Operation optimization of the steel manufacturing process: A brief review

Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel manufacturing process is essential to reduce the production cost, increase the production or energy efficiency, and improve production management. In this study, the operation optimization problem of the steel manufacturing process, which needed to go through a complex production organization from customers' orders to workshop production, was analyzed. The existing research on the operation optimization techniques, including process simulation, production planning, production scheduling, interface scheduling, and scheduling of auxiliary equipment, was reviewed. The literature review reveals that, although considerable research has been conducted to optimize the operation of steel production, these techniques are usually independent and unsystematic.Therefore, the future work related to operation optimization of the steel manufacturing process based on the integration of multi technologies and the intersection of multi disciplines were summarized.

Manufacturing Process Selection of “Green” Oil Palm Natural Fiber Reinforced Polyurethane Composites Using Hybrid TEA Criteria Requirement and AHP Method for Automotive Crash Box

In this study,the best manufacturing process will be selected to build an automotive crash box using green oil palm natural fibre-reinforced polyurethane composite materials.This paper introduces an approach consist of technical aspects(T),the economic point of view(E)and availability(A),and it’s also called as TEA requirement.This approach was developed with the goal of assisting the design engineer in the selection of the best manufacturing process during the design phase at the criteria selection stage.In this study,the TEA requirement will integrate with the analytical hierarchy process(AHP)to assist decision makers or manufacturing engineers in determining the most appropriate manufacturing process to be employed in the manufacture of a composite automotive crash box(ACB)at the early stage of the product development process.It is obvious that a major challenge in the manufacturing selection process is lack of information regarding manufacturing of ACB using natural fibre composite(NFC).There have been no previous studies that examined ranking manufacturability processes in terms of their suitability.Therefore,the TEA-AHP hybrid method was introduced to provide unprejudiced criteria-ranking selection prior to evaluation of pairwise comparisons.At the end of this study,the pulforming process was selected as the best manufacturing process for fabrication of the ACB structural component.

Mechanical and Rheological Properties of Bamboo Pulp Fiber Reinforced High Density Polyethylene Composites:Influence of Nano CaCO_(3)Treatment and Manufacturing Process with Different Pressure Ratings

In order to investigate the effect of the relative motion of nano CaCO_(3)reinforced bamboo pulp fiber(BPF)/HDPE composite components on the mechanical performance,a comparative study was performed.BPF was treated by nano CaCO_(3)blending(BM)and impregnation modification(IM)technology.The composites were produced using hot press(HPMP),extrusion(EMP)and injection molding process(IMP).The physical morphology of BPF was similar at different manufacturing processes.Compared to the samples manufactured by HPMP,a decrease in the(specific)flexural strength of BPF/HDPE composites and an increase in those of composites treated by nano CaCO_(3)manufactured by EMP and IMP were observed.The injection molded composites exhibited the best values in the(specific)impact strength,(specific)tensile properties.IM had a greater effect on the rheological behavior of the composites than BM,and nano CaCO_(3)treatment most effectively affected the performance of the extrusion molded composites.

Micro-scale Realization of Compliant Mechanisms:Manufacturing Processes and Constituent Materials-A Review

Compliant micromechanisms(CMMs)acquire mobility from the deflection of elastic members and have been proven to be robust by millions of silicon MEMS devices.However,the limited deflection of silicon impedes the realization of more sophisticated CMMs,which often require larger deflections.Recently,some novel manufacturing processes have emerged but are not well known by the community.In this paper,the realization of CMMs is reviewed,aiming to provide help to mechanical designers to quickly find the proper realization method for their CMM designs.To this end,the literature surveyed was classified and statistically analyzed,and representative processes were summarized individually to reflect the state of the art of CMM manufacturing.Furthermore,the features of each process were collected into tables to facilitate the reference of readers,and the guidelines for process selection were discussed.The review results indicate that,even though the silicon process remains dominant,great progress has been made in the development of polymer-related and composite-related processes,such as micromolding,SU-8 process,laser ablation,3D printing,and the CNT frameworking.These processes result in constituent materials with a lower Young’s modulus and larger maximum allowable strain than silicon,and therefore allow larger deflection.The geometrical capabilities(e.g.,aspect ratio)of the realization methods should also be considered,because different types of CMMs have different requirements.We conclude that the SU-8 process,3D printing,and carbon nanotube frameworking will play more important roles in the future owing to their excellent comprehensive capabilities.

Design To Manufacturing Process:Bumpy Road?

如何将设计和生产结合起来,是产品开发和PLM 界热议的话题之一。在现代企业的制造组织里,解决这一关系显得愈来愈重要。要问为什么?我来给个简单的解释——因为这是企业降低成本最核心的一环。公司每生产一个产品。

Diversified manufacturing processes and multiple mineral sources:Features of Warring States bronze vessels excavated from Chutai Cemetery M1,Anhui Province

Northern Anhui was an important region for diverse bronze culture convergence and extensive metal resource circulation in the Pre-Qin Period.In this paper,metallographic microstructure analysis,chemical composition analysis,and lead isotope ratio analysis were conducted on 12 samples of 6 Warring States Period(476–221 BCE)bronze vessels excavated from Chutai Cemetery M1,Fuyang,Anhui Province,revealing the integrated application of diversified manufacturing processes,such as casting,forging,cold working,and welding and multiple metal minerals.The analytical results showed that 2 Ding vessels(鼎)were made by casting,and 2 He vessels(盒)and 2 Dui vessels(敦)were made by forging followed by cold working.These two types of bronze vessels made by different manufacturing processes have significantly distinct alloy ratios and mineral sources,among which the Cu and Sn contents of the 2 cast bronze vessels are lower and the Pb content is higher,while the Cu and Sn contents of the 4 forged bronze vessels are higher and the Pb content is lower.The lead minerals of the two types of bronze vessels might come from Western Henan and the middle and lower reaches of the Yangtze River,respectively.In addition,the 3 pieces of solder used to weld bronze vessels were all made of pure Sn,their metal minerals should come from the densely distributed area of tin ore in Southern China,and Sn solders were mainly discovered in the Chu culture area during the Eastern Zhou Period.

Modular supervisory control for multi-floor manufacturing processes

Due to space availability limitations and high land costs,there is an increasing development of multi-floor manufacturing(MFM)systems in urban and industrial areas.The problem of coordination in a multi-floor manufacturing process,in the Ramadge Wonham framework,is introduced.The manufacturing chain of each floor and the elevator system are modeled in the form of finite deterministic automata.The models of the multi-floor manufacturing process are parametric with respect to the number of floors and the number of manufacturing machines on each floor.The coordination desired performance is formulated in the form of desired regular languages in analytic forms.The languages are realized by appropriate supervisors in the form of finite deterministic automata.The models of the supervisors are also parametric with respect to the number of floors and the number of manufacturing machines on each floor.The total control of the coordination of the multi-floor manufacturing process is accomplished via a modular supervisory control architecture.The complexity of the supervisors as well as the complexity of the total modular supervisory architecture are determined in analytic forms with respect to the number of floors and the number of manufacturing machines on each floor.The special case of a two floor manufacturing process is presented as an illustrative example.

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

An Industrial Perspective on Magnesium Alloy Wheels: A Process and Material Design

Light weights wheels improve vehicle performance with respect to road handling, cornering as well providing fuel economy and reduced greenhouse gas emissions. Aluminum wheels are currently used in many models and are produced usually by low pressure assisted gravity casting. Important property requirements are fatigue strength, pressure tightness, tensile strength, impact resistance, and corrosion resistance. Many attempts have been made to convert aluminum road wheels to magnesium. Race cars and some of the high end models (Porsche, Ferrari, etc.) have used magnesium wheels. These wheels have been gravity cast or forged. Viable corrosion protection systems have been developed and magnesium wheels have been used with success on these models. To use magnesium on more modest models is a challenge due to cost issues. Higher productivity casting processes or more cost effective coating systems need to be utilized. The project consists of selecting magnesium alloys for road wheels, examining the possible cost effective casting processes and corrosion protection systems, evaluating the cost per one wheel and comparing it to aluminum wheel costs. The wheels will also be compared with respect to fatigue and impact properties, pressure tightness, and corrosion.

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.

Cogitation on the digital transformation of the process manufacturing industry triggered by the practices of the PIDAS

Digital transformation is the process of an industry moving to a digital business of continuous growth and improvement with digital technologies.It is an important path for the manufacturing industry to reconstruct and upgrade the existing operation mode to achieve efficiency, effectiveness, and product quality improvement.In this study, we analyze why the successful digital transformation practice is scarcely found in the core business of process manufacturing industries, such as the steel and cement industries.By introducing the development and application of the Process Intelligent Data Application System(PIDAS),which is the digital platform of heavy plates in Baosteel, the concepts, ideas, and development experience of the digital transformation of the process manufacturing are summarized.Moreover, the core issues worthy of attention are pointed out.Finally, a feasible route for the digital transformation of the process manufacturing is proposed.

Consideration of green intelligent steel processes and narrow window stability control technology on steel quality

In order to promote the intelligent transformation and upgrading of the steel industry, intelligent technology features based on the current situation and challenges of the steel industry are discussed in this paper. Based on both domestic and global research, functional analysis, reasonable positioning, and process optimization of each aspect of steel making are expounded. The current state of molten steel quality and implementation under narrow window control is analyzed. A method for maintaining stability in the narrow window control technology of steel quality is proposed, controlled by factors including composition, temperature, time, cleanliness, and consumption(raw material). Important guidance is provided for the future development of a green and intelligent steel manufacturing process.

Decomposition Methods for Manufacturing System Scheduling:A Survey

Manufacturing is the application of labor, tools,machines, chemical and biological processing, to an original raw material by changing its physical and geometrical characteristics, in order to make finished products. Since the first industrial revolution, to accommodate the large-scale production,tremendous changes have happened to manufacturing through the innovations of technology, organization, management, transportation and communication. This work first reviews the highvolume low-mix process by focusing on the quantity production,transfer line and single model assembly line. Then, it reviews the high-volume high-mix process. For such a process type,mixed/multi model assembly line is usually adopted. Hence,two main decisions on them, i.e., balancing and, sequencing are reviewed. Thereafter, it discusses the low-volume high-mix process in detail. Then, technology gap and future work is discussed, and at last, conclusions are given.

Manufacturing cost optimization and estimation combined computer of composite-stiffened panels

The primary goal of this study is to fully grasp the production flow of the new processing technologies for manufacturing composite stiffened panels incorporating cost as one of the design variables early in the design process. An approach is presented to determine the optimum process for cost as objective function. A cost estimation model is established based on the integrally molding process. In the model,the cost drivers which are related to the manufacture processes in terms of material,labor,tool and equipment costs are taken into account. At the same time,estimation software combined computer is developed to aid optimization design. A case of manufacturing composite stiffened panels with T-shaped stiffeners is examined. Excellent agreement shows the optimum process for cost is obtained for the composite stiffened panel with cocuring. It is also revealed that the estimation software combined computer is efficient. The estimation methodology is valid to guide design of the manufacturing process for the composite-stiffened panel.

Magnetic Properties and Workability of 6.5% Si Steel Sheet Manufactured by Siliconizing Process

siliconning process to manufacture 6.5% Si steel sheet has been developed. Electric components, such as transformers and reactors are made easily from 6.5% Si steel sheet. However, improved workability is desirable to increase the applications. Therefore the improvement of workability of 6.5% Si steel sheet was investigated, and the results were obtained as followst: (a) workability of 6.5% Si steel sheet is deteriorated by grain boundary oxidization. (b) grain boundary oxidization can be restrained by the addition of C. Workability and magnetic properties of 6.5% Si steel sheet with C addition are discussed. Furthermore, it was found that the workability of high Si steel sheet was improved remarkably by varging the Si content gradient along the thickness without deterioration of high frequency magnetic properties. This newly developed magnetic gradient high Si steel sheet is also discussed.

The Application of Laser Processing on Clutch Manufacture

The first multi-function laser processing system in the domestic for clutch manufacture,with abilities of cutting, jointing and heat treatment,was reported in this paper.One external optical path,double laser heads,adjust device by manual operation,automatically track were employed in this system Also the other parts of vehicles can be fabricated by this system,as well as clutches.The special processing to manufacture the clutches of heavy vehicles,which was developed by the project of this laser processing system,achieved the international standards and satisfied the economic development and nation defense in the do- mestic.

Effect of Measurement Error on the Multivariate CUSUM Control Chart for Compositional Data

Control charts(CCs)are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades.Measurement errors(M.E’s)are involved in the quality characteristic of interest,which can effect the CC’s performance.The authors explored the impact of a linearmodel with additive covariate M.E on the multivariate cumulative sum(CUSUM)CC for a specific kind of data known as compositional data(CoDa).The average run length(ARL)is used to assess the performance of the proposed chart.The results indicate that M.E’s significantly affects themultivariate CUSUM-CoDaCCs.The authors haveused theMarkov chainmethod to study the impact of different involved parameters using six different cases for the variance-covariance matrix(VCM)(i.e.,uncorrelated with equal variances,uncorrelated with unequal variances,positively correlated with equal variances,positively correlated with unequal variances,negatively correlatedwith equal variances and negatively correlated with unequal variances).The authors concluded that the error VCM has a negative impact on the performance of themultivariate CUSUM-CoDa CC,as the ARL increases with an increase in the value of the error VCM.The subgroup size m and powering operator b positively impact the proposed CC,as the ARL decreases with an increase in m or b.The number of variables p also has a negative impact on the performance of the proposed CC,as the values of ARL increase with an increase in p.For the implementation of the proposal,two illustrated examples have been reported formultivariate CUSUM-CoDaCCs inthe presence ofM.E’s.Onedealswith themanufacturingprocessof uncoated aspirin tablets,and the other is based on monitoring the machines involved in the muesli manufacturing process.

Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

Starting materials, processes and characteristics of bio-based foams: A review

Biofoam products have attracted considerable attention lately because there is a growing demand for green/sustainable products.To this end,various biobased foams have either been developed or are currently in development,e.g.,bio-based polyurethanes(PUs),polylactic acid(PLA),starch,and polyhydroxyalkanotates(PHAs).Indeed,significant progress has been made;however,chal-lenges still persist,for example,biobased foam products have poor processability,inferior com-patibility,thermal and strength properties.In this review,we focus on five biofoam products:namely bio-based PUs,PLA,starch,PHAs,and cellulose biofoam products,along with their prop-erties and performance,as well as their manufacturing processes.Further efforts are still needed to unlock the full potential of these bio-based products and meet the goal of complementing and gradually replacing some of their fossil-based counterparts.Finally,the challenges,as well as arising opportunities of future research directions are discussed.