Research on Technological Process Control Model of Reverse Logistics in Manufacturing System

This paper has found out some important input factors of reverse logistics in manufacturing system throuth analysis and summary,and established four kinds of technological process control models of reverse logistics in manufacturing system according to different processing methods. These models embed each other that form a cubic control system of reverse logistics.

Simulation-Based Construction of Three-Dimensional Process Model for Punching Cartridge Cases

A method of constructing three-dimensional process model for the punching cartridge cases is presented based on DEFORM simulation analysis. Using DEFORM software,the finite element simulation models for the punching and forming process of cartridge cases are established,and the corresponding simulation result model of each intermediate procedure is obtained by continuously performing the forming process simulation. The simulation model cannot annotate size and process information due to poor interface between DEFORM software and CAD software. Thus,a 3D annotation module is developed with secondary development technology of UG NX software. Consequently,the final process model with dimension and process information is obtained. Then,with the current 3D process management system,the 3D punching and forming process design of cartridge cases can be completed further. An example is also provided to illustrate that the relative error between the simulation process model and the physical model is less than 2%,which proves the validity and reliability of the proposed method in this study.

A decision-making model of gear process for green manufacturing

A decision-making model of gear process for green manufacturing is presented, which integrates the five objectives including the time, quality, cost, resource consumption and environmental impact of gear process together into the development of a strategy. Mathematical description is provided for the multi-objectives decision-making model. The expert judgment and the multi-fuzzy assessment theory are introduced to do sensible comparisons and give quantitative results. A case study on practical cutting tool selection in gear machining demonstrates that the proposed model is applicable.

A method of manufacturing process modeling based on activity network for large-sized and complex products

Simulation technique is an efficient approach to realize the planning and scheduling of manufacturing process of products. An appropriate and efficient manufacturing process model is the basis and key of manufacturing process simulation. By analyzing the features of large-sized and complex products, a method of manufacturing process modeling based on activity network is presented and a mapping algorithm of translating BOM/BOP into the manufacturing process model is designed in detail.

Attitude Analysis in Process Conflict for C919 Aircraft Manufacturing

Based on the option prioritization in graph model for conflict resolution of two decision makers(DMs),new logical and matrix representations of four stability concepts for DMs′attitude are proposed.The logical representation of attitude is defined,and converted to the matrix form in order to develop a decision support system(DSS)efficiently.Compared with existing definitions of DMs′attitude based on states,the proposed definitions of attitude based on options are convenient and more effective to generate preferences since that of states can be significantly larger than that of options in a large conflict.In addition,it is easier to obtain the information of the prioritization of option statements than to obtain preference of states for users.The proposed representations are applied to the process conflict during aircraft manufacturing to demonstrate the efficiency of the new approach.

Locating Error Considering Dimensional Errors Modeling for Multistation Manufacturing System

Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Three-Dimensional Model Reconstruction of Nonwovens from Multi-Focus Images

The three-dimensional(3D)model is of great significance to analyze the performance of nonwovens.However,the existing modelling methods could not reconstruct the 3D structure of nonwovens at low cost.A new method based on deep learning was proposed to reconstruct 3D models of nonwovens from multi-focus images.A convolutional neural network was trained to extract clear fibers from sequence images.Image processing algorithms were used to obtain the radius,the central axis,and depth information of fibers from the extraction results.Based on this information,3D models were built in 3D space.Furthermore,self-developed algorithms optimized the central axis and depth of fibers,which made fibers more realistic and continuous.The method with lower cost could reconstruct 3D models of nonwovens conveniently.

A Perspective on Smart Process Manufacturing Research Challenges forProcess Systems Engineers

The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.

Analysis of the Manufactured Tolerances with the Three-Dimensional Method of Angular Chains of Dimensions Applied to a Cylinder Head of Car Engine

This paper proposes an analysis method of the manufactured tolerances applied to a cylinder head of car engine. This method allows to determine the manufacturing tolerances in the case of angular chains of dimensions and to check its correspondence with the functional tolerances. The objective of this work is to analyze two parameterized functions: the angular defect Δα and the projected length lg of the toleranced surface. The angular defects are determined from the precision of the machine tools, we consider only the geometrical defects (position and orientation of surfaces), making the assumption that the form defects are negligible. The manufactured defect is determined from these two parameterized functions. Then it will be compared with the functional condition in order to check if the selected machining range allows, at end of the manufacturing process, to give a suitable part.

RESEARCH AND PRACTICE FOR MANUFACTURING ENVIRONMENT MODELLING TECHNOLOGY OF CIMS

An abstract model and representation method for manufacturing environment is presented based on analysis of process planning tasks and factory configuration capable of handling the process plans. This model has been proved to be applicable to the generative process planning and to shopflow control information integration. A general manufacturing environment modelling tool has been developed under the X-window support to verity the mentioned model and modelling technology.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

Control for Intelligent Manufacturing： A Multiscale Challenge

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include： multi-time scale, spacetime scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys

Predicting the mechanical properties of additively manufactured parts is often a tedious process,requiring the integration of multiple stand-alone and expensive simulations.Furthermore,as properties are highly location-dependent due to repeated heating and cooling cycles,the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification,compounding the computational expense.This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels,a type of machine learning model for statistical inference with limited data.The metamodels can predict the varying properties within an entire part in a fraction of the time while providing uncertainty quantification.The framework was demonstrated with the prediction of the tensile yield strength of Ferrium?PH48S maraging stainless steel fabricated by additive manufacturing.Impressive agreement was found between the metamodels and the mechanistic models,and the computation was dramatically decreased from hours of physics-based simulations to less than a second with metamodels.This method can be extended to predict various materials properties in different alloy systems whose processstructure-property-performance interrelationships are linked by mechanistic models.It is powerful for rapidly identifying the spatial properties of a part with compositional and processing parameter variations,and can support part certification by providing a fast interface between materials models and part-level thermal and performance simulations.

Three-dimensional modeling and reconstructive change of residual stress during machining process of milling, polishing, heat treatment, vibratory finishing, and shot peening of fan blade

Residual stress during the machining process has always been a research hotspot,especially for aero-engine blades.The three-dimensional modeling and reconstructive laws of residual stress among various processes in the machining process of the fan blade is studied in this paper.The fan blades of Ti-6Al-4V are targeted for milling,polishing,heat treatment,vibratory finishing,and shot peening.The surface and subsurface residual stress after each process is measured by the X-ray diffraction method.The distribution of the surface and subsurface residual stress is analyzed.The Rational Taylor surface function and cosine decay function are used to fit the characteristic function of the residual stress distribution,and the empirical formula with high fitting accuracy is obtained.The value and distribution of surface and subsurface residual stress vary greatly due to different processing techniques.The reconstructive change of the surface and subsurface residual stress of the blade in each process intuitively shows the change of the residual stress between the processes,which has a high reference significance for the research on the residual stress of the blade processing and the optimization of the entire blade process.

Cooperative Optimization of Reconfigurable Machine Tool Configurations and Production Process Plan

The production process plan design and configurations of reconfigurable machine tool （RMT） interact with each other. Reasonable process plans with suitable configurations of RMT help to improve product quality and reduce production cost. Therefore, a cooperative strategy is needed to concurrently solve the above issue. In this paper, the cooperative optimization model for RMT configurations and production process plan is presented. Its objectives take into account both impacts of process and configuration. Moreover, a novel genetic algorithm is also developed to provide optimal or near-optimal solutions： firstly, its chromosome is redesigned which is composed of three parts, operations, process plan and configurations of RMTs, respectively; secondly, its new selection, crossover and mutation operators are also developed to deal with the process constraints from operation processes （OP） graph, otherwise these operators could generate illegal solutions violating the limits; eventually the optimal configurations for RMT under optimal process plan design can be obtained. At last, a manufacturing line case is applied which is composed of three RMTs. It is shown from the case that the optimal process plan and configurations of RMT are concurrently obtained, and the production cost decreases 6.28% and nonmonetary performance increases 22%. The proposed method can figure out both RMT configurations and production process, improve production capacity, functions and equipment utilization for RMT.

Advances of physics-based precision modeling and simulation for manufacturing processes

The development of manufacturing process concerns precision, comprehensiveness, agileness, high efficiency and low cost. The numerical simulation has become an important method for process design and optimization. Physics-based modeling was proposed to promote simulations with a high accuracy. In this paper, three cases, on material properties, precise boundary conditions, and micro-scale physical models, have been discussed to demonstrate how physics-based modeling can improve manufacturing simulation. By using this method, manu- facturing process can be modeled precisely and optimized for getting better performance.

A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)

Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.

A Review of Residual Stress and Deformation Modeling for Metal Additive Manufacturing Processes

A metal additive manufacturing process results in a nearly net-shaped fabrication of parts directly from digital data.A local heat source melts the deposited material,and a part is built layer-by-layer.Residual stress and de-formation are critical issues experienced by additively manufactured parts.Modeling the additive manufacturing process provides important insights and can help determine an optimal build plan so as to minimize residual stress formation.Various approaches have been used for modeling of residual stresses,ranging from high-fidelity models to simplified models,for quicker results.This paper provides a state-of-the-art review of the approaches used to numerically model residual deformation and stresses in structures built using additive manufacturing.Fur-thermore,it describes the physical causes of residual-stress generation in an additively manufactured structure.

A Novel Mechanism for TRF of Plant-wide Material Flows in Process Industry MES

This paper is standing on the recent viewpoint originated from relevant industrial practices that well or-ganized tracing, representing and feedback(TRF) mechanism of material-flow information is crucial for system utility and usability of manufacturing execution systems(MES), essentially, for activities on the side of multi-level decision making and optimization mainly in the planning and scheduling. In this paper, we investigate a key issue emphasized on a route of multi-level information evolution on the side of large-scale feedback, where material-flow states could evolve from the measuring data(local states) to networked event-type information cells(global states) and consequently to the key performance indicators(KPI) type information(gross states). Importantly, with adapta-bilities to frequent structural dynamics residing in running material flows, this evolving route should be modeled as a suit of sophisticated mechanism for large-scale dynamic states tracking and representing so as to upgrade accu-racy and usability of the feedback information in MES. To clarify inherent complexities of this evolving route, the investigated issue is demonstrated from extended process systems engineering(PSE) point of view, and the TRF principles of the multi-level feedback information(states) are highlighted under the multi-scale methodology. As the main contribution, a novel mechanism called TRF modeling mechanism is introduced.