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The purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).

A new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.

By examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.

The proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.

It is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

As an Internet fashion brand, HSTYLE has developed into an Internet enterprise with annual sales of 1.5 billion RMB within 10 years, establishing its position as the top industry performer in China. This case studies HSTYLES' innovation in business model and organizational management. HSTYLE's workgroups have achieved the balance of responsibilities and rights in a small team of three members at minimum, while mobilizing the enthusiasm and initiative of the line managers with the support of public service sector. At the same time, HSTYLE enriches its brand style, establishes a fashion cloud platform, and integrates individual and organizational consumers into its existing fashion design, manufacturing and sales system.

Hybrid manufacturing technologies combining individual processes can be recognized as one of the most cogent developments in recent times. As a result of integrating additive, subtractive and inspection processes within a single system, the relative benefits of each process can be exploited. This collaboration uses the strength of the individual processes, while decreasing the shortcomings and broadening the application areas. Notwithstanding its numerous advantages, the implementation of hybrid technology is typically affected by the limited process planning methods. The process planning methods proficient at effectively using manufacturing sources for hybridization are notably restrictive. Hence, this paper aims to propose a computer-aided process planning system for hybrid additive, subtractive and inspection processes. A dynamic process plan has been developed, wherein an online process control with intelligent and autonomous characteristics, as well as the feedback from the inspection, is utilized.

In this research, a computer-aided process planning system for hybrid additive, subtractive and inspection process has been proposed. A framework based on the integration of three phases has been designed and implemented. The first phase has been developed for the generation of alternative plans or different scenarios depending on machining parameters, the amount of material to be added and removed in additive and subtractive manufacturing, etc. The primary objective in this phase has been to conduct set-up planning, process selection, process sequencing, selection of machine parameters, etc. The second phase is aimed at the identification of the optimum scenario or plan.

To accomplish this goal, economic models for additive and subtractive manufacturing were used. The objective of the third phase was to generate a dynamic process plan depending on the inspection feedback. For this purpose, a multi-agent system has been used. The multi-agent system has been used to achieve intelligence and autonomy of different phases.

A case study has been developed to test and validate the proposed algorithm and establish the performance of the proposed system.

The major contribution of this work is the novel dynamic computer-aided process planning system for the hybrid process. This hybrid process is not limited by the shortcomings of the constituent processes in terms of tool accessibility and support volume. It has been established that the hybrid process together with an appropriate computer-aided process plan provides an effective solution to accurately fabricate a variety of complex parts.

This paper aims to illustrate the tolerance optimization method based on the assembly accuracy constrain, precession constrain and the cost of production of the assembly product.

A tolerance optimization method is an excellent way to perform product assembly performance. The tolerance optimization method is adapted to the process analysis of the hatch and skin of an aircraft. In this paper, the tolerance optimization techniques are applied to the tolerance allocation for step difference analysis (example: step difference between aircraft cabin door and fuselage outer skin). First, a mathematical model is described to understand the relationship between manufacturing cost and tolerance cost. Second, the penalty function method is applied to form a new equation for tolerance optimization. Finally, MATLAB software is used to calculate 170 loops iteration to understand the efficiency of the new equation for tolerance optimization.

The tolerance optimization method is based on the assembly accuracy constrain, machinery constrain and the cost of production of the assembly product. The main finding of this paper is the lowest assembly and lowest production costs that met the product tolerance specification.

This paper illustrated an efficient method of tolerance allocation for products assembly. After 170 loops iterations, it founds that the results very close to the original required tolerance. But it can easily say that the different number of loops iterations may have a different result. But optimization result must be approximate to the original tolerance requirements.

It is evident from Table 4 that the tolerance of the closed loop is 1.3999 after the tolerance distribution is completed, which is less than and very close to the original tolerance of 1.40; the machining precision constraint of the outer skin of the cabin door and the fuselage is satisfied, and the assembly precision constraint of the closed loop is satisfied.

The research may support further research studies to minimize cost tolerance allocation using tolerance cost optimization techniques, which must meet the given constrain accuracy for assembly products.