Sequence Planning Research Articles

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path.

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

An ontology-based framework to formalize and represent 4D printing knowledge in design

Over the last decade, 4D printing paradigm has received intensive research efforts, whether from researchers in additive manufacturing (AM) or in smart materials (SMs) development. Related research works have thereby generated a large number of ad-hoc solutions with relevant disparate and scattered knowledge. This lack of common core knowledge is mainly due to the multiple involved expertise for fabricating stimulus-reactive structures. The scientific issue of federating and reconciling knowledge is also reinforced especially if such technology must be integrated into the product design process, falling under the field of design for 4D printing. To tackle this challenge, it becomes crucial to formalize and represent knowledge relating AM processes/techniques, SMs behaviours, stimuli and transformation functions with the variety of design objects. In such a context, the paper aims at developing an ontology-based framework for the semantic and logical description of transformable objects in the era of 4D printing for product-process design related purposes. This framework – which is built upon a foundational ontology associated with mereotopology for describing dynamical phenomena called basic formal ontology – consists in introducing a domain ontology equipped with reasoning capabilities supported by description logics for SMs selection and distribution, transformation sequence planning and AM process planning purposes.

Simulation-Based Two Stage Sequencing of Robotic Assembly Operations with Deformable Objects

Automatic generation of assembly sequences for products containing deformable objects, is an important step required for fully automated production. Robotic manipulation with deformable objects involves handling uncertainty related to object deformation which should be accounted for during assembly sequence planning (ASP). This work describes a two-stage sequencing planning of robotic assembly operations with deformable objects. ASP is an NP-hard problem and many heuristics have been developed for solving it. We suggest a method that improves a purely heuristic ASP by using a heuristic stage based on genetic algorithm (GA) and an additional robotic simulation stage. The method was demonstrated with products with deformable objects and promising results were achieved.

Assembly sequence planning method based on knowledge and ontostep

In today's manufacturing environment, the technology of intelligent assembly has developed rapidly, and the technique level of intelligent assembly sequence planning has been improved significantly. However, the current assembly sequence planning always focuses on modern heuristic algorithms with single goals. Although these methods increase the automation level, they still cannot be applied to actual production because they ignore artificial experience and mature knowledge, which are of great value. This paper comprehensively considers the five key factors that affect the assembly sequence, proposes an assembly sequence planning method based on experience and knowledge, and realizes the sequence automatic reasoning by constructing the corresponding domain ontology. In addition, the OntoSTEP tool is used to complete the ontology mapping of geometric model's information, so as to verify the feasibility of the assembly sequence. The method has guiding significance for actual production, such as assisting process designer do assembly sequence planning effectively.

Optimizing a Disassembly Sequence Planning With Success Rates of Disassembly Operations via a Variable Neighborhood Search Algorithm

Disassembly planning is an effective way to recycle valuable parts or materials from end-of-life products. Due to the parts are easily affected by some unpredictable factors in the real disassembly process (such as defective parts and human factors), there may be failure operations that cannot successfully disassemble the corresponding parts in the real world. Therefore, a partial disassembly sequence planning with uncertainty is proposed in the paper, where the success rates of disassembly operations are formulated to model an expected profit-based disassembly sequence planning. Also, a general variable neighborhood search algorithm is developed to optimize the mathematical model. Meanwhile, in the approach, a heuristic procedure is utilized to obtain a high-quality initial solution, and four different neighborhood structures are introduced to improve the disassembly solution. Its superiority and effectiveness are well illustrated by two case studies and comparison with two existing metaheuristics, i.e., artificial bee colony algorithm and genetic algorithm.

Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

The evolution of Industry 4.0 made it essential to adopt the Internet of Things (IoT) and Cloud Computing (CC) technologies to perform activities in the new age of manufacturing. These technologies enable collecting, storing, and retrieving essential information from the manufacturing stage. Data collected at sites are shared with others where execution automatedly occurs. The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process. However, information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern. The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process. An optimal disassembly sequence plan (DSP) can possess valid information to organize the necessary actions in manufacturing. However, finding an optimal DSP is complex because of its combinatorial nature. The genetic algorithm (GA) is a widely preferred artificial intelligence (AI) algorithm to obtain a near-optimal solution for the DSP problem. The converging nature at local optima is a limitation in the traditional GA. This study improvised the GA workability by integrating with the proposed priori crossover operator. An optimality function is defined to reduce disassembly effort by considering directional changes as parameters. The enhanced GA method is tested on a real-time product to evaluate the performance. The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes. The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product. The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

Modeling of Virtual Assembly and Disassembly Process Based on Selective Disassembly

The virtual disassembly and assembly system based on computer virtual reality technology has been rapidly developed and applied. However, the current virtual disassembly system and assembly based on the hierarchy model and association model does not consider the problem of selective disassembly, which leads to inconsistency between virtual disassembly and actual disassembly. Aiming to solve the problem, introduces the concept of skipping disassembly path on the basis of the original disassembly system based on the hierarchical relationship and the association relationship model, and perfects and optimizes the disassembly and assembly structure model based on the hierarchical relationship model and the association relationship model. The sequence planning of the disassembly model after introducing the shipping disassembly path was carried out．And the combination and reduction of disassembling units in the course of disassembly decision was described. Finally, a selective disassembly algorithm based on the association relationship model is established. With the marine oil separator for example verification. The results show that the model can better solve the actual problem of selective disassembly which improved the authenticity and user experience of the virtual disassembly and assembly system, and has great influence on the application development of the virtual disassembly and assembly system.

Ensinar gramática Percursos que se criam, caminhos que se trilham

This article has as framework the inductive approach to grammar teaching, in which the students are expected to discover and to recognize grammar rules through data observation, detection of regularities and generalization. It is based on Ana Maria Brito’s works in which grammar learning is viewed as “discovery learning”. In the first part, the author’s body of work on this subject is revisited, in order to create an unified suggestion of grammar teaching. In a second part, a teaching sequence plan is presented: based on grammar teaching as reflective activity, it shows a consistent and articulated development of learner’s syntactic knowledge from early ages, namely from the 1st cycle of basic education

Construction and implementation of extraction rules for assembly hierarchy information of a product based on OntoSTEP

In the framework of an intelligent assembly information system, the three-dimensional model of a product serves as a critical information source for the system, which also provides initial product feature information for assembly sequence planning. At present, in the process of extracting information from the STEP neutral file, the products are rarely performed at the assembly level. In addition, the characteristic of semantic expression of the same parts included in a product is always ignored. In this paper, based on the theory of ontology, the OntoSTEP model is constructed to extract information from STEP neutral files at the assembly level. A set of extraction rules for the basic corresponding information of the same parts in the assembly are developed. Further, a SWRL rule library is established in Protégé to realize the comprehensive information extraction of the assembly hierarchy for a product from its 3D model, which lays the foundation for the assembly sequence planning.

Disassembly Sequence Planning of Used Smartphone Based on Dual-population Genetic Algorithm

Disassembly Sequence Planning of Used Smartphone Based on Dual-population Genetic Algorithm

Моделювання радіаційної обстановки під час «раннього» демонтажу будівельних конструкцій об’єкта «Укриття»

A model of the “early” dismantling of the building structures of the Shelter object is developed in the ChNPP VRdose Planner program. The ChNPP VRdose Planner software product was developed at the Institute for Energy Technology (IFE), Norway. ChNPP VRdose Planner is a tool for real-time computer simulation of the radiological environment and actions sequence planning. The simulation is performed in a directly existing environment, while optimizing radiation protection, and makes it possible to prepare reports on work plans with dose estimates. Based on experimental data, mathematical modeling of gamma fields was carried out and spatial distributions of the power of gamma-radiation dose over the roof of the central hall of the “Shelter” object ChNPP. The distributions were analyzed to clarify the location of the most intense sources of gamma radiation, the characteristics and locations of the sources of ionizing radiation were determined, a visual display of the change in the radiation field above the roof of the Shelter object is shown. The eastern wall of the central hall is an accumulation of fragments of the reactor core in the vertical plane, located from an altitude mark of 38.0 m to an altitude of 64.0 m — about 26 m high, about 23 m wide and 3 m thick. It has been established that the dismantling of the roll-off from the pipes should be started from the western side of the Shelter, since this provides a lower level of ambient equivalent dose rate. After dismantling the “light” roof and rolling up from pipes, the ambient equivalent dose rate at the 67.5 m mark increases from 1.1 to 2.8 times. The use of the ChNPP VRdose Planner program for planning and performing installation activities, as well as the activities for the maintenance, repair and replacement of the equipment in the radiation-hazardous conditions will allow: make justified decisions about the feasibility of a particular activity; choose the best technical solutions to achieve set goals; calculate or optimize the structure of radiation protection of the personnel. The use of a computer model will allow selecting and optimizing design solutions for the unstable structures deconstruction, verify them for the compliance with the established characteristics and requirements, and create an interactive software package for the personnel training.

Cyber-physical assembly system-based optimization for robotic assembly sequence planning

Robotic assembly plays a principal role in intelligent manufacturing and Industry 4.0. Well-informed coordination between the robotic arms and the control modules is of paramount importance in the design and planning of automated systems. Given the time-extensive nature of assembly sequence planning and the need for labor-intensive coding and coordination, knowledge-based automated systems are much-needed tools to help facilitate the development of intelligent manufacturing systems. This research puts forward a Cyber-Physical Assembly System (CPAS)-based metaheuristic for fully-automated assembly sequence planning taking into account the physical characteristics of robotic arms. Illustrative case examples are provided to evaluate the practicability of the proposed approach. It is shown that the proposed approach generates better assembly sequences compared to the traditional methods and that the single-arms robots can execute the resulting assembly plans effectively. The numerical analysis confirmed that the CPAS is more efficient than the existing interactive method most commonly applied for assembly sequence planning. Overall, the proposed framework sets the stage for more effective and fully automated planner-robot interaction in the assembly sequence planning context.

A multi-objective genetic algorithm for assembly planning and supplier selection with capacity constraints

This study evaluates the supplier selection problem encountered when using multiple assembly plants with production capacity constraints to produce multiple products. It also applies assembly sequence planning (ASP) and assembly task assignment to production planning for the overall supply chain to determine optimal supplier combinations and production resource allocation. A multi-objective optimization mathematical model was constructed and a modified multi-objective algorithm was used to solve the optimization model. Developed on the basis of the nondominated sorting genetic algorithm II (NSGA-II), this modified algorithm incorporates an algorithmic and determination mechanism involving the greatest number of following tasks and the longest task time when initial solutions are generated. This increases the efficiency of solutions derived from the NSGA-II, here referred to as the W-NSGA2. The W-NSGA2 is then applied to the actual case of a faucet assembly task, and solutions derived by the W-NSGA2 are compared with those derived by the NSGA-II and the nondominated sorting particle swarm optimizer (NSPSO). Results show that the W-NSGA2 yields better solutions.

Electric Vehicle Battery Disassembly Sequence Planning Based on Frame-Subgroup Structure Combined with Genetic Algorithm

Since the electric vehicle battery (EVB) is wildly recycled in industry, the disassembly procedures of variable EVBs is so important that can influence the efficiency and environmental impacts in remanufacturing. To improve disassembly efficiency in EVB remanufacturing, a disassembly sequence planning method based on frame-subgroup structure is proposed in this paper. Firstly, the improved disassembly relation hybrid graph and disassembly relation matrix are proposed to identify the disassembly precedence relationship and connection relationship between the components in EVB. Secondly, the frame - subgroup structure is given, and the method for solving disassembly sequence planning with frame-subgroup structure and genetic algorithm is introduced. In this method, to simplify the series of processes such as encoding, decoding, crossover and mutation, the solution space composed of all disassembly sequences is transformed into the positive integer sequence for the disassembly efficiency in battery remanufacturing. Finally, the case study of EVB disassembly sequence planning is presented to validate the feasibility of this proposed method. Comparing with other traditional methods, the advantage and application of this proposed method are introduced.

A hierarchical parallel multi-station assembly sequence planning method based on GA-DFLA

In recent years, the parallel assembly sequence planning (PASP) is put forward to accommodate the development of greater variety in the mass-customization production. Although many researchers have made contributions to assembly sequence planning (ASP), how to construct parallel multi-station ASP model to effectively solve the integrated ASP and assembly line balancing (ALB) problem need to be further investigated. In this paper, a hierarchical parallel multi-station assembly sequence planning method based on the genetic algorithm and discrete frog leaping algorithm (GA-DFLA) is proposed to solve the integrated parallel multi-station ASP and ALB problem. The assembly information datasets of the model based definition (MBD) including parts information, hierarchical information, matrix information and resource information are defined firstly. Then the hierarchical structure tree is constructed according to the divided assembly units. The hierarchical parallel multi-station assembly sequence planning is carried out from bottom to top in hierarchical structure tree model using the GA-DFLA. The fitness function with feasibility index, time cost index and assembly line balance index, is proposed to determine a more appropriate solution. Finally, the rear independent suspension is taken as an example to validate this method. The results show that the time of parallel multi-station model is at least 35.27% less than the time of serial multi-station model.

Disassembly Sequence Planning: A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Sequence optimization of machining elements for process model based on the genetic algorithm of matrix constrained

Process sequence planning is the permutation and combination orienting to product manufacturing process, which plays an important role in improving the manufacturing produce efficiency and reducing the manufacturing cost. In this paper, an improved constraint matrix method is proposed to transform the relation between manufacturing features into the constraint relation between machining elements, and to determine the sequence coefficient of machining elements according to the correlation relation of manufacturing features. Through constructing the interactive associative relationship of sequence constraint matrix and combined constraint matrix, the initial population with high adaptability is selected out, and can improves the adaptability of initial population of genetic algorithm by using the process sequencing algorithm of improved constraint matrix. In order to improve the convergence speed, the accuracy and efficiency of process sequencing, the genetic algorithm of elite preserving is used.

4R program results: What is the impact of 4R care delivery model on patient self-management and which patients benefited?

139 Background: We previously proposed a 4R model of teamwork and patient self-management (pSM) in cancer care (NCI ASCO Teams Project, Trosman JOP 2016). 4R (Right Info/Care/Patient/Time) enables patient and care team to manage complex care with an innovative 4R Care Sequence plan, including a novel visual feature describing timing and sequence of care. We report final results of a program which tested 4R at 5 safety net and 5 non safety net US centers from 2016 to 2019. Methods: Patients with stage 0-III breast cancer received 4R plans (4R Cohort). We surveyed 4R cohort and a historical control cohort of patients receiving care at same centers pre-4R. We assessed the usefulness of 4R to the 4R cohort and the impact on pSM in 4R cohort compared to historical cohort. Results: Survey response rates: 63%, 422/670 (4R cohort); 47%, 466/992 (control). 4R significantly increased the composite pSM score and 5 of 7 pSM metrics vs control (Table). The increase was not influenced by patient age, stage or whether treated at safety net site. pSM scores increased in 4R vs control cohort to a similar extent for patients < 65 years old (74% vs 51%, p = .0001) as for patients ≥ 65 years old (78% vs 57%, p = .0002). pSM improved similarly for patients with stage 0 or I breast cancer (77% vs 56%, p = .0001) as for patients with stage II or III breast cancer (72% vs 54%, p = .0001). Safety net patients showed pSM increase (77% vs 51%, p = .0001) similar to non safety net patients (74% vs 58%, p = .0002). Within the 4R cohort, 80% found 4R useful for organizing care and 70% found the novel visual feature useful to manage care timing and sequence. Usefulness was similar for age groups and stages, but higher for safety net than non safety net patients (88% vs 74%, p = .0008). Conclusions: 4R markedly improved patient self-management in early breast cancer across age groups and cancer stages, but further enhancements are needed to benefit as many patients as possible. 4R benefits in safety net setting indicate that 4R may reduce disparities. [Table: see text]

4R program results in breast cancer: The impact of 4R Care Delivery Model on timing and sequence of guideline recommended care.

15 Background: We previously proposed a 4R care delivery model which enables patients and care teams to manage timing and sequence of interdependent, time sensitive care with a novel multimodality 4R Care Sequence plan (NCI ASCO Teams Project; Trosman, JOP 2016). We report final results of a program which tested 4R at 10 US centers (4 academic and 6 non-academic) from 2016 to 2019. Methods: 4R Sequences were provided to stage 0-III breast cancer patients (4R cohort, N = 422) at participating centers. Analyses of clinical and patient-reported data compared the 4R cohort to a historical control cohort of patients who received care pre-4R at the same centers (N = 466). Results: We significantly improved 5 guideline recommended referral metrics and 4 referral completion metrics indicating receipt of care by patients who were referred (Table). Although significantly increased, referrals to dental visit and smoking cessation before treatment remained low (< 20% and < 10% respectively). Patient survey comments indicated that insufficient lead time to quit smoking or obtain fertility consult before cancer treatment was a key barrier for completing these referrals. Conclusions: 4R markedly improved referral and receipt of interdependent guideline recommended breast cancer care for most metrics. Future 4R program should optimize the timing of referrals within the 4R Care Sequence to allow sufficient time for smoking cessation and fertility care before treatment initiation. [Table: see text]

Joint optimization of job scheduling and maintenance planning for a two-machine flow shop considering job-dependent operating condition

Two-machine flow shops are widely adopted in manufacturing systems. To minimize the makespan of a sequence of jobs, joint optimization of job scheduling and preventive maintenance (PM) planning has been extensively studied for such systems. In practice, the operating condition (OC) of the two machines usually varies from one job to another because of different processing covariates, which directly affects the machines’ failure rates, PM plans, and expected job completion times. This fact is common in many real systems, but it is often overlooked in the related literature. In this study, we propose a joint decision-making strategy for a two-machine flow shop with resumable jobs. The objective is to minimize the expected makespan by taking into account job-dependent OC. We consider two situations. In the first situation, where the failure rate of a machine under a fixed OC is constant, a hybrid processing time model is proposed to obtain the optimal job sequence based on the Johnson's law. For the second situation, where the failure rate of a machine is time-varying, the job sequence and PM plan are jointly optimized. An enumeration method is adopted to find the optimal job sequence and PM plan for a small-scale problem, and a genetic algorithm-based method is proposed to solve a large-scale problem. Numerical examples are provided to demonstrate the necessity of considering the effect of job-dependent OC and the effectiveness of the proposed method in handing such joint decision-making problems in manufacturing systems.