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Automated Manufacturing Systems
Published in Helmi Youssef, Hassan El-Hofy, Traditional Machining Technology, 2020
The main tasks involved in CIM can be separated into four areas (Figure 10.10): Product design, for which an interactive CAD system allows drawing, analysis, and design to be performed. The computer graphics are useful to get the data from the designer’s mind to be ready for interaction (Figure 10.11).Manufacturing planning, where the computer-aided process planning (CAPP) helps to establish optimum manufacturing routines and processing steps, sequences, and schedules.Manufacturing execution, in which CAM identifies manufacturing problems and opportunities. Intelligence in the form of microprocessors is used to control machines and material handling and to collect data controlling the current shop floor (Figure 10.11).Computer-aided inspection (CAI) and computer-aided reporting (CAR), so as to provide a feedback control loop (Figure 10.10).
Due-Date Agreement in Integrated Process Planning and Scheduling Environment Using Common Meta-Heuristics
Published in Rakesh Kumar Phanden, Ajai Jain, J. Paulo Davim, Integration of Process Planning and Scheduling, 2019
Halil Ibrahim Demir, Rakesh Kumar Phanden
Process planning is a bridging function among design and manufacturing, and it affects the scheduling function in terms of generating an effective and optimal schedule (Lee and Kim, 2001). Developments in Computer Aided Process Planning (CAPP) provide convenience while preparing process plans. This led to the use of alternative process plans in the scheduling. Both process planning and scheduling are important manufacturing functions and they are conventionally treated separately (Zhao et al., 2006). Therefore, schedules are generated using a fixed process plan in a traditional manufacturing system. Since the outputs of process planning become inputs of production scheduling function, these two functions should be integrated to get the global benefit. Additionally, it is better to prepare alternative process plans in order to get a better shop floor workload and higher machine utilisation. Researchers have studied the optimisation of schedule and process plans by IPPS Integration of Process Planning and Scheduling to obtain a global benefit. Production scheduling itself belongs to the Non-Polynomial hard class of combinatorial optimisation. So, the cohesiveness of both process planning and scheduling functions becomes even harder to solve and it is difficult to find the correct results within a stipulated and realistic duration. Over the last three decades, the IPPS problem has caught researchers’ attention and numerous studies have been carried out in literature.
Direct digital prototyping and manufacturing
Published in Fuewen Frank Liou, Rapid Prototyping and Engineering Applications, 2019
This section helps answer the following questions: What would it take to provide a fully automatic digital prototyping and manufacturing system?What is computer-aided process planning (CAPP)? What level of part complexity can be handled by current CAPP software?What is feature-based design? Why is it important in digital prototyping and manufacturing?Is it true that feature-based design will only be suitable for some simple solid models?
Development and performance evaluation of a web-based feature extraction and recognition system for sheet metal bending process planning operations
Published in International Journal of Computer Integrated Manufacturing, 2021
Eriyeti Murena, Khumbulani Mpofu, Alfred T Ncube, Olasumbo Makinde, John A Trimble, Xi Vincent Wang
Due to the increase in product variety and mass customisation, Computer-Aided Process Planning(CAPP) (Harik et al. 2008) played an essential role in manufacturing over the past decade. It works has a link between product design and product manufacturing as it generates the procedures to manufacture a product. Manufacturing industries require vastly sophisticated CAPP technologies and methods that merge with the manufacturing systems. To reduce production cycle time and human errors, continuous integration of CAD and Computer-Aided Manufacturing (CAM) are very crucial (VENU and KOMMA 2017). The integration of CAPP with CAD and CAM, the automation of CAPP, data exchange and collaboration, reconfigurable systems offers flexibility to today’s manufacturing industries. Feature Recognition (FR) is a vital key to the automation of CAPP because the information obtained from FR and extraction system is used as input in all the three other stages of CAPP, namely, tool selection, manufacturing sequence and the remote planning, which is the output of the process plan. Feature recognition is the conversion of geometrical data into a feature model. The feature model is what the machine is required to produce.
Automated process planning for turning: a feature-free approach
Published in Production & Manufacturing Research, 2019
Morad Behandish, Saigopal Nelaturi, Chaman Singh Verma, Mats Allard
Computer-aided process planning (CAPP) is the systematic determination of a set of steps by which a product can be manufactured in a cost-effective, competitive manner (Alting & Zhang, 1989). The most common approaches to CAPP are based on automated feature recognition (Babic, Nesic, & Miljkovic, 2008; Shah, Sreevalsan, & Mathew, 1991; Weill, Spur, & Eversheim, 1982; Xu, Wang, & Newman, 2011). These methods include convex volume decomposition (Eftekharian & Campbell, 2012; Kim, 1992; Perng, Chen, & Li, 1990), graph-based heuristics (Fu, Eftekharian, Radhakrishnan, Campbell, & Fritz, 2012; Joshi & Chang, 1988; Wu & Lit, 1996), rule-based pattern recognition (Babic et al., 2008; Vandenbrande & Requicha, 1993), among others. Their main challenge is that the notion of a ‘feature’ is typically defined in an application-specific context (Hoffmann & Shapiro, 2017). There is no consensus on a single unified definition across the various design and manufacturing applications, which limits the usability or success of CAPP tools that commit to a specific taxonomy. Most process planners are rule-based systems that map individual features (e.g., holes, pockets, slots, and so forth) to specialized manufacturing tools and parameters. These methods are a lot more effective when features are clearly separable, but they rarely extend to complex interacting features (Tseng & Joshi, 1994, 1998).
A discrete particle swarm optimisation for operation sequencing in CAPP
Published in International Journal of Production Research, 2018
Nowadays computer aided process planning (CAPP) is widely used in machining industry, owing to its advantage of reducing throughout time and improving part quality. The main function of a CAPP system is to generate suited process plan for producing a part. CAPP is conceived as the key technology to integrate computer-aided design and computer aided manufacturing for computer integrated manufacturing and digital manufacturing. The developed CAPP systems can be generally classified into two classes, namely variant systems and generative systems. Operation sequencing is one of the most important activities in generative CAPP systems, and is to determine the sequence of machining operations required to produce a part. A good sequence of operations can ensure low machining cost and satisfy precedence constraints among the operations that come from geometrical and technological considerations (Guo et al. 2006). Operation sequencing problem in CAPP similar to the transfer line balancing/design problems (Dolgui et al. 2006; Dolgui, Guschinsky, and Levin 2009) is well known as a complicated decision problem for parts with complex structures and features, and is shown to be a nondeterministic polynomial-time hard (NP-hard) problem (Li, Gao, and Wen 2013) Therefore, the operation sequencing problems for parts of many complex geometrical features are computationally intractable to find optimal solutions, and then metaheuristics have been widely used to find near-optimal solutions within an acceptable time. In the following, we will briefly review generic metaheuristics for the operation sequencing or process planning, and then we will survey the applications of particle swarm optimisation (PSO) for relevant problems.