Generation Of Process Plans Research Articles

Planning with mental models – Balancing explanations and explicability

Human-aware planning involves generating plans that are explicable, i.e. conform to user expectations, as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. To achieve this, we conceive a first-of-its-kind planner MEGA that can reason about the possibility of explaining a plan in the plan generation process itself. We will also explore how solutions to such problems can be expressed as “self-explaining plans” – and show how this representation allows us to leverage classical planning compilations of epistemic planning to reason about this trade-off at plan generation time without having to incur the computational burden of having to search in the space of differences between the agent model and the mental model of the human in the loop in order to come up with the optimal trade-off. We will illustrate these concepts in two well-known planning domains, as well as with a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

A Dynamic Emergency Plan Generation Method Considering Different Situations and Limited Resources

When an emergency event occurs, emergency plans are usually employed to respond the emergency situations in order to prevent or mitigate possible losses of life and property. Therefore, emergency plans play an important and indispensable part in emergency decision-making. With regard to the question of how to generate emergency plans, extant studies have discussed the problem from various perspectives, and fruitful results have been obtained. Dynamic evolution is a typical feature of emergency events, and usually involves the updating of related information regarding an emergency event. Existing studies have considered dynamic evolution during the emergency plan generation process only from a single perspective, neglecting the related need to update information as dynamic evolution occurs. Information related to the emergency event plays a vital role in the emergency plan generation process, and needs to be considered. To overcome these limitations, the present study proposes a novel dynamic emergency plan generation method based on integer linear programming, which considers different emergency situations and limited resources. An illustrative example and descriptive comparisons are provided to demonstrate the novelty, superiority, and validity of the proposed method.

Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks

Industry 4.0 calls for highly autonomous manufacturing process planning. Significant effort has been devoted over the years to generative Computer Aided Process Planning (CAPP), which aims to generate process plans for new designs without human intervention. This goal has not been realized to date due to several reasons, such as poor scalability and the difficulty in modeling manufacturing process capability, which encapsulates the part shape, quality, and material property transformation capabilities of the process. In our prior work, the shape transformation capabilities of lathe-based machining operations were modeled as latent probability distributions using a data-driven deep learning-based generative machine learning approach, from which visualizations of realistic machinable features could be sampled to assist manual process selection. In this paper, a Siamese Neural Network (SNN) is integrated with Autoencoder-based deep generative models of machining operations to enable automated comparison of the query part shapes with sampled outputs. This enables automated manufacturability analysis and machining process selection necessary for generative CAPP. The paper also demonstrates that the proposed Autoencoder and Siamese Neural Network (AE-SNN) achieves a class-average process selection accuracy of 89 %, and a manufacturability analysis accuracy of 100 %, which outperforms a discriminative model trained on the same dataset.

A Comparative Study of Three NSGA Versions for the Multi-Objective Sustainable Process Plans Optimization in RMS

Reconfigurable manufacturing system (RMS) is a paradigm that considers many of the issues the economy and society face today. This paper addresses an environmental-oriented multi-objective process plan generation problem in a reconfigurable manufacturing context. Four objective functions are minimized: the total production cost, the total production time, the amount of greenhouse gas emitted by machines, and the hazardous liquid wastes. To solve the problem, three adapted versions of the well-known non-dominated sorting genetic algorithm (NSGA) approach, namely the penalty boundary intersection (PBI) reference point (RP) with the NSGA-II (PBI-R-NSGA-II), NSGA-III with a selection and elimination operator (SE-NSGA-III), and NSGA-III with the similarity coefficient (SC-NSGA-III) are evaluated and compared. Moreover, the impacts of perturbation ratio on SC-NSGA-III's performance as the number of iterations increases is examined. Finally, the efficacity of the three approaches is demonstrated by rich experimental results and analyzes using two metrics, respectively cardinality of the mixed Pareto fronts (CMPF) and max Pareto front error (MPFE).

Sketching a Generative Model of Intention Management for Characters in Stories: Adding Intention Management to a Belief-Driven Story Planning Algorithm

Previous work on story planning has shown success in the generation of plans that are both intention-coherent and demonstrate aspects of inter-character conflict. However, the initial models of intention and conflict have been limited, in that they lack methods to generate story plots wherecharacters drop sub-plans to achieve their goals in believably consistent and expressive ways and adopt new sub-plans in the face of plan failure. In current work, we have developed models of failed actions in stories that go hand in hand with erroneous belief models for character. Motivated by characterizations of rational agents' intentions as choice combined with commitment, we provide a framing of the plan generation process that is intended to show how characters form their own plans to achieve their own goals, act upon those plans until they feel that conditions no longer support their plans, and then re-plan in the face of adversity to achieve their goals. We show an example story plan that contains several types of character-based intention dynamics targeted by our approach.

Sustainable reconfigurable manufacturing system design using adapted multi-objective evolutionary-based approaches

Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today’s competitive markets. This paper aims to solve an environmental-oriented multi-objective reconfigurable manufacturing system design (i.e., sustainable reconfigurable machines and tools selection) in the case of a single-unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimized respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines, and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well-known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA), and random weighted genetic algorithms (RWGA). To show the efficiency of the four approaches, several instances of the problem are experimented, and the obtained results are analyzed using three metrics respectively hypervolume, spacing metric, and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators (crossover and mutation) on the convergence of the adapted NSGA-III are analyzed. Finally, the TOPSIS method is used to help the decision-maker ranking and select the best process plans.

A simulation-based optimisation framework for process plan generation in reconfigurable manufacturing systems (RMSs) in an uncertain environment

ABSTRACT Reconfigurable manufacturing system (RMS) is a manufacturing paradigm which is proven to be time and cost-effectively adaptable to a wide range of market changes due to its customisable capacity and functionality. In this paper, a two-step framework is proposed for the process plan generation in RMS. The first step, aimed at solving the multi-objective part-family Single-Unit Process Plan (SUPP) generation problem, involves a comparative approach using three metaheuristics, namely: The Non-Dominated Sorting Genetic Algorithm (NSGA-II), the Archived Multi-Objective Simulated Annealing (AMOSA) and the Multi-Objective Particle Swarm Optimisation (MOPSO) as well as a simulation-based optimisation method. The second step is designed to solve the multi-objective part-family Multi-Unit Process Plan (MUPP) generation problem with unpredictable demands in different periods using a combination of the answers of the algorithms in step 1. The number of units is also optimised using the NSGA-II. Finally, a novel heuristic algorithm named Designed Periods Algorithm (DPA) is proposed in the second step to meet the unpredictable demands in different periods. To illustrate the applicability of the framework, an example is presented, the results of which have shown the superiority of the MUPP over the SUPP in response to unpredictable demands according to the periods designed by DPA.

NSGA-II vs NSGA-III for the Sustainable Multi-Objective Process Plan Generation in a Reconfigurable Manufacturing Environment

Abstract Since global economic competition, a new generation of manufacturing systems has been developed for changing market needs while minimizing harmful environmental impacts. Reconfigurable manufacturing system (RMS) can increase the system sustainability and reactivity to satisfy the market needs. In this paper, we consider an environmental oriented multi-objective problem for a sustainable reconfigurable manufacturing system (S-RMS). First, in the case of a single unit process plan generation, a non-linear multi-objective integer program (NL-MOIP) is presented, where four objectives are optimized respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted from energy consumption and the hazardous liquid wastes. Second, to solve the problem, adapted versions of the well-known non-dominated sorting genetic algorithm respectively NSGA-II and NSGA-III are presented. Finally, to illustrate the comparisons between both meta-heuristics, some experimental results are presented and analyzed.

Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop

Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop.

Towards a sustainable reconfigurable manufacturing system (SRMS): multi-objective based approaches for process plan generation problem

Nowadays to meet the continuously growing worldwide demand for capital and consumer goods while considering the associated economic, environmental, and social aspects, new generation of manufacturing systems are developed and adopted. The goal is to respond to changing market needs while minimising adverse effects on the environment. Reconfigurable manufacturing system (RMS) – due to its flexibility and characteristics – can increase the system sustainability and responsiveness to satisfy the market needs. This paper considers an environmental oriented multi-objective process plan generation problem for a sustainable reconfigurable manufacturing system (SRMS). Three criteria are minimised respectively the sustainability-metric value, the total production time and the total production cost. As a new criterion, the sustainability-metric value considers both liquid hazardous waste and greenhouse gas emissions (GHG). First, a multi-objective integer linear programming (MOILP) model is proposed. Second, due to the problem complexity, three approaches are adapted respectively a posteriori approach called augmented ε-constraint (AUGECON) and two evolutionary approaches namely, non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm II (SPEA-II). Finally, due to the lack of real world data and to illustrate the applicability of the three approaches, a simple numerical example is presented and the obtained numerical results are analysed.

Development and clinical implementation of semi-automated treatment planning including 3D printable applicator holders in complex skin brachytherapy.

High-dose-rate brachytherapy (HDR-BT) is a treatment option for malignant skin diseases compared to external beam radiation therapy, HDR-BT provides improved target coverage, better organ sparing, and has comparable treatment times. This is especially true for large clinical targets with complex topologies. To standardize and improve the quality and efficacy of the treatments, a novel streamlined treatment approach in complex skin HDR-BT was developed and implemented. This approach consists of auto generated treatment plans and a 3D printable applicator holder (3D-AH). The in-house developed planning system automatically segments computed tomography simulation images (a), optimizes a treatment plan (b), and generates a model of the 3D-AH (c). The 3D-AH is used as an immobilization device for the flexible Freiburg flap applicator used to deliver treatment. The developed, automated planning is compared against the standard clinical plan generation process for a flat 10×10cm2 field, curved fields with radii of 4, 6, and 8cm, and a representative clinical case. The quality of the 3D print is verified via an additional CT of the flap applicator latched into the holder, followed by an automated rigid registration with the original planning CT. Finally, the methodology is implemented and tested clinically under an IRB approval. All automatically generated plans were reviewed and accepted for clinical use. For the clinical workflow, the coverage achieved at a prescription depth for the flat 4, 6, and 8cm applicator was (100.0±4.9)%, (100.0±4.9)%, (96.0±0.3)%, and (98.4±0.3)%, respectively. For auto planning, the coverage was (99.9±0.3)%, (100.0±0.2)%, (100.0±0.3)%, and (100.1±0.2)%. For the clinical test case, the D90 for the clinical workflow and auto planning was found to be 93.5% and 100.29% of the prescribed dose, respectively. Processing of the patient's CT to generate trajectories and positions as well as the 3D model of the applicator took <5min. This workflow automates time intensive catheter digitizing and treatment planning. Compared to printing full applicators, the use of 3D-AH reduces the complexity of the 3D prints, the amount of the material to be used, the time of 3D printing, and amount of quality assurance required. The proposed methodology improves the overall treatment plan quality in complex HDR-BT and impact patient treatment outcomes potentially.

A Web-based Generative Process Planning System for FDM-based Additive Manufacturing

Abstract Additive manufacturing (AM) is an emerging technology that opens up new possibilities for mass personalization and manufacturing of high added-value parts. However, in its current state, AM faces some challenges, such as part quality consistency or slow production speeds, which must be resolved in order to take full advantage of the capabilities provided by this technology. In this paper, a web-based generative process planning system is proposed to help expert and non-expert users to improve dimensional accuracy and surface finish of parts manufactured by AM at the process planning stage. Fused deposition modeling (FDM) process is specially analyzed due to its common use and accessibility. The system architecture is proposed and key enabling technologies are discussed. A case study is presented to illustrate how this system can help users understand the capabilities of FDM process and use it efficiently. The functionality of the developed system was tested successfully.

A hybrid process planning for energy-efficient machining: Application of predictive analytics

Computer-aided process planning for energy-efficient machining is essential as energy consumption becomes a major environmental metric in the metal cutting industry. This paper introduces a process planning approach that enables energy prediction in the process planning phase through incorporating Generative Process Planning (GPP) and Variant Process Planning (VPP), called Hybrid Process Planning. GPP is used to provide decision making algorithms in computers by generating energy prediction models specific for machining conditions. VPP is adopted to reuse existing process plans with inclusion of such prediction models so that process planners can anticipate the energy values to be consumed in machine tools. Particularly, the present approach builds upon predictive analytics to efficiently handle sensor-level data collected from real machining operations, and create energy prediction models by using a machine-learning technique.

Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics

Low costs, high reactivity and high quality products are necessary criteria for industries to achieve competitiveness in nowadays market. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfil these requirements. RMS is one of the latest manufacturing paradigms, where machines components, software or material handling units can be added, removed, modified or interchanged as needed and when imposed by the necessity to react and respond rapidly and cost-effectively to changing. This research work addresses the multi-objective single-product multi-unit process plan generation problem in a reconfigurable manufacturing environment where three hybrid heuristics are proposed and compared namely: repetitive single-unit process plan heuristic (RSUPP), iterated local search on single-unit process plans heuristic (LSSUPP) and archive-based iterated local search heuristic (ABILS). Single-unit process plans are generated using the adapted non-dominated sorting genetic algorithm (NSGA-II). Moreover, in addition to the minimisation of the classical total production cost and the total completion time, the minimisation of the maximum machines exploitation time is considered as a novel optimisation criterion, in order to have high quality products. To illustrate the applicability of the three approaches, examples are presented and the obtained numerical results are analysed.

Machine layout design problem under product family evolution in reconfigurable manufacturing environment: a two-phase-based AMOSA approach

Reconfigurable manufacturing systems (RMSs) are designed to manufacture a specific product, incorporating the scalability to other products in the same family. This ability is based primarily on the reconfiguration capabilities offered by reconfigurable machines tools (RMTs). This paper addresses one of the most important aspects related to the reactivity of RMSs. More specifically, it considers the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family (products that share similarities) in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. We study the machine layout problem by considering the product family evolution where two sub-problems are addressed. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. For this, our two-phase-based approach combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search–based heuristic to determine the best machine layout for all the selected machines of the product family. The developed approach is based on the initially generated process plans of products (in the product family) for the RMS design under performance metrics. Moreover, the proposed layout must, at its best, respect both the constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed. An illustrative numerical example is proposed to demonstrate the applicability of our approach.

Automatic designation of feature faces to recognize interacting and compound volumetric features for prismatic parts

The important aspect of computer-aided process planning (CAPP) is to recognize surfaces and features of parts to aid downstream manufacturing of prismatic parts. Ample work is done on recognition of surface and its non-complex shape features by various methods, but there is shortfall in auto-recognition of interacting and compound features. The non-recognition of interacting and compound features limits the user from knowing individual feature type and material removal volume (MRV) of feature leading to lack of feature information provision to subsequent generative process planning. Therefore, this paper presents (i) an enriched classification of regular form features and (ii) a novel algorithm to automatically recognize interacting and compound volumetric features of prismatic part and to auto-generate material removal volume for the recognized volumetric features. All the faces of a feature are designated based on their geometrical shape, and combination of these designations expresses the type of feature present in a part.

Automatic Support Removal for Additive Manufacturing Post Processing

An additive manufacturing (AM) process often produces a near-net shape that closely conforms to the intended design to be manufactured. It sometimes contains additional support structure (also called scaffolding), which has to be removed in post-processing. We describe an approach to automatically generate process plans for support removal using a multi-axis machining instrument. The goal is to fracture the contact regions between each support component and the part, and to do it in the most cost-effective order while avoiding collisions with evolving near-net shape, including the remaining support components. A recursive algorithm identifies a maximal collection of support components whose connection regions to the part are accessible as well as the orientations at which they can be removed at a given round. For every such region, the accessible orientations appear as a ‘fiber’ in the collision-free space of the evolving near-net shape and the tool assembly. To order the removal of accessible supports, the algorithm constructs a search graph whose edges are weighted by the Riemannian distance between the fibers. The least expensive process plan is obtained by solving a traveling salesman problem (TSP) over the search graph. The sequence of configurations obtained by solving TSP is used as the input to a motion planner that finds collision free paths to visit all accessible features. The resulting part without the support structure can then be finished using traditional machining to produce the intended design. The effectiveness of the method is demonstrated through benchmark examples in 3D.

An energy-efficient process planning system using machine-monitoring data: A data analytics approach

This paper presents a system development of incorporating Computer-Aided Process Planning (CAPP) with energy-efficient machining based on a hybrid approach to take advantage of Generative Process Planning (GPP) and Variant Process Planning (VPP) and compensate for the drawbacks of both GPP and VPP. The GPP decides process plans without human assistance through decision-making algorithms in computers but lacks in ensuring the models’ robustness for different machining conditions. The VPP adopts group technology by reusing existing plans through the identification and classification of part family but does not support predictive and optimum decision-making. The developed Energy-Efficient Process Planning System (EEPPS) builds upon data analytics to efficiently process the machine-monitoring data collected from real machine tool’s operations and to develop energy prediction and optimization models based on historical machine-monitoring data. Particularly, those energy prediction and optimization models allow process planners to anticipate the energy consumed during executing a numerical control program and optimize process parameters at the level of machining features for minimizing energy use. This paper also presents a prototype implementation to show the feasibility of the proposed EEPPS.

A New Hybrid Approach for Machine Layout Design Under Family Product Evolution for Reconfigurable Manufacturing Systems

Reconfigurable manufacturing system (RMS) is a new paradigm that answers many of the challenges that the market nowadays imposes. In this paper, we address one of the most important aspects related to the reactivity of RMSs. We consider the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. More specifically, we study the machine layout problem by considering the family product evolution where two sub-problems are considered. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. We develop an approach which combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search-based heuristic to determine the best machine layout for all the selected machines of our product family. The approach is based on the initially generated process plans of products (in the product family) for the RMS design under performance metrics. The proposed layout must at its best, respects both the constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed. To show the applicability of our approach, we present a simple numerical example and discuss the obtained numerical results.