Quality Function Deployment (QFD) is a popular planning method often used to transform customer demands/requirements into the technical characteristics of a new or improved product or service. In order to better capture (and represent) the multifarious relationships between customer requirements and technical characteristics, and the relative weights among customer requirements, in this study a hybrid analytic network process (ANP)-weighted fuzzy methodology is proposed. The goal is to synthesize renowned capabilities of ANP and fuzzy logic to better rank technical characteristics of a product (or a service) while implementing QFD. To demonstrate the viability of the proposed methodology a real-world scenario, where a new equipment to squeeze the polyethylene pipes to stop the gas flow without damaging the pipes, is developed. The ranking of technical characteristics of the product is calculated using both crisp and fuzzy weights for illustration and comparison purposes.

Use of ANP weighted crisp and fuzzy QFD for product development

State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing

Off-site construction (OSC) is known as an efficient construction method that could save time and cost, reduce waste of resources, and improve the overall productivity of projects. Coupled with digital technologies associated with the Industry 4.0 concept, OSC can offer a higher rate of productivity and safety. While there is a rich literature focusing on both OSC and Industry 4.0, the implementation of associated digital technologies in the OSC context has not been fully evaluated. This paper intends to evaluate the current literature of digital technology applications in OSC. Scientometric analyses and a systematic review were carried out evaluating fifteen typical digital technologies adopted by OSC projects, including building information modelling (BIM), radio frequency identification devices (RFID), global positioning systems (GPS), the Internet of Things (IoT), geographic information systems (GIS), sensors, augmented reality (AR), virtual reality (VR), photogrammetry, laser scanning, artificial intelligence (AI), 3D printing, robotics, big data, and blockchain. This review formulates a clear picture of the current practice of these digital technologies and summarizes the main area of application and limitations of each technology when utilized in OSC. The review also points out their potential and how they can be better adopted to improve OSC practice in the future.

https://www.mdpi.com/2075-5309/10/11/204/pdf

A Systematic Review of Digital Technology Adoption in Off-Site Construction: Current Status and Future Direction towards Industry 4.0

Computer-aided process planning (CAPP) is the application of the computer to assist process planners in the planning functions. It is considered as the key technology for computer-aided design (CAD) and computer-aided manufacturing (CAM) integration. Nowadays, due to dynamic market and business globalization, CAPP research faces new challenges. In this article, an attempt is made to provide a comprehensive survey on CAPP based on features, knowledge, artificial neural networks, genetic algorithms (GA), fuzzy set theory and fuzzy logic, Petri nets (PN), agent, Internet, standard for the exchange of product data (STEP)-compliant method, and functional blocks (FB) method/technologies for last 12 years (2002–2013). The aim of this paper is to provide an up-to-date survey with graphical representation for easy understanding of the past, present, and future of CAPP. The design of this paper includes a brief introduction of CAPP and its approaches, methods/technologies of CAPP, survey on CAPP, discussion, and conclusion.

Survey on computer-aided process planning

As manufacturing complexity increases, and as factory yields, equipment reliability and equipment utilization each approach 100%, one must look for alternative improvement programs to help reduce manufacturing costs and assist in managing increasing factory complexity. There are a number of possibilities, such as automating routine decision-making processes involved in manufacturing, resulting in faster or more cost-effective decisions, or capturing and applying manufacturing knowledge to reduce the time necessary to detect, analyze and solve manufacturing problems. Either possibility can result in significant savings in productivity improvement or cost reduction, or in avoidance of significant losses. Such strategic programs are in addition to the current automation programs which help manage factory performance, i.e., physical automation to replace human physical activity, and information automation to replace routine human data gathering or data analyses procedures. >

Intelligent manufacturing systems

This paper presents the research work involved in the development of a knowledge based system (KBS) for automated design of deep drawing die for axisymmetric parts. The production rule based KBS approach of artificial intelligence (AI) is utilized for development of the proposed system. The overall system is organized in 27 modules. System modules are coded in AutoLISP language and user interface is created using Visual Basic 6.0 and AutoCAD software. The proposed system is capable to automate all major activities of design of deep drawing die such as manufacturability assessment of deep drawn parts, design of strip-layout, process planning, selection of die components, and modeling of die components and die assembly. The system is user interactive, flexible and has low cost of implementation.

A knowledge based system for automated design of deep drawing die for axisymmetric parts

Design of deep drawing die is a skilled and experienced based activity. Selection of die components is a vital step in the design process of a deep drawing die. This paper describes a knowledge based system (KBS) developed for the selection of major components of a deep drawing die. The proposed system uses production rule based approach of artifi- cial intelligence (AI) and consists of eight modules. Production rules are coded in AutoLISP language and user interface is created in Visual Basic 6 on AutoCAD platform. The system output includes recommendations for selection of proper type and size of various die components of deep drawing die like die block, stripper, die gages, punch plate, die set, fasteners, etc. Validation of the proposed system is demonstrated by taking an industrial sheet metal part. As this system can be im- plemented on a PC having AutoCAD software, therefore its low cost of implementation makes it affordable even for small scale sheet metal industries.

/pdf/a-knowledge-based-system-for-selection-of-components-of-deep-2zsytigsib.pdf

A Knowledge Based System for Selection of Components of Deep Drawing Die

Design of Tracking System for Prefabricated Building Components using RFID Technology and CAD Model

Metal stamping die design is a complex, experiencebased and time-consuming task. Various artificial intelligence (AI) techniques are being used by worldwide researchers for stamping die design to reduce complexity, dependence on human expertise and time taken in design process as well as to improve design efficiency. In this paper a comprehensive review of applications of AI techniques in manufacturability evaluation of sheet metal parts, die design and process planning of metal stamping die is presented. Further the salient features of major research work published in the area of metal stamping are presented in tabular form and scope of future research work is identified. Keywords—Artificial Intelligence, Die design, Manufacturability Evaluation, Metal Stamping Die.

/pdf/ai-applications-to-metal-stamping-die-design-a-review-2pudvs4nh8.pdf

AI Applications to Metal Stamping Die Design– A Review

In this paper, research work involved in the prediction of life of deep drawing die using artificial neural network (ANN) is described. The parameters affecting life of deep drawing die such as size and material of die block and punches are investigated through finite element (FE) analysis and the critical simulation values are determined. Based on FE analysis results, stress amplitude (S) vs. cycles to failure (N) approach is used for prediction of number of cycles of deep drawing die. The number of cycles gives the number of sheet metal parts that can be produced with the deep drawing die before its failure. The ANN model of the proposed system is developed using MATLAB. The data required for ANN are obtained from FE analysis. The generated output data from FE analysis are used to train ANN model. The developed ANN model predicts the life of deep drawing die in terms of number of sheet metal parts. Usefulness of the proposed system is demonstrated through an example of an industrial sheet metal ...

Vishal Naranje

Papers

A knowledge based system for automated design of deep drawing die for axisymmetric parts

A Knowledge Based System for Selection of Components of Deep Drawing Die

Design of Tracking System for Prefabricated Building Components using RFID Technology and CAD Model

AI Applications to Metal Stamping Die Design– A Review

Prediction of life of deep drawing die using artificial neural network