Generation Of Process Plans Research Articles

Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.

A feature-based approach towards integration and automation of CAD/CAPP/CAM for EDM electrodes

Electrical discharge machining (EDM) is widely used in mold manufacturing to form the intricate geometric features which are difficult to be produced by the conventional machining process. Owing to the use of a large number of electrodes, the cost and time consumption for electrode design and NC programming has become a bottleneck in boosting productivity of mold enterprises. Although computer-aided design (CAD)/computer-aided manufacturing (CAM) systems are widely used, because of the complex shapes and diversity of electrodes, manual operations are commonly employed to generate process plans and tool paths for electrode manufacturing, which is cumbersome and error-prone. In order to realize the intelligent manufacturing of electrodes in such a knowledge-intensive domain, this paper presents a feature-based integration approach of CAD/computer-aided process planning (CAPP)/CAM. A hierarchical taxonomy of electrode features is introduced and a hybrid feature recognition method is proposed to build multi-level feature tree. Then, feature knowledge and domain know-how based process planning and optimization are achieved followed by automatic tool path generation which aim to machining the electrode precisely and economically. In the framework of system integration, a structured product model is established to capture and encapsulate geometric entities, machining features, technical information, process plans, and measurement data of machining error to realize seamless flow of information among CAD/CAPP/CAM systems. The effectiveness and efficiency of the proposed approach are demonstrated by case studies and industry implementation.

A STEP-based machining data model for autonomous process generation of intelligent CNC controller

As the key component of intelligent CNC machine tool, CNC controller is expected to provide various intelligent functions, among which, autonomous process planning is one of the most important functions. However, current machining data models are not very suitable to support this function. G&M code gives movement and auxiliary instructions directly, making the CNC controller just an executor. A new standard STEP-NC, providing semantically rich information, aims to overcome the drawbacks of G&M code. However, information in the data model specified in STEP-NC is not very suitable for intelligent CNC controller autonomous process planning because of the lack of important machining requirements. Also, in STEP-NC model, machining strategies, which are supposed to be decided by CNC controller, have already been included. Namely, decision right is not totally handed over to the controller. To solve these problems, a STEP-based machining data model is proposed in this paper. The machining data model is an ARM, and it is designed as the machining task input of intelligent CNC controller. It can provide necessary workpiece information for intelligent process planning functions based on knowledge base. With the consideration of interoperation with design stage, STEP data format is used in the machining data model. Detailed structure and implementing method is introduced. In order to verify the feasibility of this data model, generation method of machining task complied with this data model and process plan generation based on the machining task have been put forward through a case. Results show the machining task complied with this data model can provide sufficient information for the autonomous process planning function of CNC controller.

Exhaustive Search Based Heuristic for Solving Machine Layout Problem in Reconfigurable Manufacturing System Design

Reconfigurable manufacturing systems (RMS) are designed to overcome the shortcomings of conventional manufacturing systems in terms of effectiveness and responsiveness facing the increasingly influencing factors imposed on the manufacturing environment by unpredictable market condition and global competition. RMS is required to be more robust, flexible and reconfigurable, which makesmachine layout problem a key step to fulfill such design. Thus, any proposed layout needs to be changeable and easily redesigned. In this paper, we consider the machine layout problem in reconfigurable context. We develop an exhaustive search based heuristic to solve it. To propose the best respective machine layout, the heuristic considers various constraints from the manufacturing environment, and the initial generated process plan for the designed RMS under performance metrics. The proposed layout must at its best, respects both the constraints imposed by the generated process plan and those depicting the available location in the shop floor where machines are going to be placed. An illustrative numerical example is proposed to demonstrate the applicability of our approach.

Interdisciplinary Data Driven Production Process Analysis for the Internet of Production

Recent developments in the industrial field are strongly influenced by requirements of the fourth industrial revolution (I4.0) for modern Cyber-Physical Production Systems (CPPS) and the coherent phenomenon of industrial big data (IBD). I4.0 is characterized by a growing amount of interdisciplinary work and cross-domain exchange of methods and knowledge. Similar to the development of the Internet of Things (IoT) for the consumer market, the emergence of an Internet of Production (IoP) in the industrial field is imminent. The future vision for an IoP is based on aggregated, multi-perspective and persistent data sets that can be seamlessly and semantically integrated to allow diagnosis and prediction in domain-specific real-time. In this paper, we demonstrate an exemplary scenario of collaborative cross-domain work, in which domain-experts from largely different fields of expertise, i.e. heavy plate rolling (HPR), injection molding (IM) and machine learning (ML), generate insights through data driven process analysis in two use cases. Specifically, in the HPR use case, reinforcement-learning was utilized to support the planning phase of the process aiming to reduce manual work load and to ultimately generate process plans that serve as a foundation for a simulation to calculate process results. On the contrary, in the IM use case, supervised-learning was utilized to learn a complex and computationally demanding finite element simulation model in order to predict process results for unknown process configurations, which can be used to optimize the process planning phase. While both use cases had the overall goal to utilize ML to gain new insights about the respective process, the actual ML application was utilized with reversed purpose. Particularly, in the HPR use case, ML was used to learn the process planning in order to calculate process results while in the IM use case, ML was used to predict process results in order to improve the process planning. We facilitate the communication between physically separated domain experts and the exchange of gained insights in the respective use cases by a framework that addresses the specific needs of cross-domain collaboration. We show that the insights gained from two largely different use cases are valuable to the domain experts of the other respective use case, facilitating cross-domain data driven production process analysis for future IoP scenarios.

Reconfigurable machining process planning for part variety in new manufacturing paradigms: Definitions, models and framework

Abstract Conventional machining process planning approaches are inefficient to handle the process planning complexity induced by part variety. Reconfigurable process planning is a new process planning approach which has been well recognized as a key enabler for current manufacturing paradigms. However, in the literature, there is neither a comprehensive part variety representation model to support reconfigurable process planning nor a global solution framework to instruct the generation of the feasible process plans for a specific part variant. Therefore, this paper extends the concept of reconfigurable process planning to a concept of reconfigurable machining process planning which targets the process plan generation for a part family. A solution framework is developed for reconfigurable machining process planning. In this framework, a feature-based part variety model is proposed to represent a part family; A reconfigurable machining process plan is defined as a set of modular components which can be configured/reconfigured into the machining process plans for any part variant in the family; a novel configuration approach is proposed to generate the process plan components for a specific part variant while configuring this part variant from the family. The feasibility and effectiveness of the proposed framework and models are tested in a real case study.

Analysis and structuring of process planning logic for CNC machining: An education and training perspective

Programming of computer numerical control (CNC) machining centres using computer aided manufacturing (CAM) software is efficient provided that machining operations and their sequence has been correctly determined by the user. Such decisions, however, are a largely matter of experience. If the user is inexperienced, then Computer Aided Process Planning systems may be employed, but these are usually legacy systems. Thus, normally some prior knowledge has to be induced to the inexperienced user in the form of structured knowledge or rules. After studying a representative number of parts, creating and validating process plans and feeding them to a CAM system some baseline knowledge has been created and structured into rules. Training the user towards applying these rules, or even creation of custom rules is based on part analysis according to a basic reasoning. This is best implemented by advance-organisers of the schematic type regarding part form, process identification, work-holding and setup sequencing, whilst creation of steps or instructions for guiding the process plan generation flow is effected according to the Structure of the Observed Learning Outcome paradigm. The method has been applied to a sizeable audience and preliminary statistical and qualitative evidence on its effectiveness is presented.

Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints

The reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm driven by the high responsiveness and performance efficiencies. In such system, machines, material handling units or machines components can be added, modified, removed or interchanged as needed. Hence, the design of RMS is based on reconfigurable machines capabilities and product specification. This paper addresses the problem of machines selections for RMS design under unavailability constraints and aims to develop an approach to ensure the best process plan according to the customised flexibility required to produce all parts of a given product. More specifically, we develop a flexibility-based multi-objective approach using an adapted version of the well-known non-dominated sorting genetic algorithm to select adequate machines from a set of candidate (potential) ones, in order to ensure the best responsiveness of the designed system in case of unavailability of one of the selected machines. The responsiveness is based on the flexibility of the designed system and a generated process plan, which guarantees the management of machines unavailability. It is defined as the ability and the capacity to adapt the process plan in response to machines unavailability. Two objectives are considered, respectively, the maximisation of the flexibility index of the system and the minimisation of the total completion time. To choose the best solution in the Pareto front, a multi-objective decision-making method called technique for order of preference by similarity to ideal solution is used. To demonstrate the applicability of the proposed approach, a simple example is presented and the numerical results are analysed.

Comparative Study of Machining Technology Selection to Manufacture Large-Size Components of Offshore Constructions

Abstract The focus of this paper is on process planning for large parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centres. The analysis of machining heavy mechanical components used in off-shore constructions has been carried out. Setup concepts applied and operation sequences determined in related process plans underwent studies. The paper presents in particular a reasoning approach to setup sequencing and machine assignment in manufacturing large-size components of offshore constructions. The relevant reasoning mechanism within a decision making scheme on generated process plan is shown based on a case study derived from the offshore sector. Recommendations for manufacture of selected exemplary and typical parts were formulated.

Toward Process Control from Formal Models of Transformable Manufacturing Systems

The automation and flexibility of production systems is a key step towards improved profitability and competitiveness in high labour cost areas, when producing high-complexity, low-volume products. In the Evolvable Assembly Systems (EAS) project, the ‘manufacturability’ (or ‘realisability’) and ‘control’ algorithms were introduced to accommodate the batch-size-of-one production of highly customisable products. These algorithms enable checking whether a production line can manufacture a given product with its available set of resources, and how the product should be manufactured, e.g. which resources to use, and when. To this end, the authors formally define production recipes, which represent products, and manufacturing resources which make up a manufacturing facility. This paper re-defines these notions in the ISO-standard EBNF (Extended Backus-Naur Form) notation, and adapts the the manufacturability and control algorithms to accommodate the new definitions. The new algorithms and data structures reflect more closely the ones that are used in an implemented software tool. This paper also reports a method by which recipes and resources could be used to generate manufacturing process controllers in the Business to Manufacturing Markup Language (B2MML) standard. In doing so, this paper takes a step toward a complete path from the formal specification of a manufacturing facility and the products to be manufactured, to the automatic generation of executable process plans.

E-CAPP: A distributed collaborative system for internet-based process planning

Advances in distributed technologies have enabled engineers to communicate more effectively, collaborate, obtain, and exchange a wide range of design resources during product development. Shared internet-based virtual environments allow experts in remote locations to analyze a virtual prototype, together and simultaneously in centers in which the product is being developed.This paper presents a system for distributed and collaborative environment which could assist manufacturing enterprises and experts in discussing, suggesting, evaluating and selecting best process plans for family of manufacturing parts. The represented e-CAPP system enables the implementation of expert knowledge in an appropriate knowledge repository. The knowledge from this repository is integrated into intra-company CAPP systems and used while generating process plans for new products. The proposed internet-based collaborative environment, dedicated to distributed process planning, is yet another step in the direction advancing of distributed manufacturing.

Automatic Recognition of Internal Features of Axisymmetric Parts from 2-D Images

Objectives: This paper presents the development of a software package for Automatic Extraction of Geometrical Data (AEGD) and Recognition of Internal Turning Features (RITF). Methods/Analysis: The developed algorithms used to extract geometrical data and recognize internal features from the 2-D gray scale images and analyzed the recognized features. The expert system is developed using Java Advanced Image packages. The extracted test examples geometrical data analyzed with known geometrical data of a CAD model of the same axisymmetric component. Findings: The present research work deals with the development of a feature-based approach to automatically extract geometrical data and recognize the internal features of the workpiece from an image database without any human intervention. The geometrical data extracted from the sample images and the extracted data used to recognize turned features with the developed algorithms. It is found that the average percentage variation of extracting geometrical data not more than 0.09. Applications/Improvement: The proposed system is novel approach to extract geometrical data from the image files and use this data to recognize turned features. This information is required for downstream applications i.e. generation of process plans, production schedules, NC code generation etc.

An Automated Assembly Process Planning System

The generation of assembly process plans of complex products is a challenging task. An assembly processing planning system reducing the human intervention and the computational effort is discussed. The method utilizes the information stored into the CAD model of the assembly for the extraction of the part precedence information which is then complemented with technological priorities. Results are demonstrated in an industrial case study from the optoelectronics industry.

A Hybrid Learning Algorithm for Generating Multi-Agent Daily Activity Plans

This paper proposes a hybrid learning algorithm based on the competing risk model and the cross entropy method for generating complete one-day activity plans for multi-agent traffic simulations. An agent’s activity plan generation process is modeled using the Markov decision process. As generating complete activity plans of agents using a reinforcement learning approach is computationally expensive and inefficient, we propose a hybrid method that first estimates the activity type of agents and the scheduled ending time sequences from empirical data based on a competing risk model. The activity plans obtained are then completed by the cross entropy method for the optimal destination choice learning of agents. The performance of the proposed method is compared with the Q-learning algorithm. The numerical result shows that the proposed method generates consistent daily activity plans for multi-agent traffic simulations.

Development of a CAD-CAM Interaction System to Generate a Flexible Machining Process Plan

A unique machining knowledge has led to several different perspectives between planners and operators as regards in designing a machining process plan. All precedents have shown the need to maintain a suitable machining process plan. Commercial Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems have facilitated the manipulation of 3D models to generate a machining process plan. The open Advanced Programming Interfaces (APIs) are also helpful in tailoring decision support systems to determine process plans. This study proposes an emergent system to generate flexible machining process plans. The proposed system considers the integration between design and manufacturing perspective to produce relevant machining process plan. The generation of process plans begins by considering the total removal volume of the raw material, estimating the removal features, thus analyzing and ordering several candidates of machining process plans. The total machining time and number of setups from each machining process plan candidate is analyzed and evaluated. Eventually, the proposed system is tested using several prismatic 3D models of a workpiece to show the outcomes.

An overview of function block enabled adaptive process planning for machining

Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.

An intelligent process planning system for micro turn-mill parts

An attempt has been made to develop a generalised system for the generation of process plan for the manufacture of micro parts in this work. It has two components; first one deals with automatic part feature extraction from feature-based model and second one performs the execution of process planning activities in accordance with feature using knowledge-based system approach. The proposed system maps extensible markup language (XML) data for the feature based-model and produces the corresponding manufacturing activities needed for the manufacture of micro parts. The process plan modules considered in this work includes process sequence, tools and fixtures, process parameters selection and set-up plan generation. An attempt has been made to develop process parameters selection module based on experimental investigation and optimisation apart from manufacturing catalogues and user manuals. Feature extraction through XML files avoids complex feature extraction process. The application of the developed system has been verified with a case study. The present system is limited to micro turn-mill features. Incorporation of more micro features and consideration of other activities of process plan ensures a complete process planning system for micro-machining processes.

A process planning method for reduced carbon emissions

Consumers, industry, and government entities are becoming increasingly concerned about the issue of environmental sustainability. With this in mind, manufacturers have begun to explore proactive means for reducing their level of resource consumption, and the amount and impact of their generated waste streams. Little research has been conducted on the development of process planning methods that consider environmental factors. In this paper, a new process planning method based on a carbon emission function model is presented that integrates both economic and environmental considerations. The proposed method consists of four steps: (1) component feature identification, (2) generation of alternative operations, (3) selection of operations with lower carbon emissions, and (4) generation of process plan based on a genetic algorithm. This method produces a comparatively ‘green’ and economical process plan. The method is demonstrated using an example part and the benefits of the method in terms of energy consumption and carbon emissions are evaluated. This paper concludes with a discussion of potential approaches that can facilitate seamless integration of environmental considerations into process planning.

Data Mining-supported Generation of Assembly Process Plans

The application and functional scope of digital assembly planning tools have been permanently increasing in order to deal with product and process complexity. Consequently a large amount of assembly-related data is stored in different systems alongside the product emergence process. By means of data mining techniques an intelligent utilization of this data can be accomplished for future assembly planning. This paper presents an approach for data mining-supported generation of assembly process plans to enhance planning efficiency. The approach is based on the classification and clustering of both product and process data as well as on the identification of their correlations.

Automatic CNC Part Programming for through Hole Drilling

This paper describes a novel method for automatic generation of process plans and numerical control (NC) part programs from STEP data files. Using proposed system, it is possible to achieve fully automation of recognition of through hole features, generation of process plans and NC part programs. Thus, it becomes possible to go from neutral file to finished product in a fully automated fashion. The algorithms have been implemented on mini-computer to process product data, and display recognized hole features, process plans and creates output files containing NC part programs. These NC part programs are tested through BMV45TC24 CNC Vertical Machining Centers. The results are satisfactory.