A Survey of Automated Process Planning Approaches in Machining

Global industrial trend is shifting towards next industrial revolution Industry 4.0. It is becoming increasingly important for modern manufacturing industries to develop a Computer Integrated Manufacturing (CIM) system by integrating the various operational and information processing functions in design and manufacturing. In spite of being active in research for almost four decades, it is clear that new functionalities are needed to integrate and realize a completely optimal process planning which can be fully compliant towards Smart Factory. In order to develop a CIM system, Computer Aided Process Planning (CAPP) plays a key role and therefore it has been the focus of many researchers. In order to gain insight into the current state-of-the-art of CAPP methodologies, 96 research papers have been reviewed. Subsequent sections discuss the different CAPP approaches adopted by researchers to automate different process planning tasks. This paper aims at addressing the key approaches involved and future directions towards Smart Manufacturing

Influence of the ratio on the mechanical properties of epoxy resin composite with diapers waste as fillers for partition panel application

Materials play significant role in the domestic economy and defense with the fast growth of science and technology field. New materials are the core of fresh technologies and the three pillars of modern science and technology are materials science, power technology and data science. The prior properties of the partition panel by using recycled diapers waste depend on the origin of waste deposits and its chemical constituents. This study presents the influence of the ratio on the mechanical properties of polymer in diapers waste reinforced with binder matrix for partition panel application. The aim of this study was to investigate the influence of different ratio of diapers waste polymer reinforced epoxy-matrix with regards to mechanical properties and morphology analysis. The polymer includes polypropylene, polystyrene, polyethylene and superabsorbent polymer (SAP) were used as reinforcing material. The tensile and bending resistance for ratio of 0.4 diapers waste polymers indicated the optimum ratio for fabricating the partition panel. Samples with 0.4 ratios of diapers waste polymers have highest stiffness of elasticity reading with 76.06 MPa. A correlation between the micro structural analysis using scanning electron microscope (SEM) and the mechanical properties of the material has been discussed

Integrated of Machining Operation Sequences and Cutting Tools Selection Based Artificial Neural Network for Rotational Parts

Process planning is a central, knowledge extensive and important activity in a manufacturing company. The selection of machining operations sequence and cutting tools are important tasks in process planning. In this paper, the main aim is to develop an integrated process planning and artificial neural networks that can automatically perform the task of process planning for rotational parts". The proposed method consists of two modules; the first module NN1 for selection of machining operations sequence based on feature type and their attribute include dimensional, "tolerance, and surface finish by pre-structure of thumb rule and neural network were employed for automated CAPP. The second module NN2 for cutting tool selection based on the type, condition and dimensional ratio of feature, so the NN1outputs as parameter of input layer for NN2. The methods of training, testing and validation of the network have been used back propagation". Case study has found that the developed system is able to give best prediction solutions for process planning problems so, the proposed approach to explain its selection of machining operation sequences  and cutting tools for using in real manufacturing environment

Feature technology and its applications in computer integrated manufacturing

A Thesis submitted for the degree of Doctor of Philosophy of University of LutonComputer aided design and manufacturing (CAD/CAM) has been a focal research area for the manufacturing industry. Genuine CAD/CAM integration is necessary to make products of higher quality with lower cost and shorter lead times. Although CAD and CAM have been extensively used in industry, effective CAD/CAM integration has not been implemented. The major obstacles of CAD/CAM integration are the representation of design and process knowledge and the adaptive ability of computer aided process planning (CAPP). This research is aimed to develop a feature-based CAD/CAM integration methodology. Artificial intelligent techniques such as neural networks, heuristic algorithms, genetic algorithms and fuzzy logics are used to tackle problems. The activities considered include: 1) Component design based on a number of standard feature classes with validity check. A feature classification for machining application is defined adopting ISO 10303-STEP AP224 from a multi-viewpoint of design and manufacture. 2) Search of interacting features and identification of features relationships. A heuristic algorithm has been proposed in order to resolve interacting features. The algorithm analyses the interacting entity between each feature pair, making the process simpler and more efficient. 3) Recognition of new features formed by interacting features. A novel neural network-based technique for feature recognition has been designed, which solves the problems of ambiguity and overlaps. 4) Production of a feature based model for the component. 5) Generation of a suitable process plan covering selection of machining operations, grouping of machining operations and process sequencing. A hybrid feature-based CAPP has been developed using neural network, genetic algorithm and fuzzy evaluating techniques

CAD-Based Automated Design of FEA-Ready Cutting Tools

The resources of modern Finite Element Analysis (FEA) software provide engineers with powerful mechanisms that can be used to investigate numerous machining processes with satisfying results. Nevertheless, the success of a simulation, especially in three dimensions, relies heavily on the accuracy of the cutting tool models that are implemented in the analyses. With this in mind, the present paper presents an application developed via Computer-Aided Design (CAD) programming that enables the automated design of accurate cutting tool models that can be used in 3D turning simulations. The presented application was developed with the aid of the programming resources of a commercially available CAD system. Moreover, the parametric design methodology was employed in order to design the tools according to the appropriate standards. Concluding, a sample tool model was tested by performing a number of machining simulations based on typical cutting parameters. The yielded results were then compared to experimental values of the generated machining force components for validation. The findings of the study prove the functionality of the tool models since a high level of agreement occurred between the acquired numerical results and the experimental ones

STEP COMPLIANT APPROACH FOR TURN-MILL OPERATIONS

Current machine tools have incurred challenges on limitation such as part programming complexity of G and M code, weak integration of digital machine tools and coverage of universal data modeling for product and manufacturing resources. In response to this manufacturing system requirement, Standard for Exchange of Product data (STEP) and its implementation on developing an interface for the next generation of machine tool controllers (STEP-NC) has become a concern of research interest and performed on basic manufacturing technology limited to a unit domain such as turning, milling or Wire EDM. Therefore; extending this STEP implementation on multipurpose machine tools such as turn-mill machines is mandatory since the machines are the main component in these industries. The research work offers a STEP-NC compliant interface supporting turn-mill machining environment identified as SCSTMO

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists