Search

Plastic injection mould manufacturing is diverse and complex. Traditional mould manufacturing planning often relied on experience and techniques. Therefore, in order not to affect the operation or even cause some loss of the enterprises, geometry information of computer-aided design (CAD) should be converted into manufacturing information of computer-aided process planning (CAPP) and CAM for automatic feature recognition to shorten the lead time. This study is the secondary development in a CAD environment creating a network plastic injection mould manufacturing navigating system and CAD/CAPP for integration and application. As part design is feature-based, each processing step can be regarded as a feature. By applying the hybrid recognition technology of the graph-based approach, the rule-based approach and hint-based approach in the analysis of part feature appearances, it can automatically categorise all the manufacturing features of the part and convert them into corresponding processes. The CAPP developed in this study can convert the part manufacturing features into corresponding processes and plan the part process sequence according to the process priority constraint relations. As proved by the case studies, the use of CAPP in this study can reduce planning time by 87%. It can provide users with a reference regarding process sequence, and speed up the part planning progress. Coupled with the mould manufacturing navigating process, it can achieve automatic design and manufacturing. [ABSTRACT FROM PUBLISHER]

Under the computer-aided design (CAD) software architecture, this study aims to develop navigation processes for plastic injection mould manufacturing scheduling optimisation. Mould manufacturing is a job-shop scheduling problem, with components processing sequence under limited conditions. This study uses the search capabilities of the ant colony system (ACS) to determine a set of optimal schedules, under the condition of not violating the processing sequences, in order to minimise the total processing time and realise makespan minimisation. As the test results suggest, it can save up to 52% of manufacturing time, and also substantially shorten the processing time of the production plan. This study completes the algorithm steps and manufacturing process time estimation by operations on the navigation interface, and uses mould manufacturing scheduling to make optimised arrangements of finished components. The method can comply with the on-site manufacturing processes, improve scheduling prediction accuracy and consistently and efficiently integrate the optimisation scheduling system and mould manufacturing system. Visualised information of the scheduling results can be provided, thus allowing production management personnel to ensure smooth scheduling. [ABSTRACT FROM AUTHOR]

Plastic injection mould manufacturing process is a job-shop scheduling problem (JSSP) and a non-polynomial (NP) problem. This study developed a mould-manufacturing scheduling navigation system in which the genetic algorithm (GA) was applied to optimise the mould-manufacturing scheduling problem. A navigation process ranging for manufacturing process planning and manufacturing scheduling was planned, and scheduling management was planned, while considering the processing restrictions needed to effectively manage and arrange all the components to be processed in a more reasonable way. The study used the searching capabilities of GA for determining the sequence of component processing with limited machines during mould manufacturing, and computed the makespan of the components currently planned in the factory. Compared with the makespan of manual planning in real cases, the proposed system can save up to 50% of the working time, and the quality of the solution can provide production management staff with a better reference in improving actual scheduling jobs. The proposed system can be used to effectively plan and manage component processes, accelerate the scheduling and shorten mould manufacturing time, in order to achieve the objectives of reducing the production cost and improving the competitiveness of an enterprise. [ABSTRACT FROM PUBLISHER]