Innovative approach to the design of mechanical parts

Artificial Intelligence is a mix of software engineering, physiology, and logic. Artificial Intelligence (AI)is the zone of software engineering concentrating on making machines that can draw in on practices that people consider smart. It has capacities to make clever machines, has interested people since old circumstances and today with the approach of the PC and 50 years of research into AI programming systems, the fantasy of brilliant machines is turning into a reality. On the other hand Mechanical Engineering is a creative and novel teach that uses the principles of designing, material science, and counterfeit science for the plan, and assess the, industrialized and valuable component of mechanical frameworks. Artificial intelligence, especially intelligent systems, is recently mainstream components in industrial automation. The part of AI in robotization apparatuses like CAD, CAE, CIM and shrewd robots in enhancing the profitability of assembling process has been given due consideration in these years. There are many applications of artificial intelligence in design and manufacturing processes such as, component selection, design, reasoning, learning, perception, sensing, recognition, intuitions, creativity, analysis, abstraction, planning and prediction. Utilization of clever framework is expanding to upgrade quality and creation rate in all assembling sectors.The focal target of mechanical building is to influence new foundation where machine to can reproduce the canny human practices. This paper has given an overview regarding the applications of artificial intelligence in the field of mechanical engineering.

ResearchGate, 2023

This paper aims to conduct a literature review and analyze the current AI techniques used in product design and development within industries in a competitive manner. With a proper implementation process, AI techniques will positively impact organizations to manage the increasing complexity of products and customers' requirements within a short product life cycle. Manufacturers must process and manage complex information efficiently, effectively reducing time-to-market. These requirements have led to the rise in the application of artificial intelligence (AI) technology in product design and development to manage the process. Also, it attracted significant attention to a new design paradigm called AI-enabled product design. Incorporating AI into the Product Development Process (PDP) facilitates the product design process to be more intelligent, accurately interprets vast data, and achieve specific goals and tasks through flexible adaptation. The paper reviews the AI techniques that set the foundations for PDP development. Subsequently, this paper will cover how AI-enabled design has helped in product design and development, such as e.g., extending product life. Furthermore, it can contribute versatile designs to meet a variety of customer demands since AI can process excess data instantly and provide predictions to optimize product design strategies such as matching mechanisms. With the support of AI technology, manufacturers can develop more effective maintenance and recovery by measurement of their products in real-time. Finally, a conclusion about the advantages/limits of the (AI)-enabled design and future research perspectives are discussed in the last section.

International Journal of Design Engineering, 2022

Generative design concept for product design is now evolving in design industries day by day. Many design software developers are now trying to develop software which can generate design solutions using this concept. Companies like Autodesk, Creo, Altair and Siemens have already started providing this functionality in their software products. To showcase the above concept, in this paper we generated multiple novel generative design solutions for mechanical related products on Autodesk Fusion 360 software by performing three design case studies, viz., wall bracket, connecting rod and knuckle joint fork end. The methodology adopted by these software tools to develop multiple novel solutions is presented using flowchart. From these multiple solutions, one optimal design is selected. The static FEA simulation results can be visualised using the simulation user interface provided in the software. For all the three case studies, it is observed that, the stress and global displacement results are found within the critical yield strength values of respective material along with mass customisation.

2007

Intelligent "design for X" systems aim to capture the expertise of an expert in some aspect of manufacture or use of a design, and to make this expertise available to designers to assist in optimising the design in the area of expertise. Two examples of such expert computer aids are discussed in this paper. The first system is related to ergonomic and aesthetic design, while the second system is meant to provide advice and design strategies in product development from plastic materials. Key-Words: knowledge based systems, engineering design, product development, ergonomics, aesthetics, plastics

Engineering with Computers, 1988

The present investigation is aimed toward the development of knowledge-based aids for the design of mechanical systems. We have developed and implemented the knowledgebased aid system, which includes MEET and DPMED. The basic approach of MEET follows along the lines of Design = Refinement + Constraint Propagation. This approach has been proven successful in the circuit design domain. Our attempts to utilize MEET have convinced us that we need to extend this methodology to solve mechanical design problems. The DPMED methodology has been applied to design gear-pairs, v-belts, bearings, and shafts. Rules for selecting materials, critical design criteria, and so on are incorporated as part of the rule-system. In order for DPMED to select the design parameter values within the feasible design space, design criteria need to be investigated. Based on these criteria and input/output specifications, DPMED attempts to perform parameter selections. DPMED uses a general hill-climbing algorithm to guide the search.

International Journal Of Recent Advances in Engineering & Technology

This paper demonstrates a cumulative use of computational intelligence and knowledge engineering to build a process automation tool for closed die forging process. A principle of soft computing has been used to develop computational intelligence with the help of Visual Basic (VB) codes. Mathematical and rule based algorithms has been developed from a relative correlation exercise between Empirical, Statistical, CAE and Experimental test analysis approaches. These algorithms have been used as knowledge base to interact with an inference engine of developed computational intelligence system. This paper also summarizes a case study to validate and review the developed process automation tool in need to get a virtual prototype die model with the prediction of forging process variables and parameters for connecting rod. This will leads to develop an automated, robust, quick, quality, accurate & decision making tool for society in order to achieve the power/cost/time/skill effectiveness even if operated by an unskilled user. This work will be worth full for forging industrial applications in estimating forging load.

2020

Lecture Notes in Computer Science, 2012

This paper presents a new concept of intelligent interactive automated systems for design of machine elements and assemblies on the basis of its features described in a natural language. In the proposed system, computational intelligence methods allow for communication by speech and handwriting, meaning analyses of design engineer's messages, analyses of constructions, encoding and assessments of constructions, CAD system controlling and visualizations. The system uses an intelligent subsystem for assessment of engineer's ability for efficient designing. It is capable of control, supervision and optimization of the designing process. The system consists of spoken natural language and handwriting interfaces between the designing system and design engineers. They are equipped with several adaptive intelligent layers for human biometric identification, recognition of speech and handwriting, recognition of words, analyses and recognition of messages, meaning analyses of messages, and assessments of human reactions. The paper also makes a comparison of the proposed new automated designing system with the present system of realization of designing tasks. In the system also proposed are new concepts of a system of symbolic notation of construction features and language for notation, archiving and processing of construction description data (object oriented language for construction).

2007

Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys computational intelligence concepts and approaches applicable to product design engineering. Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and computational intelligence. Some research issues that arise from the broad perspective presented in the paper have been signaled but not fully pursued. No survey of such a broad field can be complete, however, the material presented in the paper is a summary of state-of-the-art computational intelligence concepts and approaches in product design engineering.

The proposed paper g1ves an overview of the current techniques available for handling non-algorithmic problems by computers. The decision-making process and types of knowledge u sed for making decisions are categorized. An in troduction to different paradigms in the field of Artificial intelligence, including object-oriented programming, Expert Systems, Case Based Reasoning, neuro adaptive systems, fuzzy inference systems and genetic algorithm with respect to their use in engineering design synthesis is presented.

2015

Decisions made at the conceptual design stage have significant influence on factors such as costs, performance, reliability, safety and environmental impact of a product. However, knowledge of all the design requirements and constraints during this early phase of a product’s life cycle is usually imprecise, approximate or unknown. Faced with such complexity, individual designers have restricted themselves to narrow, well-defined sub-tasks and as a result, progress in this area has been patchy and spasmodic. The purpose of this review is to document the current state of research and development in this crucial design activity and in doing so, to identify avenues of fruitful exploration. In this paper, we provide a comparison of the advantages/disadvantages and limitations between the various techniques/tools and, where applicable, suggest possible future research directions.

Journal of Manufacturing Systems, 2003

This paper describes a systematic approach to material and process selection during the embodiment design of mechanical components and a system for generating process and material selection advice. Quite often during the embodiment design stage, design requirements are not precisely defined. Therefore, the system described in this paper accounts for imprecision in design requirements during generation and evaluation of alternative process sequences and material options. To reduce the computational effort, the system uses a depth-first branch-andbound search algorithm. This aids in exploring promising process sequences and material options that can be used to meet the given set of design requirements. Various process sequences and material options are evaluated by using a commercial cost estimation tool.

Research in Engineering Design, 1989

This is the second of a two-part paper summarizing and reviewing research in mechanical engineering design theory and methodology. Part I included 1) descriptive models; 2) prescriptive models; and 3) computer-based models of design processes. Part II includes: 4) languages, representations, and environments for design; 5) analysis in support of design; and 6) design for manufacture and the life cycle. For each area, we discuss the current topics of research and the state of the art, emphasizing recent significant advances. A final section is included that summarizes the six major areas and lists open research issues.

Artificial Intelligence in Engineering, 1999

Design synthesis represents a highly complex task in the field of industrial design. The main difficulty in automating it is the definition of the design and performance spaces, in a way that a computer can generate optimum solutions. Following a different line from the machine learning, and knowledge-based methods that have been proposed, our approach considers design synthesis as an optimization problem. From this outlook, neural networks and genetic algorithms can be used to implement the fitness function and the search method needed to achieve optimum design. The proposed method has been tested in designing a telephone handset. Although the objective of this application is based on esthetic and ergonomic cues (subjective information), the algorithm successfully converges to good solutions. ᭧

An automated design-to-manufacture system and the description of its implementation are outlined. The system is described in the context of rapid prototyping of a mechanism for the post-fabrication of miniature metal tubular components.