 GEP-PSO 알고리즘을 사용하여 생성된 결과는 다중 선형 회귀에 의해 생성된 결과와 비교하여 상당히 높은 예측 정확도를 생성함을 확인하였다. 가능한 경우 GEP-PSO 알고리즘에 의해 생성된 결과와 다른 연구자가 개발한 예측 모델을 비교하여 현재 연구 과정에서 개발된 모델의 장점을 보여 줄 것으로 보인다. 높은 수준의 정확도 외에도 GEP-PSO 알고리즘을 사용하여 개발된 모델은 기존 예측 모델의 단점을 상당 부분 극복할 수 있다. 개발된 모델은 얻기 쉬운 입력 매개변수를 거의 요구하지 않으면서 더 많은 신뢰성 및 정확도를 제공하거나 기존 예측 모델에서 무시되었던 중요한 입력 매개변수를 포함하므로 더 유리하다고 볼 수 있다.With the advances in mechanical excavation technology, increasing number of underground spaces are built using mechanical excavation rather than the conventional drilling and blasting method. In the field of mechanical rock excavation, there are a fair number of deterministic solutions for the relations between different variables. However, in many cases, establishing such a relation is extremely difficult. As a result, many researchers try to explain those relations using regression analysis. Due to the complex and non-linear nature of rock cutting phenomenon, it is not easy to reasonably determine the form of the non-linear functions that fit to the statistical data as it is required by the conventional non-linear function fitting techniques. As a result, a combination of Gene Expression Programming (GEP) and Particle Swarm Optimization (PSO) was used for data analysis in this study in order to solve problems in the field of mechanical excavation. GEP and PSO are evolutionary computation techniques and the GEP-PSO algorithm is capable of automatically finding the form and constants of a non-linear function that fits on a data set. The algorithm was used in order to develop a performance prediction model for impact hammer, a prediction model for specific energy required by point attack picks, and models for prediction of cutting, normal, and side force acting on a point attack pick. In all cases, the results generated using the GEP-PSO algorithm produced significantly high prediction accuracy in comparison to those generated by multiple linear regression. When possible, comparisons were made between the results generated by the GEP-PSO algorithm and the prediction models developed by other researchers to show the advantages of the models developed over the course of the present study. In addition to high level of accuracy, the models developed using GEP-PSO algorithm could overcome shortcomings of the existing prediction models to a fair extent. The developed models are more advantageous as they provide more reliability/accuracy while requiring few easy-to-obtain input parameters, and/or they include the significant input parameters that have been neglected by the existing prediction models.1. Introduction 1 2. Literature Review 10 2.1 Impact hammer performance prediction 10 2.1.1 Existing performance prediction models 11 2.1.2 Performance prediction model 13 2.2 Specific energy prediction 14 2.2.1 Parameters with a significant impact on specific energy 16 2.2.2 Specific energy prediction model 22 2.3 Forces acting on a point attack pick 22 2.3.1 Existing force prediction models 23 2.3.2 Parameters with a significant impact on forces 29 2.3.3 Forces prediction models 30 3. Statistical Data 31 3.1 Impact hammer performance 31 3.1.1 Levent-Hisarustu tunnel 31 3.1.2 Uskudar-Cekmekoy tunnel 33 3.2 Specific energy required by point attack picks 37 3.3 Forces applied on point attack picks 41 4. Data Analysis Method 43 4.1 Gene Expression Programming (GEP) 45 4.1.1 Genetic Operators 47 4.1.2 The Basic Flowchart of GEP algorithm 55 4.2 Particle Swarm Optimization (PSO) 56 4.3 GEP-PSO algorithm 58 5. Results and Discussion 64 5.1 The suggested impact hammer performance prediction model 65 5.2 The model suggested for prediction of specific energy required by point attack picks 75 5.3 The suggested models for prediction of forces acting on a point attack pick 88 6. Conclusions 97 6.1 Performance prediction model for impact hammer 97 6.2 Prediction model for specific energy required by point attack picks 99 6.3 Models for prediction of cutting, normal, and side force acting on a point attack pick 100 References 102 초 록 116 Appendix A 118 Acknowledgment 138박