Process Parameters Optimization Of Micro Electric Discharge Machining Process Using Genetic Algorithm

Micro Electric Discharge Machining (micro EDM) is a non-traditional machining process which can be used for drilling micro holes in high strength to weight ratio materials like Titanium super alloy. However, the process control parameters of the machine have to be set at an optimal setting in order to achieve the desired responses. This present research study deals with the single and multiobjective optimization of micro EDM process using Genetic Algorithm. Mathematical models using Response Surface Methodology (RSM) is used to correlate the response and the parameters. The desired responses are minimum tool wear rate and minimum overcut while the independent control parameters considered are pulse on time, peak current and flushing pressure. In the multiobjective problem, the responses conflict with each other. This research provides a Pareto optimal set of solution points where each solution is a non dominated solution among the group of predicted solution points thus allowing flexibility in operating the machine while maintaining the standard quality

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Analysis of the Integration of DFM Techniques and Effective Machining Parameter Selection in Metal Parts Manufacturing

This dissertation investigates the minimization of part design with self-locating features. The research focuses primarily on self-fastening characteristics, standardization of parts, and minimal use of fasteners. Further, the present research studies the design for base parts in the construction of a moving joint system, in order to locate potential part and system design improvements. This process may then be extended to industrial applications in the manufacturing industry. Relatively little work to date has examined the significance of Design for Manufacturing Techniques (DFMT), with their inherent machine element systems and machining parameters to investigate which DFMT has the most influence on cost reduction and increasing throughput, and under which circumstances. As such, this dissertation analyzes the inter-operational and synergistic elements of the DFMT, machine element systems, and machining parameters. The parametric specifications for the DFMT are examined and integrated with the cost and productivity-related information. In sum, this research applies DFMT to product design. The trade-off between cost of manufacturing and productivity in terms of DFM alternatives was subject to preliminary model development and sensitivity analysis. For each DFMT and associated machine element systems and Machining parameters, process planning was used effectively with computer-aided tools to enhance the evaluation impact of the dialogue between the design and manufacturing functions. Expert systems and systematic algorithms are inherently incorporated into the software tools used herein. Generative process planning software is used to measure and analyze sensitivity in plan effectiveness, particularly where material property attributes are changed. The shift that occurs according to process plan attributes is explored. These attributes are presented by manufacturing cost and production rate with respect to variations in specific material properties. The research analyzes four DFMT: Modifying the selection of raw material Modifying quality Modifying geometry Modifying the selection of process/es In terms of organizing and evaluating the work, a systematic algorithm was developed, discussed, and tested in this dissertation. This algorithm has sequenced elements to investigate and analyze each DFMT. This analysis identifies several potential process plans, from which the plan with the lowest projected cost and highest production rate is selected and constructed. The developed process plans illustrate the importance of alternative DFMT, without impacting product functionality. Each process plan attempts to decrease production cost, maintain quality, and increase throughput. The results of these plans show their respective effectiveness in relation to part utilization, process, and system-level parameters (such as surface finish, tolerance, heat treated condition of the material, geometry, material hardness, melting point, production quantity, cutting tools, cutting fluids, cutting conditions, and machine tools). The criteria for effectiveness include machining cost, tool cost, and throughput. From this data, the current study determines the most appropriate DFMT and examines underlying alternate machine element systems and machining parameters for each process plan. The effects of DFMT and inherent use of varying machine element systems and machining parameters on cost and productivity-based objectives are also examined. This enables exploration of the selected DFMT choice, according to effective cost reduction and production rate improvement for varying product design. The modified process plan is then compared to the original process plan to highlight areas of improvement. In this comparison, the results of DFMT analysis show significant influence on cost reduction and production rates. These findings suggest that further beneficial outcomes and variety might be obtained by applying this algorithm

Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a way that has compelled users to investigate into their sustainability. For the purposes of this study, cost effectiveness, energy and auxiliary material usage efficiency have been considered to be key indicators of manufacturing process sustainability. The objective of this research study is to develop a set of expert systems which can aid metal manufacturing facilities in selecting Binder Jetting, Direct Metal Laser Sintering or CNC Machining based on viable product, process, system parameters and inherent sustainability aspects. The expert systems have been developed using the knowledge automation software, Exsys CorvidÒ. Comprehensive knowledge bases pertaining to the objectives of each expert system have been created using literature reviews and communications with manufacturing experts. An interactive environment which mimics the expertise of a human expert has been developed by the application of suitable logical rules and backward chaining. The programs have been verified by analyzing and comparing the sustainability impacts of Binder Jetting and CNC Machining during fabrication of a stainless steel 316L component. According to the results of the study, Binder Jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC Machining, for fabrication of components feasible for each technology