Advanced Manufacturing Using Linked Processes: Hybrid Manufacturing

Hybrid Manufacturing Processes (HMP) can significantly reduce time to customer, waste, and tooling costs per part, while increasing possible part geometric complexity for small batch parts. In the following chapter, HMP is defined by the production of parts produced first with a near-net shape process using methods including: additive manufacturing, casting, injection molding, etc., which is then coupled with multi-axis computer numerical control (CNC) subtractive machining or some other secondary material removal process. Creating process plans for such hybrid manufacturing processes typically takes weeks rather than hours or days. This chapter outlines several hybrid manufacturing processes and the intricacies required to develop process plans for these complex linked processes. A feature-based advanced hybrid manufacturing process planning system (FAH-PS) uses feature-specific geometric, tolerance, and material data inputs to generate automated process plans based on user-specified feature precedence for additive-subtractive hybrid manufacturing. Plans generated by FAH-PS can optimize process plans to minimize tool changes, orientation changes, etc., to improve process times. A case study of additive-subtractive methods for a patient-specific bone plate, demonstrates system capabilities and processing time reductions as compared to the current manual process planning for hybrid manufacturing methodologies. Using the generated FAH-PS process plan resulted in a 35% reduction in machining time from the current hybrid manufacturing strategy

Multi-Axis Planning System (MAPS) for Hybrid Laser Metal Deposition Processes

This paper summarizes the research and development of a Multi-Axis Planning System (MAPS) for hybrid laser metal deposition processes. The project goal is to enable the current direct metal deposition systems to fully control and utilize multi-axis capability to make complex parts. MAPS allows fully automated process planning for multi-axis layered manufacturing to control direct metal deposition machines for automated fabrication. Such a capability will lead to dramatic reductions in lead time and manufacturing costs for high-value, low-volume components with high performance material. The overall approach, slicing algorithm, machine simulation for planning validation, and the planning results will be presented

Part grouping for efficient process planning

A framework to provide automated part grouping has been investigated in order to overcome the limitations found in existing part grouping techniques. The work is targeted at: exploration of criteria for feature-based part grouping to make the process planning activity efficient; determination of the optimal number of part families in the part grouping process; development of an experimental hybrid process planning system (HYCAPP); investigation of the effects of improved part grouping on manufacturing cell design. The research work has explored the creation of a feature-based component data model and manufacturing system capability data model, and checked the limitations inherent in existing part grouping techniques i.e. part grouping: around methods; based on part geometry; based on machining processes; and based on machines. [Continues.

Stereo Vision Based Hybrid Manufacturing of Ti-6Al-4V in Component Repair Process

Parts or products from high performance metal are very expensive, partly due to the processing complexities during manufacturing. Recent studies have indicated that hybrid processes of additive manufacturing and machining process can be used to repair titanium parts, thus extending the service life. In order to implement these methods automatically, it is necessary to obtain the spatial geometry information of component with defects to generate the tool path. The purpose of this paper is to summarize the research on hybrid manufacturing with stereo vision function which can be applied to the component repair process. Stereo vision is adopted to detect the location and the size of the defect area which is marked by color marker. And then laser displacement sensor is applied to scan the defect area. Therefore, automated alignment, reconstruction of the defect area and tool path planning could be implemented based on the spatial geometry information. Finally, a Ti64 part repair experiment is done to verify the method. This work provides an automatic method for repairing damaged parts by hybrid manufacturing.Mechanical Engineerin

Repair of metallic components using hybrid manufacturing

Many high-performance metal parts users extend the service of these damaged parts by employing repair technology. Hybrid manufacturing, which includes additive manufacturing (AM) and subtractive manufacturing, provides greater build capability, better accuracy, and surface finish for component repair. However, most repair processes still rely on manual operations, which are not satisfactory in terms of time, cost, reliability, and accuracy. This dissertation aims to improve the application of hybrid manufacturing for repairing metallic components by addressing the following three research topics. The first research topic is to investigate and develop an efficient best-fit and shape adaption algorithm for automating 3D models\u27 the alignment and defect reconstruction. A multi-feature fitting algorithm and cross-section comparison method are developed. The second research topic is to develop a smooth toolpath generation method for laser metal deposition to improve the deposition quality for metallic component fabrication and repair. Smooth connections or transitions in toolpath planning are achieved to provide a constant feedrate and controllable deposition idle time for each single deposition pass. The third research topic is to develop an automated repair process could efficiently obtain the spatial information of a worn component for defect detection, alignment, and 3D scanning with the integration of stereo vision and laser displacement sensor. This dissertation investigated and developed key technologies to improve the efficiency, repair quality, precision, and automation for the repair of metallic components using hybrid manufacturing. Moreover, the research results of this dissertation can benefit a wide range of industries, such as additive manufacturing, manufacturing and measurement automation, and part inspection --Abstract, page iv

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Integrated automation for manufacturing of electronic assemblies

Since 1985, the Naval Ocean Systems Center has been identifying and developing needed technology for flexible manufacturing of hybrid microelectronic assemblies. Specific projects have been accomplished through contracts with manufacturing companies, equipment suppliers, and joint efforts with other government agencies. The resulting technology has been shared through semi-annual meetings with government, industry, and academic representatives who form an ad hoc advisory panel. More than 70 major technical capabilities have been identified for which new development is needed. Several of these developments have been completed and are being shared with industry

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies