Modeling and Solution Algorithms for Process and Operations Planning with Applications in Manufacturing and Related Sectors

加拿大制造系统必须通过加速采用新技术和新工艺来保持其高科技和经济优势。混合制造是柔性制造系统未来发展的必然选择。它结合了增材制造（AM）和减材制造（SM），并提供了巨大的优势，包括提高精度，速度，质量以及积极的环境影响。该提案的目标是通过开发和测试设计和规划功能来改进HM的决策过程。这将通过实现以下四个目标来实现。第一个目标是开发HM（DfHM）设计指南，将指南与计算机辅助制造（CAM）或数字制造（DM）系统集成，并构建零件并对其进行测试。二是建立了面向制造和再制造问题的HM宏观工艺规划的数学模型和求解方法。为了进一步解释这一点，给定一个新的产品规格，目的是找到一个最佳的分组，方向，和序列的AM和SM过程。该模型和解决方案的算法将与CAM或DM系统集成，并与实际的工业规模的大型制造和再制造问题进行测试。第三个目标是为HM微工艺规划开发数学模型和求解方法，即，找到最佳的HM参数和批量大小，以最大限度地降低加工成本，保持费用，质量成本和环境影响。模型和解决方案将与CAM或DM系统集成，并通过构建实际物理部件进行测试。最后，第四个目标是开发一个模型和一个解决方案的过程中，设计一个平台，在不确定的情况下，并安排制造和再制造的一个家庭的零件。这四个目标的最终目的是将数学模型和求解过程集成到CAM或DM系统中。众所周知，HM正在创造成功的故事。通过进一步的研究，HM可以在工业中引领潮流，特别是在安大略的温莎等工业城市。需要在过去所作贡献的基础上，在这一部门开展更多的调查。

Modeling and Solution Algorithms for Process and Operations Planning in Manufacturing Canadian manufacturing system must sustain its high-tech and economic edge by accelerating the adoption of new technologies and processes. Hybrid Manufacturing (HM) is an inevitable future choice of the flexible manufacturing system. It combines additive (AM) and subtractive manufacturing (SM) and offers great advantages including improvements in accuracy, speed, quality, as well as a positive environmental impact. The goal of this proposal is to improve the decision-making process for HM through the development and testing of design and planning functions. This will be accomplished by achieving the following four objectives. The first objective is to develop a design for HM (DfHM) guidelines, integrate the guidelines with a Computer Aided Manufacturing (CAM) or Digital Manufacturing (DM) system, and build parts and test them. The second is to develop a mathematical model and solution approach for HM macro-process planning for both manufacturing and remanufacturing problems. To explain this further, given a new product specification, the aim is to find an optimal grouping, orientation, and sequence of the AM and SM processes. The model and solution algorithms will be integrated with a CAM or DM system and tested with actual industrial-sized large manufacturing and remanufacturing problems. The third objective is to develop a mathematical model and solution approach for HM micro-process planning i.e., to find the optimal HM parameters and batch size to minimize machining costs, holding expenses, cost of quality, and environmental impact. The model and solution approach will be integrated with a CAM or DM system and tested by building actual physical parts. Lastly, the fourth objective is to develop a model and a solution procedure to design a platform under uncertain circumstances and to schedule the manufacturing and remanufacturing of a family of parts. The ultimate aim of these combined four objectives is to integrate mathematical models and solution procedures into a CAM or DM system. It is known that HM is creating success stories. With further research, HM can lead the way in industry especially for industrial cities like Windsor, Ontario. More investigation needs to be developed in this sector, building on the past contributions.