An integrated knowledge based system for sheet metal cutting–punching combination processing

Cutting and punching are two important processes in the sheet metal industry; the former is flexible in processing whilst the latter is effective in production. To combine the advantages of both, the so-called combination machines that combine the cutting and punching processes are used in sheet metal processing. To support such machines, this paper presents an integrated knowledge based CAD/CAM system for sheet metal cutting–punching combination processing. The whole system consists of five functional modules, i.e., modeling, nesting, process planning, NC-programming and simulation and reporting module. These modules share a unified objected-oriented data structure, so they are integrated seamlessly in terms of data processing. The knowledge base is the core of the integrated system, based on which, the system runs in an efficient and intelligent manner. This system has been successfully applied in industry, and an application example is demonstrated.     

Design considerations of flat patterns analysis techniques when applied for folding 3-D sheet metal geometries

This paper discusses a systematic approach to implement the principles of Flat Pattern Analysis FPA for folding sheet metal products. The paper starts by highlighting the needs for the vehicular structure forming process with respect to the main production line requirements through using Quality Function Deployment QFD matrix. Additionally, the potentials of fold forming for sheet metal parts in achieving the major production needs will then be benchmarked against other forming techniques through a decision making tool namely; the Analytical Hierarchy Process AHP. The study investigates the application of flat pattern tools for sheet metal products derived from analysis for thin or zero thickness sheets (i.e. paper origami). The analysis sets an approach to generate all possible configurations of flat patterns that result in a specific 3-D structure profile. Secondly, a set of optimality selection metrics are developed and applied to these configurations to help determine the most optimized flat pattern. These optimality measures are a metric based on compactness, a metric for nesting efficiency to describe the strip layout planning, and two measures to assess the manufacturing aspect i.e. bending operation in terms of number and orientation of bend lines.     

自适应模糊神经网络在板料弯曲回弹预测中的应用

回弹是板料冲压成形中影响工件质量的重要因素，因为它是一个多变量相互作用的高度非线性问题，至今在解析和数值方法中未能找到一个很有效的解决途径。该文提出利用自适应模糊神经网络(ANFIS)对非线性问题的良好逼近能力，采用基于有限元方法获得训练样本，经训练后得到具有回弹预测能力的ANFIS模型。实验验证了该方法的有效性。     

Intelligent manufacture of laser cutting, punching and bending parts

It is possible to carry out the flexible manufacture of small parts using existing machine tools. Various computer programs and expert systems are required for this purpose. Continuous further development of the installation is possible with expert systems. The most important software component is a CAD system suitable for the input of sheet parts and which is externally accessible, along with expert systems, for the determination of developed projections and bending sequences and also for the generation of laser and punching plans for the sheet blanks. The system described here, for the flexible manufacture of small parts, was designed for best possible compatibility with different machines and adaptibility to new information.     

Evolutionary path planning for robot assisted part handling in sheet metal bending

While the bending sequence planning has been intensively studied, design of the motion path of a sheet metal part in the bending operation tends to be ignored by researchers. Because during the bending operation, the space for maneuvering a sheet metal part is very small, collisions between the part and bending tools are likely to occur. When a robot is used to handle the part, the role of an automatic path-planning tool becomes more significant. In this study, an evolutionary path-planning approach for robot-assisted handling of sheet metal parts in bending is firstly proposed and implemented. The proposed approach globally searches the motion path space to identify feasible paths. Collision detection algorithms based on segment intersection are used to check if the generated paths are feasible or infeasible. This method can automatically design feasible handling operations for a robot. Simulation examples on a simple “V” shaped part and a part with multiple bents demonstrate that the approach is efficient and practical.     

Optimized bending sequences of sheet metal bending by robot

In the present study, a method is proposed which generates bending sequences of a sheet metal part handled by a robot. If parts are handled by a robot, the best grasping positions for each bending and the number of repositions must be indicated in advance. Using the proposed method, feasible bending sequences with grasping positions are obtained and the sequences are sorted in the order of the number of repositions. In generating the sequences, several important features for the sheet metal bending are considered by dividing them into channels, which is one of the base features. The error accumulated during bending operation is calculated for each sequence, and set-up positions can be selected so as to satisfy the preferential tolerance. The proposed method assists the sheet metal process planner by allowing him/her to plan in advance the best bending sequences and to confirm if the robot can perform the handling operation. A computer simulation was written based on the proposed method, and illustrative examples and results of various simulation models are also given.     

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the case in practice.The theory-guided DNN could also be applicable to other sheet metal forming processes.It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.     

In-process angle measurement and control for flexible sheet metal manufacture

A new method of controlling springback in small-radius pressbrake bending operations has been developed. This method provides a more accurate bending process, necessary for the further development of precision, small-lot sheet metal assembly manufacture. The pursuit of this research has led to the development of an inexpensive, high-resolution, on-line angle sensor. In addition, a simplified analytic model of the bending process was developed to predict springback in terms of material and tooling geometry variables. Finally, a springback control system has been developed with demonstrated accuracy of one-third of a degree in right-angle bends for cold-rolled steel samples covering a range of material properties.     

A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop

This paper presents a genetic algorithm-based job-shop scheduler for a flexible multi-product, parallel machine sheet metal job shop. Most of the existing research has focused only on permutation job shops in which the manufacturing sequence and routings are strictly in a predefined order. This effectively meant that only the jobs shops with little or no flexibility could be modeled using these models. The real life job shops may have flexibility of routing and sequencing. Our paper proposes one such model where variable sequences and multiple routings are possible. Another limitation of the existing literature was found to be negligence of the setup times. In many job shops like sheet metal shops, setup time may be a very sizable portion of the total make-span of the jobs, hence setup times will be considered in this work. One more flexibility type arises as a direct consequence of the routing flexibility. When there are multiple machines (parallel machines) to perform the same operation, the job could be routed to one or more of these machines to reduce the make-span. This is possible in situations where each job consists of a pre-defined quantity of a specified product. In other words, same job is quantity-wise split into two or more parts whenever it reduces the makespan. This effectively assumes that the setup cost is negligible. This model has been implemented on a real-life industry problem using VB.Net programming language. The results from the scheduler are found to be better than those obtained by simple sequencing rules.     

Optimization of variable blank holder force trajectory for springback reduction via sequential approximate optimization with radial basis function network

Springback is one of the major defects in sheet metal forming. Variable blank holder force (VBHF) approach is one of the effective ways for the springback reduction. In this paper, the VBHF trajectory is optimized to reduce the springback by a sequential approximate optimization (SAO) with radial basis function (RBF) network. The U-shaped forming in NUMISHEET’93 is employed to determine an optimum VBHF trajectory, for example. In this paper, the bending moment is taken as the objective function. The tearing of sheet during the forming is considered as the design constraint, and the forming limit diagram (FLD) is employed to evaluate the design constraint quantitatively. It has been found from numerical results that the optimal VBHF trajectory can drastically reduce the springback in comparison with various VBHF trajectories. Through the theoretical examination and numerical simulation, the springback reduction of metal forming by the VBHF trajectory is discussed.     

KBS-aided design of tube bending processes

Cold bending of metal tube products is a metal forming process widely used in industry. With today's demand on high quality tube-dependent products, bending of tubing parts has become a precision metalworking operation that requires sophisticated knowledge and experience. In this work, a knowledge-based system (KBS) has been developed to aid the design of tube bending processes, including bending methods selection, tool/die design, and process parameters setup. The object-oriented programming techniques and the goal-driven search mechanism, featured by an interactive graphic user interface, have been applied in development of the proposed KBS. The system developed has shown effective in tube parts production with significantly reduced number of potential defects and failures, such as inaccuracy of bend angles or linear dimensions, wall thinning, flattening, wrinkling, twisting, dents or cracks, etc.     

Logic programming for process planning in the domain of sheet metal forming with progressive dies

In this work an intelligent system pertaining to sheet metal part and process design has been developed, storing knowledge and prescribing ways to use this knowledge according to the ‘programming in logic’ paradigm. The sheet metal parts covered by the software are those having U shape and being manufactured by bending (folding), cutting and piercing with particular emphasis on progressive dies. The use envisaged and corresponding parts of the system are: checking the part design for manufacturability, planning process phases, and checking the configuration of press tools involved. Particular attention is paid to the presentation of knowledge that has been gathered from handbooks and verified / enhanced in industry. This is first presented in natural language and then its formal representation in Prolog is described and explained by examples. Part design and press tool checking knowledge is relatively straightforward to represent and structure ‘linearly’. Process planning knowledge is based on patterns that are captured in lists and activated in a case-by-case fashion exploiting the power of Prolog. Validation of the system was conducted using examples from industry.     

金属板材智能化弯曲信号采集与控制系统

信号采集及控制系统是板材弯曲智能化实验系统的核心部分,它包括对信号的监测、采集,处理与控制,是板材弯曲智能化控制得以顺利进行的技术保障.应用虚拟仪器技术,以LabVIEW作为系统控制软件,研究开发了板材弯曲智能化控制的信号采集与控制系统.该系统采样速率高、操作平台先进、信号处理功能强、集成化程度高.该系统的开发可移植于其他数据采集与控制系统,具有推广价值.     

The design of an automated material handling system for a job shop

This paper describes the application of techniques developed for the management and control of Flexible Manufacturing Systems (FMS's) to a traditional job shop manufacturing sheet metal parts on typical sheet fabricating machines. The problem was the design and evaluation of an automated material handling subsystem (MHS) to support the shop. The MHS controlled the shop loading and floor control by its choice of orders to transport and enter the shop. Detailed simulation models were used to simulate the existing shop, the shop with a MHS, and several algorithms for loading and sequencing orders through the shop. This paper describes the shop, the models developed, the algorithms tested, and the simulation results. A highlight of the study was the ease and speed with which multiple models were developed using an interactive simulation generator, CAPS. Of particular interest is the procedure for shop scheduling, downstream pull, which uses heavily loaded machines to provide sequencing alternatives with the objective of “pulling” work through them to reach more lightly loaded machines. Shop output was estimated to increase from 26 orders per day to 45, a 73% increase, and turnaround time to decrease from ten days to two days.     

Optimisation of press-brake bending operations in 3D space

Sheet metal bending processes are applied in a multitude of mechanical parts. The process involves optimising the sequence of designated bends taking into account the total processing and handling time, avoiding collisions of the sheet metal with tools and machine and respecting the dimensional accuracy constraints of the part. Experienced operators employ expert rules to define possible different feasible sub-optimal sequences that lead to the desired final shape. The expert knowledge is replaced in this work by stochastic search using a classic genetic algorithm. In addition, dimensional accuracy issues are introduced by determining machine stopper positions. Interference detection libraries employed in connection to the search nature of the approach enabled coping with the full 3D problem instead of quasi 2D problems dealt with in literature. Optimum bending sequence, respective suitable bending tools and machine stopper positions for each bending stage result from the algorithm and are demonstrated with examples.     

PREDICTIVE MODELS OF THE SPRING-BACK IN THE BENDING PROCESS

Spring-back is one of the most sensitive features of sheet metal forming processes because the elastic recovery during unloading leads to some geometric changes in the product. Three parameters that are most influential on spring-back in the V-die bending process are sheet thickness, sheet orientation, and punch tip radius. In this research, radial basis function (RBF) neural network and back propagation (BP) neural network approaches are used to predict the spring-back using the data generated from experimental observations. The performances of the models in training and testing sets are compared with those observations. Furthermore, output obtained through regression analysis is used to compare with the two developed model outputs. The results indicate that both networks can be applied successfully for prediction of spring-back against the input parameters of the V-die bending process. Also, the BP model shows better performance for prediction of spring-back with respect to the RBF model.     

一种基于高压水射流的金属板材柔性渐进成形技术

本文提出了一种新型的金属板材无模成形方法,即高压水射流柔性渐进成形方法,并基于此方法设计出一套五轴成形装置,其中喷嘴具有二个旋转自由度,工作台具有三个平动自由度,具有很好的柔性,非常适合多品种小批量产品的生产和新产品的试制。对高压水射流柔性渐进成形装置的各个子系统进行了详细的介绍,并对成形过程提出了一种新的仿真分析方法,将比较复杂的流固耦合问题,简化为加载等效压强的方法。最后通过单点水柱成形的仿真分析,揭示射流压力和板厚对金属板材成形性能的影响。     

A decision support system for machine selection with load balancing

This paper presents a new approach towards the machine selection problem. This approach utilizes a decision support system (DSS) for advising the production manager which machine to select. The DSS is of special kind: its machine selection rules are modified to ensure equal load on the machines. The basic structure of the decision tree is defined by process experts and the rules threshold values are dynamically evaluated.

The implementation domain is the aluminum extrusion process, with the objective of aiding the manager in selecting the appropriate extrusion press machine from a set of similar machines. These machines are the most expensive equipment in the process, and are expected to be fully utilized.

The methodology presented in this paper was implemented in such a facility resulting in about 3% improvement on machine utilization.     

Next generation stamping dies — controllability and flexibility

In sheet metal forming, external energy is transferred to sheet metal through a set of tooling to plastically deform a workpiece. The design of the tooling and its associated forming process parameters play important roles in this manufacturing process since they directly affect the quality and cost of the final product. With increasing demands from customers, government regulations, and global competition, the controllability and flexibility of stamping dies have been challenged. In this paper, we will summarize the research activities conducted at the Advanced Materials Processing Laboratory at Northwestern University in the area of sheet metal forming. An overview of our approach towards the system will be given followed by a summary of individual projects in the areas of failure prediction, design and control of a variable binder force, and the segmented die design with local adaptive controllers.     

一种全自动金属钻杆校直机的设计与实现

设计了一种以ATmega128A 为主控制器的全自动金属钻杆校直系统。系统主要由工件旋转系统、弯曲检测系 统、工件校直系统、校直激光检测系统及主控电路组成。创造性地给出了一种基于触摸屏技术的金属钻杆弯曲检测方法,此方 法是触摸屏技术在弯曲检测领域的创新应用。校直系统采用PID 控制算法,提高了钻杆的校直精度。经过系统仿真和整机实 际测试,达到了预期的设计目标。