Manufacturing Resource Capability Research Articles

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

The development of an ontology for describing the capabilities of manufacturing resources

Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

Formal Resource and Capability Models supporting Re-use of Manufacturing Resources

In the field of manufacturing the responsiveness has become a new strategic goal for the enterprises alongside with quality and costs. Efficient responsiveness requires production reconfiguration ranging from layout to equipment. The production system capabilities originate from the tool and equipment level. While a resource is being used, its condition and capability may change. It is crucial to consider the resources’ individual lifecycle, their actual capabilities and condition during the system design and reconfiguration. Thus, the lifecycle perspective in the capability management is of utmost importance. This paper presents the development of the Manufacturing Resource Capability Ontology (MaRCO), focusing on describing the functional capabilities of manufacturing resources. Special emphasis is placed on the lifecycle management aspect of the resource descriptions.

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.

Parts Tolerance Allocation Based on Assembly Clearance Analysis and Product Functional Parameters

Geometric tolerances are assigned to avoid parts rejection in a manufacturing process while ensuring fulfillment of intended function. Selection of tolerance value is an important task which affects the overall product cost and performance both. The significance of tolerance value is more evident in case of mating parts where the dimension of more than one parts share responsibility of successful assembly and design. The objective of this research is to determine appropriate tolerance value based on assembly clearance analysis which takes into account relationship between assembly clearance and product performance parameters. The study is conducted through CAD-CAE integrated finite element analysis (FEA) simulations of the product process model. The results output help in distinguishing different tolerance levels which can be further probed for optimal, using available manufacturing resource capability data. The research output is a more practical tolerance value achieved based on product performance analysis as well as available resources and appropriate cost. The results obtained validate the utility of the approach presented in this paper.

RESEARCH ON GRAPH THEORY-BASED MANUFACTURING SETUP PLANNING

Automatic generation of manufacturing setup planning is studied in this work. The mathematical model to describe the tolerance information and datum-machining feature relationship has been formulated based on extended graphics. By means of the transformation between the feature tolerance relationship graph(FTG) and the datum-machining feature relationship graph (DMG), the algorithm to automatically generate setup planning has been given based on the tolerance analysis and manufacturing resources capability model. The algorithm can identify the machining feature and datum, optimize setup groups based on the manufacturing resource capability and tolerance analysis, as well as minimize the influence of the locating error stack-up on the machining quality. Finally, a case study of setup planning is presented to verify the feasibility of the methodology.

Computer aided manufacturing planning for mass customization: part 2, automated setup planning

Setup planning plays a crucial role in CAPP to ensure product quality while maintaining acceptable manufacturing cost. The tasks of setup planning include identifying manufacturing features and corresponding manufacturing processes, determining the number of setups, part orientation, locating datum and process sequence in each setup, and selecting machine tools and fixtures. An automated setup planning technique and system has been developed based on not only the tolerance analysis, but also the manufacturing resource capability analysis. The automated setup planning is divided into two levels: setup planning in single part level and in machine station level. Algorithms for setup generation and process sequencing have been developed and a case study of setup planning is presented.

TOLERANCE ANALYSIS BASED COMPUTER-AIDED SETUP PLANNING

ABSTRACT Setup Planning is to determine the number/sequence of setups in a production line and operation details in each setup. To bridge the gap between computer-aided process planning (CAPP) and computer-aided fixture design (CAFD), an automated setup planning and tolerance decomposition method is developed. Directed graph is extended to represent feature/dimension/tolerance relationships (FTG) and datum/machining feature relationships (DMG). According to different production schemes and manufacturing resource capabilities, setup planning principles and algorithms are explored to automatically extract DMG from FTG. Under the true positioning frame, tolerance decomposition models are concluded to partition a tolerance into interoperable machining errors, such as locating error, tool alignment error, and random process error. As one of the deterministic components of machining errors, locating error is primarily caused by positional variations of locating elements in fixture design and dimensional variations of locating features on workpieces. In this research, mathematical models are developed to analyze locating variations and their influences on machining features. The analysis results of locating error effects can be used in budgeting the locating precision and improving the setup plan. The technique has been implemented on a commercial CAD system with applications to automobile manufacturing industry.