Manufacturing Process Planning Research Articles

Investigating elastic recovery of monocrystalline silicon during plunging experiments with chamfered diamond tools

This study investigates the relationship between elastic recovery and friction during ductile mode machining of monocrystalline silicon. Plunging experiments were performed on an ultra-precision CNC machining center as the silicon work material was rotated. This approach allowed for investigating the combined effect of varying crystal directions and increasing depth on elastic recovery, pressures, and friction. A diamond cutting tool with a chamfered edge that maintains a constant negative rake along the cutting edge was used in plunging tests. Considering the importance of flank face contact area on friction and pressure distributions at the cutting zone, a non-linear expression is proposed to model the elastic recovery ratio as a function of groove depth. It is then employed in an analytical machining model to calculate the variation of pressure and coefficient of friction. The influences of pressure variation and changes in pressure direction as the machining mode transitions from ductile to brittle are investigated based on resolved stresses on the identified active slip plane. The methodology proposed in this study can be extended to study other monocrystalline materials, and the findings can be utilized in the process planning of advanced optics manufacturing.

To achieve sustainability in supply chain with Digital integration: A TISM approach

Conventional supply chain has been shown to be incapable of meeting the ever-increasing demands of customers as well as the requirements of innovation. Due to various uncertainty volatility, ambiguity, and intricacy, the sustainability of supply chain becomes a major topic for organisations. Now there is need to integrate the digital technologies like cloud computing, internet of things, artificial intelligence, big data analysis etc. which improve the performance of supply chain in efficient and responsiveness manner. Due to this digital integration, the system undergoes various changes at organisational, operational, performance and technological level. This study aims to identify nine major critical factors which are enablers to achieve the sustainability in digitally integrated supply chain. A TISM model is developed to address their interrelationship among them. The factors are classified as dependent and independent factors according to their driving and dependence power through the use of the MICMAC analysis. If is confirmed in MICMAC analysis that the factors Agile Organisational structure, Smart logistics Capabilities, Smart Manufacturing Process and financial planning enhance the sustainability of digitally enabled supply chain. This study provides a comprehensive list of enablers that are necessary to achieve sustainability of digitally integrated supply chains; nevertheless, the list is not exhaustive. This paper was written with the intention of contributing a pool of knowledge on achieving sustainability in digitally integrated supply chain. This study has the potential to make it possible for market specialists and executives to focus on critical elements that lead to tactical decisions and maximise value for companies. It establishes a baseline from which future studies can build.

Mathematical programming analysis of additive manufacturing process planning total cost evaluation model for multiple parts

It has been established that an effective and efficient cost evaluation of advanced manufacturing techniques, to a large extent, enhances process planning. In other to achieve optimal advanced manufacturing process planning, a good estimation and evaluation of the process cost are required. In fact, an optimal total processing cost will help to achieve optimal process planning. This study analyzed an adapted advanced manufacturing process planning cost evaluation model, using a mathematical programming technique. The results of the analyses were discussed and represented with graphs, from where conclusions were drawn. The significant status of the different cost components in all the eight cases considered, concerning the achievement of the optimal total cost at the level of planning of the advanced manufacturing process, was revealed by the graphs. Specifically, it was observed that both the processing modules cost and the material handling cost are very significant in minimizing the additive manufacturing process planning total cost of multiple parts.

Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks

Industry 4.0 calls for highly autonomous manufacturing process planning. Significant effort has been devoted over the years to generative Computer Aided Process Planning (CAPP), which aims to generate process plans for new designs without human intervention. This goal has not been realized to date due to several reasons, such as poor scalability and the difficulty in modeling manufacturing process capability, which encapsulates the part shape, quality, and material property transformation capabilities of the process. In our prior work, the shape transformation capabilities of lathe-based machining operations were modeled as latent probability distributions using a data-driven deep learning-based generative machine learning approach, from which visualizations of realistic machinable features could be sampled to assist manual process selection. In this paper, a Siamese Neural Network (SNN) is integrated with Autoencoder-based deep generative models of machining operations to enable automated comparison of the query part shapes with sampled outputs. This enables automated manufacturability analysis and machining process selection necessary for generative CAPP. The paper also demonstrates that the proposed Autoencoder and Siamese Neural Network (AE-SNN) achieves a class-average process selection accuracy of 89 %, and a manufacturability analysis accuracy of 100 %, which outperforms a discriminative model trained on the same dataset.

Manufacturing process planning in aerospace systems

Process planning is a task comprising a broad range of activities to design and develop an appropriate manufacturing process for producing a part. Interpretation of the part design, selection of manufacturing processes, definition of operations, operation sequences, machining datums, geometrical dimensions and tolerances are some common activities associated with the task. Manufacturing process planning can be defined as systematic determination of detailed methods by which work pieces or parts can be manufactured economically and competitively from raw material stage to final finished size. Geometrical features, dimensional sizes, tolerances, materials, and finishes, are analyzed evaluated to determine appropriate sequence of processing operations, which are based on specific, available resources viz machinery and manpower. Inputs to process planning are designing data, raw material data, resource data (machining data, tooling data, fixture data etc), quality requirement data, production type data etc. The output of a process planning is a process document which is an important document in production management. The paper mainly discusses about production planning and control of metallic manufacturing activities in an aerospace Research and Development (R&D) industry, main emphasis on manufacturing process planning. The general process planning methodology adopted in an aircraft industry is discussed. Elements and Functions of process planning, model of process planning, main mission of process planning, the interaction of processes and its process performance indices are discussed and presented. Importance of manufacturing process planning in an aerospace R&D industry is debated. Also, a case study of process planning adopted for manufacturing of a critical Aircraft system component is discussed.

Hybrid Manufacturing Process Planning for Arbitrary Part and Tool Shapes

Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities in multi-modal process plans that leverage the strengths of each. Despite the growing interest in HM technologies, software tools for process planning have not caught up with advances in hardware and typically impose restrictions that limit the design and manufacturing engineers’ ability to systematically explore the full design and process planning spaces. We present a general framework for identifying AM/SM actions that make up an HM process plan based on accessibility and support requirements, using morphological operations that allow for arbitrary part and tool geometries to be considered. To take advantage of multi-modality, we define the actions to allow for temporary excessive material deposition or removal, with an understanding that subsequent actions can correct for them, unlike the case in unimodal (AM-only or SM-only) process plans that are monotonic. We use this framework to generate a combinatorial space of valid, potentially non-monotonic, process plans for a given part of arbitrary shape, a collection of AM/SM tools of arbitrary shapes, and a set of relative rotations (fixed for each action) between them, representing build/fixturing directions on 3-axis machines. Finally, we define a simple objective function quantifying the cost of materials and operating time in terms of deposition/removal volumes and use a search algorithm to explore the exponentially large space of valid process plans to find “cost-optimal” solutions. We demonstrate the effectiveness of our method on 3D examples.

Design manufacturing mesin pengaduk adonan roti

Currently, bread is one of the alternative staple foods that is quite in demand by the public. This has resulted in the bread making industry being enthusiastically welcomed by business people because it has bright and open opportunities for large and small scales. However, the Micro, Small and Medium Enterprises (MSMEs) industry is unable to compete. One of them is because of the use of technology in the dough kneading process. This article discusses product planning for a horizontal type of bread dough mixer using the Design for Manufacturing and Assembly (DFMA) method and Quality Function Deployment (QFD) analysis where the design is based on design, materials, analysis, and equipment and manufacturing process planning. In this study, the Action Research method, engineering engineering and the Solidwork simulation design software were used. The design of the machine can be used for a stirring capacity of 10 kg and a rotation speed of 40 rpm. The results of the design consist of several main parts, namely the main frame, container, stirrer, motor, and transmission system

Network-based integer programming models for flexible process planning

Flexible process planning (FPP) involves selecting and sequencing the requisite operations according to technological requirements, and meanwhile allocating a right machine, a right tool and a right access direction to each selected operation by a given criterion. In this article, the FPP problem is exactly and concisely formulated as linear integer programming models based on the topology of the AND/OR-network under two criteria: production cost minimisation and completion time minimisation. Distinctively, more flexible manufacturing elements and process plan evaluation criteria are considered; more complicated tool and access direction changeover identifications are linearly expressed without the big-M parameter. Compared with the latest mathematical programming models for process planning, the proposed models have lower complexity and better performance. The results from numerous comparative experiments indicate that (i) the number of decision variables of the proposed models reduces approximately by 68% and the number of constraints of the proposed models dramatically reduces by 99%; (ii) within the same running time, the proposed models can exactly solve more benchmark cases than the latest models; and (iii) the solutions obtained by the proposed models are also better than the best ones founded by some state-of-the-art meta-heuristic algorithms.

Special Issue on “Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process”

We live in the big data era, in which a large amount of information is continuously created, registering all kinds of events, such as those generated in the design, planning, control, and execution of manufacturing, logistics, and supply chain processes [...]

Smart Virtual Product Development: Manufacturing Capability Analysis and Process Planning Module

Smart Virtual Product Development (SVPD) system provides effective use of information, knowledge, and experience in industry during the process of product development in Industry 4.0 scenario. This system comprises of three primary modules, each of which has been developed to cater to a need for digital knowledge capture for smart manufacturing in the areas of product design, production planning, and inspection planning. Manufacturing Capability Analysis and Process Planning (MCAPP) module is an important module of the SVPD system, and it involves the provision of manufacturing knowledge to experts working on product development at the early stages of the product lifecycle. In this research, we firstly describe the structure and working mechanism of the SVPD system’s MCAPP module. This is followed by validation of the MCAPP module’s Manufacturing Process Planning (MPP) sub-module against the key performance indicators (KPIs) by using our threading tap case study. Our results verify the feasibility of our approach and show how manufacturing knowledge relating to features and functions can be used to enhance the manufacturing process across similar products during the early stages of product development. An analysis of the basic concepts and methods of implementation show that this is an expert system capable of supporting smart manufacturing which can play a vital role in the establishment of Industry 4.0.

Devolpment and Modeling of an Industrial Process Plan, Its Optimization using stochastic search Optimization Technique

Industrial process planning is principally an association between design and development or final production and has vital function in the manufacturing systems. In this paper the under research industry is security vehicle manufacturing industry in Pakistan. First of all a fundamental process plan is developed and then modeled mathematically using progressive closed loop approach. Mathematically modeled process plan is then optimized in order to find optimal or sub optimal solutions. Research then investigates the capability of an innovative optimization technique called stochastic search in handling optimization of manufacturing process plan. This new technique of stochastic, searches the best approximate process planning solution. Finally the research examines the convergence of optimization techniques to an optimal solution for a manufacturing framework.

Nonlinear thermal simulation of laser metal deposition

ABSTRACT Simulation of Laser Metal Deposition (LMD) is central to the planning of Additive Manufacturing processes. This manuscript presents the computational implementation of a 2D-plus-thickness nonlinear thermal simulation of LMD, which considers: (i) temperature-dependent material properties, (ii) heat losses due to convection and radiation, (iii) geometrical update during material deposition, (iv) phase change and (v) the interaction between the laser and the substrate. The implementation computes the history of the temperature field at a cross-cut normal to the laser trajectory and the history of the bead accumulation. The material deposition model is based on the spatial distribution of the delivered powder. This manuscript presents the mathematical and numerical foundations to execute an efficient local re-meshing of the growing bead. The numerical estimation of the bead geometry is compared with experimental results found in the existing literature. The present model shows reasonable accuracy to predict the bead width ( error) and bead height ( error). This implementation is an in-house one, which allows for the inclusion of additional physical effects. Additional work is needed to account for the particle (thermo) dynamics over the substrate, responsible for a significant material and energy waste, which in turn leads to the actual temperature and molten depth being over-estimated in the executed simulations.

EFFICIENT DETERMINATION OF STABILITY LOBE DIAGRAMS DEPLOYING AN AUTOMATED, DATA-BASED ONLINE NC PROGRAM ADAPTION

Process instabilities due to regenerative chatter pose significant limitations on the achievable material removal rates and thus on the profitability of machining operations. Stability lobe diagrams serve to exploit the maximum yet stable cutting depth and can be determined either analytically or experimentally. While analytical approaches suffer from inaccuracies because of the assumptions made for the specific models, experimental stability lobe diagrams require extensive cutting tests. Therefore, this paper introduces a new automated experimental method for determining stability lobe diagrams in milling with reduced effort regarding time. A closed-loop system is designed, containing a sensor-based online chatter detection along with a strategy to set parameters for subsequent cuts based on the stability boundaries known at each iteration. Both cuts with continuously increasing cutting depth and varied spindle speed are deployed to ensure fast detection of stability limits. The method is tested for a slot milling use case and the results are compared to a conventionally obtained stability lobe diagram yielding a significantly reduction in required time (-90 %) and resources (-67 %) whilst maintaining good accuracy. The reduced effort qualifies the proposed method as a tool to rapidly deliver maximum productive yet stable cutting parameters for optimization of existing or enhanced planning of new manufacturing processes.

Multivariate manufacturing process planning for aircraft airframe production based on weighted criteria analysis

This paper deals with technological and operational aspects of manufacturing process planning for multiple values of decision parameters. A discrete parametric model is proposed to generate multiple process plans for a given or proposed production system. The model is based on generating graphs for possible manufacturing process operations and production system capabilities. An automated platform-based system is built around this model to generate and analyze multiple manufacturing process plans based on the chosen priority criteria. The analyses are based on estimating distances in constructed graphs and several sets of priorities for final assembly production systems as well as parts suppliers. Both manufacturing process selection and production systems architecture used in this model are specifically adapted for the aerospace industry.

Experience-Based Product Inspection Planning for Industry 4.0

In this paper we describe how our Smart Virtual Product Development (SVPD) system can be used to enhance product inspection planning. The SVPD system is comprised of three main modules, these being the design knowledge management (DKM) module, the manufacturing capability and process planning (MCAPP) module, and the product inspection planning (PIP) module. Experiential knowledge relating to formal decisional events is collected, stored and used by the system in the form of set of experiences (SOEs). Here we discuss the working mechanism of the PIP module and show how experiential knowledge relating to the inspection of products that have features and functions in common can be used to enhance product inspection planning during early stages of product development. Our discussion commences with an introduction to fundamental concepts and a general system overview. We then describe the development of our SVPD system’s PIP module, and a case study we undertook for validation purposes. Results of the case study show that our system is capable of supporting product inspection planning in smart manufacturing, and thus has a vital role to play in Industry 4.0.

A digital twin study for immediate design / redesign of impellers and blades: Part 1: CAD modelling and tool path simulation

This paper presents a digital twining study conducted for an immediate design / redesign and manufacturing of on impellers & blades. It is by accomplished by developing (i) CAD automation methods, based on the standard modelling procedures and (ii) Manufacturing automation based on the 3/3 + 1/3 + 2/5 axis milling process. Initially, the CAD model of impeller / blade is created by utilizing the dimensional parameters obtained through standard design calculations / data. It is then parametrized and converted to an automated model through simple dimensional rules and geometric algorithms developed for the purpose. After this stage, the CAD model is analyzed for manufacturing automation where the process planning data comprising cutting tools, process parameters and setups are selected. Here, the tool paths are generated for 3/3 + 1/3 + 2/5 axis milling considering a CNC Vertical Machining Center (VMC) to digitally twin milling process. Both the CAD modelling and manufacturing process plans including tool path generation are captured through journaling and customized / improved using the Application Programmable Interface’s (API’s) to suit the present scope. In this paper, the first part on CAD modelling and manufacturing simulation methodologies are discussed through validating the digital twining concept in a virtual environment. The work is developed with the focus to help industries moving towards Industry 4.0 and requiring a constant design improvement in their products. It is by emphasizing the importance of digital twinning concept where a concurrent verification of design and manufacturing process can be achieved.

Theory and Its Applications to Large-Scale Industrial Engineering Problems of Industrial Engineering Education

Industrial engineering is manufacturing and service Design, analysis of operations and systems, and related to control. Past At the time, an industrial engineer was in a manufacturing plant Working and workers and machines Engaged in functional capacity. Industrial Engineers To eliminate waste in production processes Find ways. a product or service Labor, machinery, materials, information to supply, and efficient systems for integrating energy They create. Industry Engineering every year countless international attracts students. It's great engineering and Committed to business. It is provides undergraduate students with key concepts in the design, planning and optimization of manufacturing and production processes. That is the basis of a Bachelor of Industrial Engineering degree. Petroleum engineer tops our list of highest paying engineering jobs. The primary responsibility of petroleum engineers is to design and develop ways to extract the natural resources of oil and gas from the earth. Elon Musk is a South African-born American who is an Industrial Engineer with Paypal Co-founded and Space Transportation Service He founded the company Space X. He is an electric car Among the early investors in the company Tesla Was and is one company's CEO. SPSS statistics is a SOA, SHO, GWO, PSO, MVO, SCA, GSA, GA, DE. The Cronbach's Alpha Reliability The overall Cronbach's Alpha value for the model is 0.991 which Indicates 70 % reliability. From the literature review, the above 80 % Cronbach's Alpha value model can be considered for analysis.

Decentralized cloud manufacturing-as-a-service (CMaaS) platform architecture with configurable digital assets

Contemporary Cloud Manufacturing-as-a-Service (CMaaS) platforms now promise customers instant pricing and access to a large capacity of manufacturing nodes. However, many of the CMaaS platforms are centralized with data flowing through an intermediary agent connecting clients with service providers. This paper reports the design, implementation and validation of middleware software architectures which aim to directly connect client users with manufacturing service providers while improving transparency, data integrity, data provenance and retaining data ownership to its creators. In the first middleware, clients have the ability to directly customize and configure parts parametrically, leading to an instant generation of downstream manufacturing process plan codes. In the second middleware, clients can track the data provenance generated in a blockchain based decentralized architecture across a manufacturing system. The design of digital assets across a distributed manufacturing system infrastructure controlled by autonomous smart contracts through Ethereum based ERC-721 non-fungible tokens is proposed to enable communication and collaboration across decentralized CMaaS platform architectures. The performance of the smart contracts was evaluated on three different global Ethereum blockchain test networks with the centrality and dispersion statistics on their performance provided as a reference benchmark for future smart contract implementations.

Environmental analysis of selective laser melting in the manufacturing of aeronautical turbine blades

The exponential growth of additive manufacturing technologies is not only improving production processes to achieve functional requirements for products, but it could also help to minimize environmental impacts. In order to align a green product lifecycle management vision, companies need to implement emerging technologies and define a set of metrics that measure the benefits of the change. Each product requires a particular and optimized manufacturing process plan, and each production phase must achieve a significant reduction of critical metrics for the whole Life Cycle Assessment (LCA). This paper provides a comprehensive and comparative LCA of two manufacturing process plans for the case study of an aircraft engine turbine blade. The first process consists of a combination of Investment Casting and Precision Machining and the second consists in the replacement of Investment casting by Selective Laser Melting as an emergent process for near net shape fabrication. The collected data for the comparison includes Global Warming Potential (GWP), Acidification Potential (AP), Ozone layer Depletion Potential (ODP), Human Toxicity Potential (HTP), and Human Toxicity (HT) with cancer and non-cancer effects. The relative analysis shows that, for the critical indicators, an apparent improvement in CO 2 emissions reduction is achieved as well as in the other hazardous emissions. The results showed that the whole lifecycle of Conventional Manufacturing corresponds to 7.32 tons of CO 2 , while, the emission of the Additive Manufacturing is 7.02 tons of CO 2 . The results analysis can be used for decision-making, and it can help for facing future comparative works to explore cleaner manufacturing technologies. • Process Modelling of Additive and Conventional Aerospace Manufacturing. • Data mining and simulation of manufacturing operations with a LCA tool. • Analysis of manufacturing process plan with Selective Laser Melting. • Sustainability Analysis of Investment Casting compared to Selective Laser Melting.

Unbounded Orchestrations of Transducers for Manufacturing

There has recently been increasing interest in using reactive synthesis techniques to automate the production of manufacturing process plans. Previous work has assumed that the set of manufacturing resources is known and fixed in advance. In this paper, we consider the more general problem of whether a controller can be synthesized given sufficient resources. In the unbounded setting, only the types of available manufacturing resources are given, and we want to know whether it is possible to manufacture a product using only resources of those type(s), and, if so, how many resources of each type are needed. We model manufacturing processes and facilities as transducers (automata with output), and show that the unbounded orchestration problem is decidable and the (Pareto) optimal set of resources necessary to manufacture a product is computable for uni-transducers. However, for multitransducers, the problem is undecidable.