A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing

Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and production cost reduction. Die manufacturing consists first of die design and process planning in order to make a die for extruding the customer's requirement products. The efficiency of die design and process planning are based on the knowledge and experience of die design and die manufacturer experts. This knowledge has been formulated into a computer system called the knowledge-based system. It can be reused to support a new die design and process planning. Such knowledge can be extracted directly from die geometry which is composed of die features. These features are stored in die feature library to be prepared for producing a new die manufacturing. Die geometry is defined according to the characteristics of the profile so we can reuse die features from the previous similar profile design cases. This paper presents the CaseXpert Process Planning System for die manufacturing based on feature based neural network technique. Die manufacturing cases in the case library would be retrieved with searching and learning method by neural network for reusing or revising it to build a die design and process planning when a new case is similar with the previous die manufacturing cases. The results of the system are dies design and machining process. The system has been successfully tested, it has been proved that the system can reduce planning time and respond high consistent plans

AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Optimum Flash-Less Cold Forging Process For AUV Propeller Fabrication With FEM

The selection of the manufacturing process, flash-less cold forging die design and optimization of work-piece are the major issues to reduce the overall cost of the propeller. Substantial investigations have been carried out on this area, by many researchers using various tools and techniques. However, cold forging of complex geometries such as propeller hub and blade is still lacking. Moreover, volumetric analysis and optimization of work-piece have not been reported so far for complex geometries. In this thesis, the cold forging process has been adopted to produce the propeller hub and blade

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Proceedings of the 2021 DigitalFUTURES

This open access book is a compilation of selected papers from 2021 DigitalFUTURES—The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). The work focuses on novel techniques for computational design and robotic fabrication. The contents make valuable contributions to academic researchers, designers, and engineers in the industry. As well, readers encounter new ideas about understanding material intelligence in architecture

Modeling, Simulation and Data Processing for Additive Manufacturing

Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic

Cold Micro Metal Forming

This open access book contains the research report of the Collaborative Research Center “Micro Cold Forming” (SFB 747) of the University of Bremen, Germany. The topical research focus lies on new methods and processes for a mastered mass production of micro parts which are smaller than 1mm (by forming in batch size higher than one million). The target audience primarily comprises research experts and practitioners in production engineering, but the book may also be of interest to graduate students alike

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D