Recognizing manufacturing features from a design-by-feature model

This paper presents a methodology for recognizing manufacturing features from a design feature model. The common feature-feature interacting relationships are categorized. A feature recognition processor first translates the design feature model of a part into an intermediate manufacturing feature tree by handling design features according to their properties and the interacting relationships between features. Through combination, decomposition, and (tool approach direction) TAD-led operations, alternative interpretations of manufacturing feature model for the part are then generated, and the manufacturing feature tree is updated and extended with AND/OR operators to store these interpretations. Finally, a single interpretation with the lowest machining cost will be selected in the manufacturing feature tree. The proposed processor can support a dynamic and effective recognition process of manufacturing features during the design stage of a part. By defining the interactions between volumetric features elaborately, and utilizing design features and auxiliary information, the processor can recognize manufacturing features from complex parts. The processor recognizes not only some essential manufacturing features but also replicate, compound and transition features defined in STEP. The alternative interpretations can be used for a generic manufacturing application environment.     

Computerized materials handling and facility layout design

We are proposing a systematic approach to the design of materials handling (MH) system that includes an algorithm to select the most suitable equipment. Initially, a conveyor system is proposed while determining its association cost. To seek improvements of the system in terms of cost and equipment utilization, the possibility of supplementing or replacing the conveyor with one or more fork-lift trucks (trucks) will be investigated. The final MH system configuration will consist of conveyors and trucks so that total system optimization will be achieved. The literature on MH design is very limited and often this problem is not directly coupled with the layout problem [4]. The model proposed uses the results of a tentatively designed layout and two important characteristics of the materials, namely size and weight. Facility in this paper refers to a manufacturing plant with low to medium production volume in a process oriented environment. Facility layout and materials handling are two highly interdependent problems. The location of departments, the corresponding distances and production rates dictate the cost of MH. On the other hand, MH costs between departments, influence the departmental arrangements [10]. An iterative process is developed between the layout design and MH selection problems.     

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing system paradigm A new mathematical cost model is proposed for assessing the cost-benefit analysis of introducing Industry 4.0 elements to the manufacturing facility, specifically, integrating and connecting external suppliers as strategic partners and establishing an infrastructure for communicating information between the manufacturing company and its strategic suppliers. The mathematical model takes into consideration the bi-directional relationship between hourly rates and total hours assigned to workcentres/activities in a certain production period. A case study, from a multinational machine builder, is developed and solved using the proposed model. Results suggest that though an additional cost is required to establish infrastructure to connect suppliers, the responsiveness and agility achieved resulting from uncertainty outweighs the additional cost.     

A study for optional inspection policies in a flexible manufacturing cell

The objective of the paper is to determine the location of inspection stages in order to minimize the expected total manufacturing cost while satisfying the high-quality product demand of customers in a flexible manufacturing cell (FMC). This allows a designer to examine alternative designs in order to select those which are optimal, or near optimal, in manufacturing cost terms. To accomplish this objective, we have formulated a total manufacturing decision model consisting of machine set-up and processing costs. Inspection set-up and inspection costs, and cost due to defective. A case study and sensitivity analysis are presented to demonstrate the application of the decision model developed.     

Integrated product and process designenvironment tool for manufacturing T/R modules

We present a decision-making assistant tool for an integrated product and process design environment for manufacturing applications. Specifically, we target microwave modules that use electro-mechanical components and require optimal solutions to reduce cost, improve quality, and gain leverage in time to market the product. This tool will assist the product and process designer to improve their productivity and enable them to cooperate and coordinate their designs through a common design interface. We consider a multiobjective optimization model that determines components and processes for a given conceptual design for microwave modules. This model outputs a set of solutions that the Pareto optimal concerning cost, quality, and other metrics. In addition, we identify system integration issues for manufacturing applications, and propose an architecture that will serve as a building block to our continuing research in virtual manufacturing applications.     

Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes

Polarizers are one of the key parts of Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD), and their production requires high material costs. How to reduce manufacturing costs is thus a key task in this highly competitive global market. The precise yield forecast model considering learning effects that is proposed in this work is believed to be an effective approach to reduce both the raw material input-cost and inventory cost of overproduction. Support vector regression (SVR) model is one of the commonly used approaches to forecast the yield trend. However, in the early manufacturing stages for a new product, an SVR model is usually sensitive and unstable because of the use of insufficient data. Faced with this problem, this research aims at enhancing the SVR model by using past manufacturing experience and virtual samples to estimate the yield trend model for pilot products. This paper proposes a novel Quadratic-Curve Diffusion (QCD) method, wherein we derive a quadratic yield function (QYF) of the new manufacturing process for each key manufacturing variable by utilizing past manufacturing experience; and then use the QYF to generate virtual samples to assist building the overall yield forecast model of the new manufacturing process. The results show that the proposed method is superior to the performance of other forecast and virtual sample generation models.     

Mathematical model for quality cost optimization

Quality engineering uses robust design in order to improve quality by reducing the effects of variability. Variability of the product can be reduced by two stages. One is parameter design which is adjustable to the nominal value so that output is less sensitive to the cause of variability. Other one is tolerance design which is to reduce the tolerance in order to control variability. All costs incurred in a product life cycle can be divided into two categories—manufacturing cost before the sale to the customer and quality loss after the shipment of the product to the customer. It is very important to find the optimum tolerances for each of the characteristics. A balance between manufacturing cost and quality loss should be arrived at in the tolerance design for quality improvement and cost reduction. For the case of Nominal-The-Best, a mathematical model is developed in order to determine the optimum product tolerance and minimize the total cost which includes the manufacturing cost and the quality loss. Since the process capability index (C_pm) shows the balance of quality responsibility between the design and the manufacturing engineers, this is taken as the basis in developing the functional relationship between the variability of the product and the tolerance. Based on these relationships, the total cost of model can be expressed as a function of product tolerance from which the optimal tolerance limits can be found out. Finally, using this model a tolerance design approach that increases the quality and reduces the cost can be achieved in the early stages of the product process design stage itself.     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

An algorithm for simultaneous stabilization using decentralizedconstant gain output feedback

An algorithm which solves, numerically, the simultaneous stabilization problem using a constant gain decentralized control law is presented. The algorithm is determined from the necessary conditions for minimizing an optimal control problem. The optimal control problem consists of n plant models each with its own cost function. The costs are summed to create an average cost function and equality constraints are added to yield the decentralized control structure. The algorithm can start with any stabilizing full state feedback gain for each model and will converge to the optimal constant feedback gain for all models assuming a solution exists. Examples of the algorithm are given for Kharitonov synthesis and optimal gain scheduled control law synthesis using output feedback     

Investigating impact of the order activity costing method on product cost calculations

Inaccuracies in calculated product costs have existed since the development of costing systems. A key contributor to the issue is the use of inappropriate bases for the application of overhead costs. This research proposes and provides preliminary evaluation in a virtual environment for a new allocation base that is believed to be better matched to the consumption rate of the indirect costs being allocated. Using a generalized manufacturing operational framework incorporating multi-period simulation, this research investigates the relationship between allocated cost categories and production or sales order activity. The existing cost allocation methods of full absorption and activity based costing (ABC) are used for comparison to the proposed method. Results show that at the aggregate reporting level (that is, income statement), the use of sales order or production order activity as an allocation base tracks closely with performance levels experienced using more traditional allocation bases. However, the results indicate that the impact on calculated product costs would influence decision making within a firm in terms of sales emphasis, mix, and markets in which to expand and from which to exit. This approach toward cost allocation would be equal to other Enterprise Resource Planning system based solutions in terms of simplicity of maintenance while offering product cost accuracies relatively equal with unit-level focused ABC systems, without requiring the substantial maintenance costs.     

Production planning in additive manufacturing and 3D printing

Additive manufacturing is a new and emerging technology and has been shown to be the future of manufacturing systems. Because of the high purchasing and processing costs of additive manufacturing machines, the planning and scheduling of parts to be processed on these machines play a vital role in reducing operational costs, providing service to customers with less price and increasing the profitability of companies which provide such services. However, this topic has not yet been studied in the literature, although cost functions have been developed to calculate the average production cost per volume of material for additive manufacturing machines.In an environment where there are machines with different specifications (i.e. production time and cost per volume of material, processing time per unit height, set-up time, maximum supported area and height, etc.) and parts in different heights, areas and volumes, allocation of parts to machines in different sets or groups to minimize the average production cost per volume of material constitutes an interesting and challenging research problem. This paper defines the problem for the first time in the literature and proposes a mathematical model to formulate it. The mathematical model is coded in CPLEX and two different heuristic procedures, namely ‘best-fit’ and ‘adapted best-fit’ rules, are developed in JavaScript. Solution-building mechanisms of the proposed heuristics are explained stepwise through examples. A numerical example is also given, for which an optimum solution and heuristic solutions are provided in detail, for illustration. Test problems are created and a comprehensive experimental study is conducted to test the performance of the heuristics. Experimental tests indicate that both heuristics provide promising results. The necessity of planning additive manufacturing machines in reducing processing costs is also verified.     

Structural optimization of modular product families with application to car space frame structures

This paper extends classical structural optimization from single-product optimization to optimization of a whole family of products that have common modules. It integrates the family commonality problem with the finite element models of the structures. A general mathematical frame where optimization is seen as a balance between cost and performance is given. The most obvious cost function is mass, while performance is taken to be a weighted sum of compliances. As a case study, a car product family consisting of three products is presented. These three products are a base model, a seven-seat version, and a pickup version. The study shows how optimal results are effected by requiring modules to be shared between products. Loads emanating from prescribed acceleration fields that simulate crash situations are used. This is a proof-of-concept paper which is a first step toward including more general manufacturing costs than mass and performance measures other than compliance.     

基于FTRL和XGBoost算法的产品故障预测模型

随着智能化设备的日益更新和计算机储存数据能力的提升,制造业企业在其产品制造过程中产生了大量的流水线数据,如何充分利用这些数据一直是工业界的一个难题.本文根据制造业企业的真实大规模生产数据,通过对其进行细致的探索性数据分析,建立了一种基于FTRL和XGBoost算法的二分类产品故障预测模型,并根据适用于非平衡数据集的MCC （Matthews Correlation Coefficient）评价指标采用交叉验证方法对其进行优化.实验结果表明,该模型对于大规模（不仅样本量大,特征量也很大）正负样本非平衡的生产流水线数据集具有运行效率高,故障预测精度高的效果.基于此模型我们可以构建更智能的产品故障检测系统,有效降低企业运营成本的同时也带来了可观的利润增长.     

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things (IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.      

Design synthesis of machining systems using co-platforming

Modern manufacturing environment is characterized by frequent product design changes in order to satisfy evolving customer requirements. Various strategies are implemented in order to efficiently manage the consequences arising from the product design changes starting from product design to planning and manufacturing. This paper focuses on synthesizing manufacturing system using the co-platforming concept which maps product platform features and components to the manufacturing system candidate platform machines. A matrix-based mapping model is proposed in order to determine the candidate platform and non-platform machines. Product-related characteristics including manufacturing features, feature orientation, dimensional and geometrical tolerance, cutting power requirements, workpiece volume and surface finish are considered. Characteristics of machines in the manufacturing system include machining axes, accuracy, working envelop and available power. A case study adopted from an automotive engine cylinder block manufacturer is used for demonstrating synthesizing manufacturing systems, based on co-platforming, which are capable of adapting to new products variants without changes to the platform machines. This prolongs the life of the manufacturing system and reduces costs associated with retooling and replacing it.     

Cost performance dynamics in lean production leveling

Balancing of production systems is one of the main lean manufacturing principles as it reduces in-process storage and related forms of waste. A dynamic systems approach is proposed to investigate challenges of implementing production leveling and associated costs. A lean cell producing at takt time is modeled using system dynamics. The model captures various lean tools influencing production leveling and their implications. Comparative cost analysis between various leveling implementation policies for stochastic demand with multiple products is conducted. Results showed that determining the most feasible leveling policy is highly dictated by both the cost and limitations of capacity scalability. In addition, delivery sequence plans of different products/parts needed to achieve mix leveling and lot sizes affect the feasible production leveling policy while implementing lean principles. The developed model and insights gained from the results can help lean manufacturing practitioners to better decide when and how to implement production leveling as well as determine both production lots sizes and sequence. They also emphasize the importance of cost analysis as assisting decision support tool in the trade-off required between the benefits of different levels of lean policies and their associated cost.     

An improved algorithm for layout design in cellular manufacturing systems

Layout has a significant role on the efficiency of manufacturing systems, but it has not received attention of researchers in comparison to cell formation in cellular manufacturing systems. In this paper, a mathematical model for facility layout in a cellular manufacturing system is proposed that minimizes both inter-cell and intra-cell material handling costs. A variant of simulated annealing algorithm is developed to solve the model. The developed algorithm produces solutions with better quality and less computation time in comparison with the benchmarked algorithm. The superiority of the algorithm in computation time is considerable especially when the size of the problem increases.     

集成电路技术的发展

回顾了集成电路技术的发展历史,展望了集成电路未来的发展趋势。在微电子技术诞生和发展过程中具有一些里程碑式的发明,如:晶体管、集成电路、集成电路平面工艺、MOS器件、微处理器、光刻技术、铜互连工艺的发明等。集成电路技术一直并将继续以特征尺寸缩小、集成度提高的模式,按摩尔定律预测的指数增长率发展。随着器件特征尺寸逐渐缩小并逼近其物理极限,集成电路技术的发展将受到来自于材料、工艺和物理基础等方面的挑战,呈现出多维发展的趋势,这些挑战涉及了微电子学的理论基础。     

An optimal batch size for a production system operating under periodic delivery policy

A manufacturing system that procures raw materials from suppliers in a lot and processes them into a finished product is considered in this research. An ordering policy is proposed for raw materials to meet the requirements of a production facility which, in turn, must deliver finished products demanded by outside buyers at fixed points in time. First, a general cost model is formulated considering both raw materials and finished products. Then, using this model, a simple procedure is developed to determine an optimal ordering policy for procurement of raw materials, and the manufacturing batch size, to minimize the total cost of meeting customer demands in time. The dependent relationships between production batch size and rawmaterial purchasing quantity, and various delivery patterns considered in recent literature are critically reviewed. The quality of the solution is evaluated. Numerical examples are provided.     

Petri net techniques for process planning cost estimation

The problem under consideration is the cost estimation of operation sequencing for nonlinear process planning, i.e. taking into consideration processing alternatives. In order to determine overall costs for feasible process plans, we take into account in our Petri net model of manufacturing process planning the costs caused by machine, setup and tool changing in addition to the pure operation cost. We present two modelling and cost estimation techniques based on Petri nets. Both are based on a new Petri net model: the PP-net (Process Planning net) which represents manufacturing knowledge in the form of precedence constraints and incorporates the cost of machining operation in each operation transition. The first method is based on building a complex Petri net called PPC-system (Process Planning Cost system) by integrating the PP-net and separate Petri nets describing the costs of machine, setup and tool changing. The second method proceeds in the cost calculation by attaching a specific data structure to each PP-net transition which describes the associated machine, setup and tool for the operation modelled by that transition. We apply the developed methods and calculate the optimum process plan to an industrial case study of a mechanical workpiece of moderate complexity.