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General-Purpose Metal-Cutting Machine Tools
Published in Helmi Youssef, Hassan El-Hofy, Traditional Machining Technology, 2020
A shaper machine is commonly used in single-piece and small-lot production as well as in repair shops and tool rooms. Due to its limited stroke length, it is conveniently adapted to small jobs and best suited to surfaces comprising straight-line elements and contoured surfaces when the shaper is equipped with a tracing attachment. It is also applicable for cutting keyways and splines on shafts. Although the shaping process is inherently slow, it is quite popular because of its short setup time, inexpensive tooling, and ease of operation. In comparison to a planer, it occupies less floor space, consumes less power, costs less, is easier to operate, and is about three times quicker in action, as stroke length and inertia forces are lower. Its stroke length is limited to 750 mm, as the accuracy decreases for longer strokes due to ram overhanging. Figure 3.68 shows a typical shaper. The column (1) houses the speed gearbox, the crank, and the slotted arm mechanism. The power is, therefore, transmitted from the motor (2) to the ram (3). Ram travel is the primary reciprocating motion, while the intermittent cross travel of the table is the feed motion. The tool head (5), carrying the clapper box and the tool holder (6), is mounted at the front end of the ram and is fed manually or automatically. The slot with the clamp (7) serves to position the ram in setting up the shaper.
Shaping/Planing Operations and Machines
Published in Zainul Huda, Machining Processes and Machines, 2020
A planer is a machine tool that produces planes and flat surfaces by reciprocating speed motion of the worktable, while the cutting tool has feed motion. A planer is similar to a shaper, but the former is larger and capable of doing heavy jobs. A planer consists of the following parts: bed, table, tool head, cross rail, column, and driving and feed mechanisms (see Figure 8.2). These parts are briefly discussed in the following paragraph.
Reinforcement learning-based dynamic production-logistics-integrated tasks allocation in smart factories
Published in International Journal of Production Research, 2023
Jingyuan Lei, Jizhuang Hui, Fengtian Chang, Salim Dassari, Kai Ding
As shown in Figure 8, there are 6 mfg-SCRs (R1∼R6) and 4 log-SCRs (R7∼R10) in an SF testbed, where R1 and R3 are lathe machines, R2, R4, and R6 are milling machines, and R5 is planer. R0 is the warehouse where raw materials and finished products are stored, and R0 is also the initial position of log-SCRs. The mfg-SCRs execute the production tasks of orders and the log-SCRs transport the work-in-processes among mfg-SCRs. Tasks allocation are decided autonomously and dynamically by negotiation among mfg-SCRs and log-SCRs. In this paper, we assume that the path conflictions of different log-SCRs are ignored. The production tasks are generated in the cloud platform according to the BOM-based decomposition of product orders. The detailed task information is delivered and updated by SCR agents. The SF runs according to the in-situ states of orders and SCR agents. At the same time, the in-situ data of orders are shared in the cloud platform for customers to view.
Bi-objective scheduling on uniform parallel machines considering electricity cost
Published in Engineering Optimization, 2018
YiZeng Zeng, Ada Che, Xueqi Wu
This example comes from a manufacturing company in Shaanxi Province, China, which usually receives an order of 30 annular parts used for metro construction. Each part to be processed needs to undergo a complete processing stage, which takes 13.5 h. To tackle the processing task, multiple CNC planer horizontal milling and boring machines are available, with an electricity consumption rate of approximately 33 kW/h. Further detailed information about this example can be found in Che, Zeng, and Lyu (2016). They validated their algorithm for a single-machine scheduling problem with this example. This work examines the example with multiple parallel machines. All the parallel machines are assumed to operate at the same processing speed and have the same electricity consumption rate. In particular, vj = 1 and pj = 33 kW/h hold for all machines. The aim is to minimize simultaneously the total number of machines actually used and the total electricity cost.